diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..9b49ac7672204be71efd84778421b542ba7b8fdb --- /dev/null +++ b/.gitattributes @@ -0,0 +1,6 @@ +# Auto detect text files and perform LF normalization +* text=auto +datasets/esdac/** filter=lfs diff=lfs merge=lfs -text +datasets/fusion/** filter=lfs diff=lfs merge=lfs -text +src/fuse_esdac/large_inputs/** filter=lfs diff=lfs merge=lfs -text +resources/*.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..70cea88175a77a32f92c9794da07e185613a8409 --- /dev/null +++ b/.gitignore @@ -0,0 +1,165 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# Mac +.DS_Store +._* +.idea diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f2bcb621a4724eb5e2a29beb26c8be8045335eea --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Kuangdai + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..25d8c5b4f25c43b7f8c0eb3bbd84515e10983d7d --- /dev/null +++ b/README.md @@ -0,0 +1,147 @@ +# LUCAS-MEGA + +**LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems** + +[Manuscript](manuscript.pdf) + +--- + +# Introduction + +LUCAS-MEGA is a large-scale multimodal dataset for soil-environment systems, built by fusing heterogeneous European soil +and environmental datasets with the LUCAS soil survey as the backbone. + +The released dataset contains: + +- 72,552 soil samples +- 1,017 fused soil and environmental features +- 68 integrated ESDAC source datasets +- Numerical, categorical, textual, vector-valued, and visual data + +LUCAS-MEGA is designed for representation learning in soil science. It provides a unified sample-feature space where +models can learn relationships across soil, climate, terrain, land-use, hydrological, and environmental variables. + +The dataset follows the **MEGA** principles: + +- (**M**)ultimodal: scalar, vector-valued, categorical, textual, and visual features. +- (**E**)nd-to-end machine learning-ready: standardized units, harmonized formats, unified schema, and machine-readable + metadata. +- (**G**)reat quality: corrected unit issues, invalid values, codebook mismatches, missing-value conventions, and + cross-dataset inconsistencies. +- (**A**)ccessible: released with table and dictionary formats, metadata, assets, visualization tools, and API-oriented + resources. + +The final LUCAS-MEGA dataset is the **fused representation** and is intended for most users, including model training, +soil-environment analysis, and downstream applications. + +The **standardized representation** is an intermediate layer between raw ESDAC datasets and the final fused dataset. It +contains cleaned and normalized individual source datasets before fusion, and is mainly useful for inspection, +debugging, and extension. + +--- + +# Download + +## Download LUCAS-MEGA + +For users who only need the final fused dataset (16 GB): + +```bash +git clone https://huggingface.co/datasets/earthroverprogram/lucas-mega +cd lucas-mega +git lfs pull --include="datasets/fusion" --exclude="datasets/esdac,src/fuse_esdac/large_inputs" +``` + +The fusion dataset is the released LUCAS-MEGA dataset. It integrates standardized source datasets into a unified +sample-feature representation. Each sample is a soil observation, and each feature is a fused soil or environmental +variable. Features may be scalar values, categorical labels, text fields, vector-valued measurements, images, or links +to dense asset files. + +### Released Files + +| File | Description | +|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| +| `data_table.csv` | Main flat table. Each row is a soil sample; each column is a fused feature. | +| `data_dict.json` | Hierarchical sample-level JSON with detailed feature metadata. | +| `data_dict.pkl` | Same content as `data_dict.json`, stored as Python pickle for faster loading. | +| `meta_column_complete.json` | Complete metadata for fused features, including units, provenance, modality, and fusion information where available. | +| `meta_column_names.json` | List of fused feature names. | +| `meta_fused_datasets.csv` | Metadata and provenance of source datasets used in fusion. | +| `gadm_tree_europe.pkl` | GADM administrative hierarchy attached to samples for geographic reasoning and regional lookup. | +| `assets/` | Dense feature data, including hydraulic conductivity curves, water retention curves, particle size distribution spectra, and site images where available. | + +--- + +## Download the Standardized Representation + +For users interested in the intermediate standardized data (70 GB): + +```bash +git lfs pull --include="datasets/esdac" --exclude="datasets/fusion,src/fuse_esdac/large_inputs" +``` + +The standardized representation contains cleaned and normalized individual source datasets. These datasets have been +converted into common formats but are not yet fused into the final LUCAS-MEGA sample-feature table. + +It includes: + +- standardized tabular files for sample-structured datasets; +- standardized geospatial files for map-structured datasets; +- linked asset files for high-dimensional objects such as curves and spectra. + +This layer is useful for inspecting source datasets, understanding preprocessing, developing new fusion rules, debugging +the pipeline, or extending LUCAS-MEGA. + +Use `viewer.py` to visualize the standardized data and metadata: + +```bash +python viewer.py +``` + +preview + +--- + +# For Developers + +This repository also contains the codebase used to generate LUCAS-MEGA from raw ESDAC datasets. + +The pipeline includes: + +- **Source dataset downloading**: requesting and downloading source datasets from ESDAC. This is not handled by this + repository because the original datasets are subject to ESDAC access and license terms. +- **Data standardization**: converting heterogeneous datasets into a common representation, including format conversion, + unit normalization, coordinate handling, codebook harmonization, invalid-value correction, and metadata organization. +- **Data fusion**: aligning standardized datasets into the released LUCAS-MEGA sample-feature schema, with provenance + and metadata attached to fused features. + +Example: + +- Source download: + https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data + +- Standardization code: + [src/esdac/lucas-2009-topsoil-data/process.py](src/esdac/lucas-2009-topsoil-data/process.py) + +- Fusion specification: + [src/esdac/lucas-2009-topsoil-data/fuse_schema.json](src/esdac/lucas-2009-topsoil-data/fuse_schema.json) + +## Reproducing and Extending the Dataset + +Due to license agreements, we cannot redistribute the original ESDAC source datasets. + +To fully reproduce the released dataset, users must request and download the source datasets from ESDAC separately and +accept the corresponding license terms. + +The complete dataset list is available at: + +[src/esdac/status.json](src/esdac/status.json) + +To reproduce the released version, download datasets with `status = PROCESSED`. At release time, this includes 95 ESDAC +datasets. + +Full reproduction is time-consuming because source datasets must be requested individually, and access conditions may +vary by dataset. + +If you intend to reproduce the full pipeline or extend LUCAS-MEGA with additional datasets, please contact us. We can +provide practical guidance that is not included here due to source-data licensing restrictions. diff --git a/datasets/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/processed/EU_SoilHydroGrids_250m_2017Feb08/EU_SoilHydroGrids_250m_2017Feb08/tifs/FC_M_sl1.png b/datasets/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/processed/EU_SoilHydroGrids_250m_2017Feb08/EU_SoilHydroGrids_250m_2017Feb08/tifs/FC_M_sl1.png new file mode 100644 index 0000000000000000000000000000000000000000..8e1f3712e8000a1b08a5dcb05fee53b065692ee7 --- /dev/null +++ b/datasets/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/processed/EU_SoilHydroGrids_250m_2017Feb08/EU_SoilHydroGrids_250m_2017Feb08/tifs/FC_M_sl1.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1283a1de46d4090ddc22bb1c2d9fc4d60f2eef7bd9845992ded0bf63d3ad22ae +size 167137 diff --git 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c48ce61060459cca00ea32b475a4ce8ef44634bd1f5cb69eac723a5dfe70b1f2 +size 33181 diff --git a/datasets/status.xlsx b/datasets/status.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..7db61a0658de0fdd48ee81cc3be81e4320387005 Binary files /dev/null and b/datasets/status.xlsx differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4f4ab751258de3d702ab07fd19b634fa6ac7e9d1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,18 @@ +pandas~=2.3.3 +rasterio~=1.4.3 +numpy~=2.3.4 +pillow~=12.0.0 +openpyxl~=3.1.5 +matplotlib~=3.10.7 +geopandas~=1.1.1 +dbfread~=2.0.7 +packaging~=25.0 +gdal~=3.11.4 + +pyproj~=3.7.2 +tqdm~=4.67.1 +shapely~=2.1.2 +requests~=2.32.5 +beautifulsoup4~=4.14.2 +duckdb~=1.4.1 +openai~=2.6.1 \ No newline at end of file diff --git a/resources/preview.png b/resources/preview.png new file mode 100644 index 0000000000000000000000000000000000000000..6835077d6a58888e0cd66afabf054703f5578461 --- /dev/null +++ b/resources/preview.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9107fa7b923dc632085994d4cf2029fa05c4858afc894288033aa0171407b2e +size 2344173 diff --git a/resources/viewer.png b/resources/viewer.png new file mode 100644 index 0000000000000000000000000000000000000000..bf1b232e592510bc6426825e344b43eea5e88f4a Binary files /dev/null and b/resources/viewer.png differ diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/fuse_schema.json b/src/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5a4274519938dbd4fde48eb90baafcdd1e4b7590 --- /dev/null +++ b/src/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/fuse_schema.json @@ -0,0 +1,60 @@ +{ + "EU_SoilHydroGrids_250m_2017Feb08/EU_SoilHydroGrids_250m_2017Feb08/tifs@tif": { + "hydraulic": { + "field_capacity_water_content_profile__control_depths_0_5_15_30_60_100_200cm (cmΒ³ cm⁻³ %)": [ + "FC_M_sl1", + "FC_M_sl2", + "FC_M_sl3", + "FC_M_sl4", + "FC_M_sl5", + "FC_M_sl6", + "FC_M_sl7" + ], + "wilting_point_water_content_profile__control_depths_0_5_15_30_60_100_200cm (cmΒ³ cm⁻³ %)": [ + "WP_M_sl1", + "WP_M_sl2", + "WP_M_sl3", + "WP_M_sl4", + "WP_M_sl5", + "WP_M_sl6", + "WP_M_sl7" + ], + "saturated_water_content_profile__control_depths_0_5_15_30_60_100_200cm (cmΒ³ cm⁻³ %)": [ + "THS_M_sl1", + "THS_M_sl2", + "THS_M_sl3", + "THS_M_sl4", + "THS_M_sl5", + "THS_M_sl6", + "THS_M_sl7" + ], + "saturated_hydraulic_conductivity_profile__control_depths_0_5_15_30_60_100_200cm (cm/day)": [ + "KS_M_sl1", + "KS_M_sl2", + "KS_M_sl3", + "KS_M_sl4", + "KS_M_sl5", + "KS_M_sl6", + "KS_M_sl7" + ], + "available_water_capacity_profile__control_depths_0_5_15_30_60_100_200cm (cmΒ³ cm⁻³ %)": [ + "MRC_M_sl1", + "MRC_M_sl2", + "MRC_M_sl3", + "MRC_M_sl4", + "MRC_M_sl5", + "MRC_M_sl6", + "MRC_M_sl7" + ], + "hydraulic_conductivity_at_field_capacity_profile__control_depths_0_5_15_30_60_100_200cm (cm/day)": [ + "HCC_M_sl1", + "HCC_M_sl2", + "HCC_M_sl3", + "HCC_M_sl4", + "HCC_M_sl5", + "HCC_M_sl6", + "HCC_M_sl7" + ] + } + } +} diff --git a/src/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/process.py b/src/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/process.py new file mode 100644 index 0000000000000000000000000000000000000000..ccdfb076c6b250cc88656a19a9319645a40abbd6 --- /dev/null +++ b/src/esdac/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution/process.py @@ -0,0 +1,120 @@ +from pathlib import Path + +import numpy as np +import rasterio +from rasterio.transform import Affine + +from src.process.io import prepare_dirs, upload +from src.process.io import unzip +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: merge tif together === + +# Load a sample TIF to get tile size and meta +top = local_processed_dir / "EU_SoilHydroGrids_250m_2017Feb08/EU_SoilHydroGrids_250m_2017Feb08" +sample_tile_index = (14, 23) # EU_014_023 +sample = top / "EU_014_023/FC_M_sl1_EU_014_023.tif" + +with rasterio.open(sample) as src: + data = src.read(1) # preserve original dtype + nodata = src.nodata if src.nodata is not None else src.meta.get("nodata") + meta = src.meta + sample_transform = src.transform + +# Parameters +variables = ["FC", "HCC", "KS", "MRC", "THS", "WP"] +layers = list(range(1, 8)) +xs = list(range(14, 81)) +ys = list(range(0, 53)) + +tile_height, tile_width = data.shape +tile_pixels = tile_width # == tile_height +pixel_size = sample_transform.a # 250.0 +tile_size_m = pixel_size * tile_pixels # 100000.0 + +# Compute new transform based on known sample tile index +x_sample_origin = sample_transform.c +y_sample_origin = sample_transform.f +x_sample, y_sample = sample_tile_index + +x_origin = x_sample_origin + (min(xs) - x_sample) * tile_size_m +y_origin = y_sample_origin + (max(ys) - y_sample) * tile_size_m + +new_transform = Affine(pixel_size, 0.0, x_origin, + 0.0, -pixel_size, y_origin) + +# Prepare common new_meta +big_nodata = nodata if nodata is not None else 255 +new_meta_template = meta.copy() +new_meta_template.update({ + "width": len(xs) * tile_pixels, + "height": len(ys) * tile_pixels, + "transform": new_transform, + "count": 1, + "nodata": big_nodata +}) + +# Merge loop +move_list = [] +for variable in variables: + for layer in layers: + # Initialize full canvas with nodata + total_shape = (len(ys) * tile_height, len(xs) * tile_width) + big_tif = np.ones(total_shape, dtype=meta["dtype"]) * big_nodata + + for i, y in enumerate(ys): + for j, x in enumerate(xs): + found = False + for tile_dir in top.iterdir(): + if not tile_dir.is_dir(): + continue + tile_path = tile_dir / f"{variable}_M_sl{layer}_EU_{x:03d}_{y:03d}.tif" + if tile_path.exists(): + try: + with rasterio.open(tile_path) as src_tile: + tile_data = src_tile.read(1) + row_start = (len(ys) - 1 - i) * tile_height # flip vertically + col_start = j * tile_width + big_tif[row_start:row_start + tile_height, col_start:col_start + tile_width] = tile_data + found = True + break + except Exception as e: + print(f"⚠️ Failed to load {tile_path.name}: {e}") + if not found: + pass # nodata already filled + + # Save big TIF + save_dir = top / "tifs" + save_dir.mkdir(parents=True, exist_ok=True) + save_path = save_dir / f"{variable}_M_sl{layer}.tif" + with rasterio.open(save_path, "w", **new_meta_template) as dst: + dst.write(big_tif, 1) + + move_list = process_tif(save_path, move_list=move_list) + + # Remove the merged big tif to save disk + save_path.unlink() + # Remove the small patch tiles + for i, y in enumerate(ys): + for j, x in enumerate(xs): + for tile_dir in top.iterdir(): + if not tile_dir.is_dir(): + continue + tile_path = tile_dir / f"{variable}_M_sl{layer}_EU_{x:03d}_{y:03d}.tif" + if tile_path.exists(): + try: + tile_path.unlink() + except Exception as e: + print(f"⚠️ Failed to remove {tile_path}: {e}") + print(f"βœ… Merged TIF: {save_path.name}") + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/SOC-changes-2009-18/fuse_schema.json b/src/esdac/SOC-changes-2009-18/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..21d736c27b12c51be489dccd00ef5efe981c8d8b --- /dev/null +++ b/src/esdac/SOC-changes-2009-18/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "De_Rosa.et_.al2023.g_C_kg_y@tif": { + "carbon": { + "soc_change_2009_2018 (g C kg⁻¹ yr⁻¹)": "De_Rosa.et.al2023.g_C_kg_y" + } + } +} diff --git a/src/esdac/SOC-changes-2009-18/process.py b/src/esdac/SOC-changes-2009-18/process.py new file mode 100644 index 0000000000000000000000000000000000000000..a04ea6abbe038ec863ebc2869830691e44abffc9 --- /dev/null +++ b/src/esdac/SOC-changes-2009-18/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/Soil_erosion_by_wind/fuse_schema.json b/src/esdac/Soil_erosion_by_wind/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..ea892dbbb389153298cca1b29deca27721a9c376 --- /dev/null +++ b/src/esdac/Soil_erosion_by_wind/fuse_schema.json @@ -0,0 +1,18 @@ +{ + ".@tif": { + "erosion": { + "soil_loss_by_wind_gis_rweq (t ha⁻¹ yr⁻¹)": "GIS-RWEQ/Wind_Soil_Loss_Borrelli_GIS_WEQ", + "erodible_by_wind_fraction": "EF_Europe/EFS_Europe_JRC_Borrelli_et_al_2014", + "land_susceptibility_to_wind_erosion_class": [ + "ILSWE/ILSWE 1981-2010 v2/ILSWE_Classes", + { + "1": "Very low susceptibility", + "2": "Low susceptibility", + "3": "Moderate susceptibility", + "4": "High susceptibility", + "5": "Very high susceptibility" + } + ] + } + } +} diff --git a/src/esdac/Soil_erosion_by_wind/process.py b/src/esdac/Soil_erosion_by_wind/process.py new file mode 100644 index 0000000000000000000000000000000000000000..b3c64c4a368e8c818c8342f2b4ac83252b668803 --- /dev/null +++ b/src/esdac/Soil_erosion_by_wind/process.py @@ -0,0 +1,21 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/arsenic-european-topsoils/fuse_schema.json b/src/esdac/arsenic-european-topsoils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..392c876932a97fa74bac2539b7ced2fa57624e29 --- /dev/null +++ b/src/esdac/arsenic-european-topsoils/fuse_schema.json @@ -0,0 +1,10 @@ +{ + "As/distribution_parameters@tif": { + "trace_elements": { + "As_concentration_mean (log10 mg/kg)": "Mu", + "As_concentration_std (log10 mg/kg)": "Sigma", + "As_concentration_skewness": "Nu", + "As_concentration_kurtosis": "Tau" + } + } +} diff --git a/src/esdac/arsenic-european-topsoils/process.py b/src/esdac/arsenic-european-topsoils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..335a15fad5d2295ccaac005989dfb8eddc83fb56 --- /dev/null +++ b/src/esdac/arsenic-european-topsoils/process.py @@ -0,0 +1,21 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process specified TIFF files into standardized .tif and preview .png === +move_list = [] +for tif_name in ["Mu", "Nu", "Sigma", "Tau"]: + move_list = process_tif( + local_processed_dir / f"As/distribution_parameters/{tif_name}.tif", move_list + ) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/bacterial-fungal-biomass-fatty-acid-methyl-esters/fuse_schema.json b/src/esdac/bacterial-fungal-biomass-fatty-acid-methyl-esters/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..82ea3c23e7f1f0cdbd3c57104679cc50c3dd01d4 --- /dev/null +++ b/src/esdac/bacterial-fungal-biomass-fatty-acid-methyl-esters/fuse_schema.json @@ -0,0 +1,15 @@ +{ + "Bacteria-Fungal-Biomass.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【LUCASID】) + '_.*$'", + "SHARED": { + "distance_to_grid (m)": "0.0" + }, + "biomass": { + "bacterial_biomass (nmol FAME g⁻¹ soil)": "as_float(【nmol FAME g-1 soil】)", + "fungal_biomass (nmol FAME g⁻¹ soil)": "as_float(【nmol FAME g-1 soil.1】)", + "actinobacterial_biomass (nmol FAME g⁻¹ soil)": "as_float(【nmol FAME g-1 soil.2】)", + "gram_positive_biomass (nmol FAME g⁻¹ soil)": "as_float(【nmol FAME g-1 soil.3】)", + "gram_negative_biomass (nmol FAME g⁻¹ soil)": "as_float(【nmol FAME g-1 soil.4】)" + } + } +} diff --git a/src/esdac/bacterial-fungal-biomass-fatty-acid-methyl-esters/process.py b/src/esdac/bacterial-fungal-biomass-fatty-acid-methyl-esters/process.py new file mode 100644 index 0000000000000000000000000000000000000000..c04acb6e35e9a18b58cd21145631ad996606d8d6 --- /dev/null +++ b/src/esdac/bacterial-fungal-biomass-fatty-acid-methyl-esters/process.py @@ -0,0 +1,18 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, copy_to_local, upload +from src.process.processor import process_excel + +# === Step 1: Prepare directory paths for local and drive folders === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Copy specific file(s) from drive/download to local/processed === +copy_to_local(local_processed_dir, drive_download_dir, files=["Bacteria-Fungal-Biomass.xlsx"]) + +# === Step 3: Process the Excel file into CSV === +move_list = process_excel(local_processed_dir / "Bacteria-Fungal-Biomass.xlsx", skip_rows=2) + +# === Step 4: Upload processed files to drive/processed === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/biochar-meta-analysis-database/fuse_skip.txt b/src/esdac/biochar-meta-analysis-database/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..fadba4780551e6b9f055d5f29d1444d7525e1a78 --- /dev/null +++ b/src/esdac/biochar-meta-analysis-database/fuse_skip.txt @@ -0,0 +1 @@ +No location info, 177 samples \ No newline at end of file diff --git a/src/esdac/biochar-meta-analysis-database/process.py b/src/esdac/biochar-meta-analysis-database/process.py new file mode 100644 index 0000000000000000000000000000000000000000..e4b88662d6f03dab5b243ac5aa6e245de404251e --- /dev/null +++ b/src/esdac/biochar-meta-analysis-database/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_excel + +# === Step 1: Prepare directory paths for local and drive folders === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Convert Excel file to CSV (auto-detect header row) === +move_list = process_excel( + local_processed_dir / "Biochar_Meta-analysis_ Database/Biochar_Meta-analysis_ Database.xls" +) + +# === Step 4: Upload processed files to drive/processed and optionally clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/biodiversity-factor-soil-erosion/fuse_schema.json b/src/esdac/biodiversity-factor-soil-erosion/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..507afba361e2099a4b163ccac9b612e10599982e --- /dev/null +++ b/src/esdac/biodiversity-factor-soil-erosion/fuse_schema.json @@ -0,0 +1,11 @@ +{ + "Soil_Biodiversity_Erosion@tif": { + "CRS_STRING": "EPSG:3035", + "erosion": { + "et_factor_earthworm_richness_abundance": "Et_factor_abundance_Richness", + "et_factor_earthworm_abundance_only": "Et_Factor_Abundance", + "k_factor_with_earthworm_richness_abundance": "K_factor_Et_Abundance_Richness", + "k_factor_with_earthworm_abundance_only": "K_factor_Et_Abundance" + } + } +} diff --git a/src/esdac/biodiversity-factor-soil-erosion/process.py b/src/esdac/biodiversity-factor-soil-erosion/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/biodiversity-factor-soil-erosion/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/cadmium-topsoils-european-union/fuse_schema.json b/src/esdac/cadmium-topsoils-european-union/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..9d2a3df0e56a4f75fe387ab0e9f99f6ad14e3afe --- /dev/null +++ b/src/esdac/cadmium-topsoils-european-union/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "Cd@tif": { + "trace_elements": { + "Cd_concentration (mg/kg)": "Cd_alod_yj_backtransf_clipped" + } + } +} diff --git a/src/esdac/cadmium-topsoils-european-union/process.py b/src/esdac/cadmium-topsoils-european-union/process.py new file mode 100644 index 0000000000000000000000000000000000000000..f657a718af97b92f1ec9059d27f3e4cd45497fcf --- /dev/null +++ b/src/esdac/cadmium-topsoils-european-union/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Prepare directory paths for local and drive folders === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process the specified TIFF into standardized .tif and preview .png === +move_list = process_tif( + local_processed_dir / "Cd/Cd_alod_yj_backtransf_clipped.tif" +) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/caesium-137-and-plutonium-239240-european-topsoils/fuse_schema.json b/src/esdac/caesium-137-and-plutonium-239240-european-topsoils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..d09ddfe58210c577e1eccd3dc0013beed5e00edf --- /dev/null +++ b/src/esdac/caesium-137-and-plutonium-239240-european-topsoils/fuse_schema.json @@ -0,0 +1,10 @@ +{ + "data_ESDAC/data_ESDAC@tif": { + "radioactivity": { + "Cs_137_total_inventory (Bq/m2)": "Cstot_masked", + "Cs_137_chernobyl_inventory (Bq/m2)": "CsCher_masked", + "Cs_137_global_fallout_inventory (Bq/m2)": "CsGlob_masked", + "Pu_239_240_inventory (Bq/m2)": "Pu_masked" + } + } +} diff --git a/src/esdac/caesium-137-and-plutonium-239240-european-topsoils/process.py b/src/esdac/caesium-137-and-plutonium-239240-european-topsoils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..f32c1442d3e97c3b034ad7aceab92b4343287ebb --- /dev/null +++ b/src/esdac/caesium-137-and-plutonium-239240-european-topsoils/process.py @@ -0,0 +1,21 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Prepare directory paths for local and drive folders === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process the specified TIFF into standardized .tif and preview .png === +move_list = [] +for tif_name in ["CsCher", "CsGlob", "Cstot", "Pu"]: + move_list = process_tif( + local_processed_dir / f"data_ESDAC/data_ESDAC/{tif_name}_masked.tif", move_list + ) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/carbon-budget-eu-agricultural-soils/fuse_schema.json b/src/esdac/carbon-budget-eu-agricultural-soils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..4df18e94b7a99da1521f55728e800dd5b758b68b --- /dev/null +++ b/src/esdac/carbon-budget-eu-agricultural-soils/fuse_schema.json @@ -0,0 +1,16 @@ +{ + "Carbon_Erosion_data@tif": { + "carbon": { + "carbon_erosion_flux_accelerated_scenario (MgC ha⁻¹ yr⁻¹)": "AE_e/C_flux_ER", + "carbon_deposition_flux_accelerated_scenario (MgC ha⁻¹ yr⁻¹)": "AE_e/C_flux_DEP", + "carbon_erosion_flux_accelerated_revised_model (MgC ha⁻¹ yr⁻¹)": "AE_r/C_flux_ER", + "carbon_deposition_flux_accelerated_revised_model (MgC ha⁻¹ yr⁻¹)": "AE_r/C_flux_DEP", + "carbon_erosion_flux_current_scenario (MgC ha⁻¹ yr⁻¹)": "CE_e/C_flux_ER", + "carbon_deposition_flux_current_scenario (MgC ha⁻¹ yr⁻¹)": "CE_e/C_flux_DEP", + "carbon_erosion_flux_current_revised_model (MgC ha⁻¹ yr⁻¹)": "CE_r/C_flux_ER", + "carbon_deposition_flux_current_revised_model (MgC ha⁻¹ yr⁻¹)": "CE_r/C_flux_DEP", + "carbon_erosion_flux_no_erosion_scenario (MgC ha⁻¹ yr⁻¹)": "NE_/C_flux_ER", + "carbon_deposition_flux_no_erosion_scenario (MgC ha⁻¹ yr⁻¹)": "NE_/C_flux_DEP" + } + } +} diff --git a/src/esdac/carbon-budget-eu-agricultural-soils/process.py b/src/esdac/carbon-budget-eu-agricultural-soils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/carbon-budget-eu-agricultural-soils/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/chemical-properties-european-scale-based-lucas-topsoil-data/fuse_skip.txt b/src/esdac/chemical-properties-european-scale-based-lucas-topsoil-data/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..26c46530aa5fdd8edc8365b5e28e46858676ea7b --- /dev/null +++ b/src/esdac/chemical-properties-european-scale-based-lucas-topsoil-data/fuse_skip.txt @@ -0,0 +1 @@ +Used in LUCAS \ No newline at end of file diff --git a/src/esdac/chemical-properties-european-scale-based-lucas-topsoil-data/process.py b/src/esdac/chemical-properties-european-scale-based-lucas-topsoil-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..7763a99a9fe77b76c0efdab576f3404e31191435 --- /dev/null +++ b/src/esdac/chemical-properties-european-scale-based-lucas-topsoil-data/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2/fuse_schema.json b/src/esdac/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b2c48d9c9e455a43073ac3242e07640a5de6c392 --- /dev/null +++ b/src/esdac/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2/fuse_schema.json @@ -0,0 +1,48 @@ +{ + "XRD_FYM_06052024-for-ESDAC.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【Point_ID】) + '_.*$'", + "SHARED": { + "distance_to_grid (m)": "0.0" + }, + "mineral": { + "clay_mineral_smectite (%)": "as_float(【S】)", + "clay_mineral_vermiculite (%)": "as_float(【V】)", + "clay_mineral_illite (%)": "as_float(【I】)", + "clay_mineral_mica (%)": "as_float(【M】)", + "clay_mineral_biotite (%)": "as_float(【B】)", + "clay_mineral_paragonite (%)": "as_float(【Par】)", + "clay_mineral_chlorite (%)": "as_float(【C】)", + "clay_mineral_sepiolite (%)": "as_float(【Sep】)", + "clay_mineral_talc (%)": "as_float(【Tlc】)", + "clay_mineral_talc_plus_sepiolite (%)": "as_float(【Tlc + Sep】)", + "clay_mineral_plagioclase (%)": "as_float(【Plg】)", + "clay_mineral_k_feldspar (%)": "as_float(【K】)", + "clay_mineral_illite_smectite (%)": "as_float(【I/S】)", + "clay_mineral_illite_vermiculite (%)": "as_float(【I/V】)", + "clay_mineral_illite_chlorite (%)": "as_float(【I/C】)", + "clay_mineral_illite_chlorite_vermiculite (%)": "as_float(【I/C/V】)", + "clay_mineral_chlorite_smectite (%)": "as_float(【C/S】)", + "clay_mineral_chlorite_vermiculite (%)": "as_float(【C/V】)", + "clay_mineral_hydroxy_interlayered_vermiculite (%)": "as_float(【HIV】)", + "clay_mineral_hydrobiotite (%)": "as_float(【HydroB】)", + "clay_mineral_zeolite (%)": "as_float(【Zeo】)", + "clay_mineral_kaolinite_smectite (%)": "as_float(【K/S】)", + "clay_mineral_hematite (%)": "as_float(【Hem】)", + "clay_mineral_goethite (%)": "as_float(【Gth】)", + "clay_mineral_lepidocrocite (%)": "as_float(【Lp】)", + "clay_mineral_quartz (%)": "as_float(【Qz】)", + "clay_mineral_cristobalite (%)": "as_float(【Crs】)", + "clay_mineral_feldspar (%)": "as_float(【Fsp】)", + "clay_mineral_barite (%)": "as_float(【Brt】)", + "clay_mineral_amphibole (%)": "as_float(【Amp】)", + "clay_mineral_fluorite (%)": "as_float(【Fl】)", + "clay_mineral_calcite (%)": "as_float(【Cal】)", + "clay_mineral_dolomite (%)": "as_float(【Dol】)", + "clay_mineral_gypsum (%)": "as_float(【Gp】)", + "clay_mineral_calcium_oxalate (%)": "as_float(【Caox】)", + "clay_mineral_limonite (%)": "as_float(【Lim】)", + "clay_mineral_weddellite (%)": "as_float(【Wed】)", + "clay_mineral_description": "【Description】" + } + } +} diff --git a/src/esdac/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2/process.py b/src/esdac/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2/process.py new file mode 100644 index 0000000000000000000000000000000000000000..5f9190f042e781c683747b2082d3212ca2f5491f --- /dev/null +++ b/src/esdac/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2/process.py @@ -0,0 +1,44 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, copy_to_local, upload +from src.process.processor import process_excel + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract ZIP and copy Excel to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=[ + "XRD_diffractogrammes-LUCAS-2015-for-ESDAC.zip" +]) +copy_to_local(local_processed_dir, drive_download_dir, files=[ + "XRD_FYM_06052024-for-ESDAC.xlsx" +]) + +# === Step 3: Process Excel and convert .dat to .txt === +move_list = process_excel(local_processed_dir / "XRD_FYM_06052024-for-ESDAC.xlsx") + +dat_root = ( + local_processed_dir / + "XRD_diffractogrammes-LUCAS-2015-for-ESDAC" / + "XRD_diffractogrammes-LUCAS-2015-for-ESDAC" +) + +txt_files = [] + +for dat_path in dat_root.rglob("*.dat"): + try: + txt_path = dat_path.with_suffix(".txt") + with open(dat_path, "r", encoding="utf-8") as f_in: + lines = f_in.readlines()[1:] # skip first line + with open(txt_path, "w", encoding="utf-8") as f_out: + f_out.writelines(lines) + txt_files.append(txt_path) + print(f"βœ… Converted {dat_path.relative_to(local_processed_dir)} β†’ {txt_path.name}") + except Exception as e: + print(f"❌ Failed to convert {dat_path.name}: {e}") + +move_list += txt_files + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/climate-change-regional-indicators-change-and-extremes-europe/fuse_skip.txt b/src/esdac/climate-change-regional-indicators-change-and-extremes-europe/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/climate-change-regional-indicators-change-and-extremes-europe/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/climate-change-regional-indicators-change-and-extremes-europe/process.py b/src/esdac/climate-change-regional-indicators-change-and-extremes-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..08ef0fc58b5273fc97115c484ed6f57680b86605 --- /dev/null +++ b/src/esdac/climate-change-regional-indicators-change-and-extremes-europe/process.py @@ -0,0 +1,21 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload, unzip_nested +from src.process.processor import process_rdc_rst + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all RDC/RST files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("CLM_CC_Indicators/*.RDC")): + move_list = process_rdc_rst(tif_path, move_list) +for tif_path in sorted(local_processed_dir.rglob("DMI_CC_Indicators/*.RDC")): + move_list = process_rdc_rst(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/copper-distribution-topsoils/fuse_schema.json b/src/esdac/copper-distribution-topsoils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a955547758cee435b8a03d0fabe9441f7394a2b9 --- /dev/null +++ b/src/esdac/copper-distribution-topsoils/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "Copper@tif": { + "trace_elements": { + "Cu_concentration (mg/kg)": "copper_map_fill" + } + } +} diff --git a/src/esdac/copper-distribution-topsoils/process.py b/src/esdac/copper-distribution-topsoils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/copper-distribution-topsoils/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/cover-crops-accross-europe/fuse_schema.json b/src/esdac/cover-crops-accross-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..ab4c7dfcde56e53f17116c82150b74088fee2cc4 --- /dev/null +++ b/src/esdac/cover-crops-accross-europe/fuse_schema.json @@ -0,0 +1,10 @@ +{ + "CoverCrops@tif": { + "crop_plant": { + "cover_crop_fraction_median (β€±)": "CC_Median", + "cover_crop_fraction_std (β€±)": "CC_SD", + "cover_crop_fraction_5th_percentile (β€±)": "CC_5Q", + "cover_crop_fraction_95th_percentile (β€±)": "CC_95Q" + } + } +} diff --git a/src/esdac/cover-crops-accross-europe/process.py b/src/esdac/cover-crops-accross-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/cover-crops-accross-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/cover-management-factor-c-factor-eu/fuse_schema.json b/src/esdac/cover-management-factor-c-factor-eu/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..4b799e81743b66573068929aaf4d1a93970ccd48 --- /dev/null +++ b/src/esdac/cover-management-factor-c-factor-eu/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "CFactor@tif": { + "erosion": { + "c_factor_cover_management": "EU_CFactor_final_V7" + } + } +} diff --git a/src/esdac/cover-management-factor-c-factor-eu/process.py b/src/esdac/cover-management-factor-c-factor-eu/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/cover-management-factor-c-factor-eu/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/erosion-potential-method-epm/fuse_skip.txt b/src/esdac/erosion-potential-method-epm/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/erosion-potential-method-epm/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/erosion-potential-method-epm/process.py b/src/esdac/erosion-potential-method-epm/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/erosion-potential-method-epm/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union/fuse_schema.json b/src/esdac/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..50a4616781ef55d19f3ce077b5ee93847c5e4daa --- /dev/null +++ b/src/esdac/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "Sediment_MEAN@tif": { + "erosion": { + "net_erosion_rate_WaTEM_SEDEM (t ha⁻¹ yr⁻¹)": "WATEM_Code_Mean_t_ha_yr_100m" + } + } +} diff --git a/src/esdac/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union/process.py b/src/esdac/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union/process.py new file mode 100644 index 0000000000000000000000000000000000000000..3b094f996714bdfc931782b9df47660de8e3d6a7 --- /dev/null +++ b/src/esdac/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union/process.py @@ -0,0 +1,22 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIF and SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +for shp_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(shp_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-food-safety-authority-efsa-data-persam-software-tool/fuse_schema.json b/src/esdac/european-food-safety-authority-efsa-data-persam-software-tool/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f65194dc7224afaf6f8a173afbb866cafa3bfa69 --- /dev/null +++ b/src/esdac/european-food-safety-authority-efsa-data-persam-software-tool/fuse_schema.json @@ -0,0 +1,116 @@ +{ + "EFSA_V1_1_20121130@tif": { + "CRS_STRING": "EPSG:3035", + "organic_matter": { + "organic_matter_content (g/kg)": [ + "EFSA_OM_TOP", + 10 + ] + }, + "chemical": { + "pH_in_H2O_EFSA": "EFSA_PH_TOP" + }, + "hydraulic": { + "field_capacity_water_content_EFSA (cmΒ³/cmΒ³)": "EFSA_THETA_FC_TOP" + }, + "mass_density": { + "bulk_density_topsoil_EFSA (g/cmΒ³)": [ + "EFSA_BD_TOP", + 0.001 + ] + }, + "texture": { + "texture_class_topsoil_EFSA": [ + "EFSA_TEXTURE_TOP", + { + "1": "Coarse (clay < 18 % and sand > 65 %)", + "2": "Medium (18% < clay < 35% and sand > 15%, or clay < 18% and 15% < sand < 65%)", + "3": "Medium fine (clay < 35 % and sand < 15 %)", + "4": "Fine (35 % < clay < 60 %)", + "5": "Very fine (clay > 60 %)", + "7": "No texture (because of rock outcrop)", + "8": "No texture (because of organic layer)", + "9": "No texture (other cases)", + "0": "No information", + "-9000": "No information" + } + ] + }, + "climate": { + "annual_temperature (Β°C)": "EFSA_TMEAN_YEAR", + "monthly_temperature_JAN_to_DEC (Β°C)": [ + "EFSA_TMEAN_MONTH1", + "EFSA_TMEAN_MONTH2", + "EFSA_TMEAN_MONTH3", + "EFSA_TMEAN_MONTH4", + "EFSA_TMEAN_MONTH5", + "EFSA_TMEAN_MONTH6", + "EFSA_TMEAN_MONTH7", + "EFSA_TMEAN_MONTH8", + "EFSA_TMEAN_MONTH9", + "EFSA_TMEAN_MONTH10", + "EFSA_TMEAN_MONTH11", + "EFSA_TMEAN_MONTH12" + ], + "annual_precipitation (mm)": "EFSA_PREC_YEAR", + "monthly_precipitation_JAN_to_DEC (mm)": [ + "EFSA_PREC_MONTH1", + "EFSA_PREC_MONTH2", + "EFSA_PREC_MONTH3", + "EFSA_PREC_MONTH4", + "EFSA_PREC_MONTH5", + "EFSA_PREC_MONTH6", + "EFSA_PREC_MONTH7", + "EFSA_PREC_MONTH8", + "EFSA_PREC_MONTH9", + "EFSA_PREC_MONTH10", + "EFSA_PREC_MONTH11", + "EFSA_PREC_MONTH12" + ] + }, + "crop_plant": { + "maize_crop_fraction (%)": "EFSA_CAPRI_MAIZE", + "common_wheat_fraction (%)": "EFSA_CAPRI_COMMON_WHEAT", + "durum_wheat_fraction (%)": "EFSA_CAPRI_DURUM_WHEAT", + "barley_fraction (%)": "EFSA_CAPRI_BARLEY", + "sunflower_fraction (%)": "EFSA_CAPRI_SUNFLOWER", + "rapeseed_fraction (%)": "EFSA_CAPRI_RAPES", + "potatoes_fraction (%)": "EFSA_CAPRI_POTATOES", + "sugar_beet_fraction (%)": "EFSA_CAPRI_SUGARBEET", + "pulses_fraction (%)": "EFSA_CAPRI_PULSES", + "oats_fraction (%)": "EFSA_CAPRI_OATS", + "rye_fraction (%)": "EFSA_CAPRI_RYE", + "soya_fraction (%)": "EFSA_CAPRI_SOYA", + "fallow_fraction (%)": "EFSA_CAPRI_FALLOW", + "other_crops_fraction (%)": "EFSA_CAPRI_OTHER_ANNUALCROPS" + }, + "land_site": { + "land_use_class_EFSA": [ + "EFSA_GENERAL_LU", + { + "1": "Annual Crops", + "2": "Grass", + "3": "Permanent Crops", + "4": "Rice", + "5": "Non Agricultural", + "0": "Unclassified", + "8": "Unclassified", + "9": "Unclassified", + "-9000": "Unclassified" + } + ] + } + }, + "PermanentCrops_EFSA_data_2017_12_12_final@tif": { + "CRS_STRING": "EPSG:3035", + "crop_plant": { + "apples_fraction (%)": "APPLES_CLC222xFERA_PERCENT", + "berries_fraction (%)": "BERRIES_CLC222xFERA_PERCENT", + "citrus_fraction (%)": "CITRUS_CLC222xFERA_PERCENT", + "hops_fraction (%)": "HOPS_CLC222xFERA_PERCENT", + "olives_fraction (%)": "OLIVES_GROVES_CLC223_PERCENT", + "pastures_fraction (%)": "PASTURES_CLC231_PERCENT", + "vineyards_fraction (%)": "VINEYARDS_CLC221_PERCENT" + } + } +} diff --git a/src/esdac/european-food-safety-authority-efsa-data-persam-software-tool/process.py b/src/esdac/european-food-safety-authority-efsa-data-persam-software-tool/process.py new file mode 100644 index 0000000000000000000000000000000000000000..dffd145329aa14ff849c2b21a1bb712df254cdff --- /dev/null +++ b/src/esdac/european-food-safety-authority-efsa-data-persam-software-tool/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.asc")): + move_list = process_tif(tif_path, move_list, nodata=-9000.0) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/fuse_schema.json b/src/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..44b135ebdd752ec2fdf86618ac0497b193c2bcc1 --- /dev/null +++ b/src/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/fuse_schema.json @@ -0,0 +1,70 @@ +{ + "EUHYDI_public_v1.1.0_csv/EUHYDI_public_v1.1.0_csv/BASIC_with_coords.csv": { + "ENTRY_KEY": "'_'.join(['EUHYDI', as_int_id(【PROFILE_ID】), as_int_id(【SAMPLE_ID】)])", + "LAT_LONG": "(as_float(【Y_WGS84】), as_float(【X_WGS84】))", + "COUNTRY_CODE": "【ISO_COUNTRY】", + "SAMPLE_DATE": "f\"{str(【DAY】).zfill(2) if not str(【DAY】).startswith('-') else 'XX'}-{str(【MONTH】).zfill(2) if not str(【MONTH】).startswith('-') else 'XX'}-{str(【YEAR】) if not str(【YEAR】).startswith('-') else 'XXXX'}\"", + "SAMPLE_DEPTH_RANGE_CM": "[as_float(【SAMPLE_DEP_TOP】), as_float(【SAMPLE_DEP_BOT】)]", + "mass_density": { + "bulk_density (g/cmΒ³)": "as_float(【BD】)", + "porosity_volume_percentage (%)": "as_float(【POR】)" + }, + "texture": { + "coarse_percentage (%)": "as_float(【COARSE】)" + }, + "ASSETS": { + "hydraulic_conductivity_curve": "f\"cond/{'_'.join([as_int_id(【PROFILE_ID】), as_int_id(【SAMPLE_ID】)])}.npz\"", + "water_retention_curve": "f\"ret/{'_'.join([as_int_id(【PROFILE_ID】), as_int_id(【SAMPLE_ID】)])}.npz\"" + } + }, + "EUHYDI_public_v1.1.0_csv/EUHYDI_public_v1.1.0_csv/GENERAL.csv": { + "ENTRY_KEY": "'^EUHYDI_' + as_int_id(【PROFILE_ID】) + '_.*$'", + "topography_geology": { + "elevation (m)": "as_float(【ELEV】)" + }, + "land_site": { + "land_cover_primary": "resolve_code(【LC_L3】, codebook='lucas_lc', ignore_error_codes=['ND', 'NA'])", + "land_use_primary": "resolve_code(re.sub('^U', '', str(【LU_L2】).replace('X', '')), codebook='lucas_lu', ignore_error_codes=['ND', 'NA'])" + }, + "soil_type": { + "class_and_ref_EUHYDI": "[【NAT_CLAS】,【NAT_CLAS_REF】]" + } + }, + "EUHYDI_public_v1.1.0_csv/EUHYDI_public_v1.1.0_csv/CHEMICAL.csv": { + "ENTRY_KEY": "'^' + '_'.join(['EUHYDI', as_int_id(【PROFILE_ID】), as_int_id(【SAMPLE_ID】)]) + '$'", + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC】, factor=10)", + "total_carbon_content (g/kg)": "as_float(【TC】, factor=10)", + "harmonized_organic_carbon_content (g/kg)": "as_float(【HOC】, factor=10)", + "CaCO3_content (g/kg)": "as_float(【CACO3】, factor=10)" + }, + "organic_matter": { + "organic_matter_content_loss_on_ignition (g/kg)": "as_float(【LOI】, factor=10)" + }, + "chemical": { + "pH_in_H2O": "as_float(【PH_H2O】)", + "pH_in_KCl": "as_float(【PH_KCL】)", + "pH_in_CaCl2": "as_float(【PH_CACL2】)", + "electrical_conductivity (mS/m)": "as_float(【EC】, factor=10)", + "soluble_salt_content (%)": "as_float(【SALT】)", + "exchangeable_acidity (cmol(+)/kg)": "as_float(【EX_AC】)", + "potential_acidity (cmol(+)/kg)": "as_float(【POT_AC】)" + }, + "fertility": { + "Na_exchangeable (cmol(+)/kg)": "as_float(【EX_NA】)", + "Mg_exchangeable (cmol(+)/kg)": "as_float(【EX_MG】)", + "K_exchangeable (cmol(+)/kg)": "as_float(【EX_K】)", + "Ca_exchangeable (cmol(+)/kg)": "as_float(【EX_CA】)", + "cation_exchange_capacity (cmol(+)/kg)": "as_float(【CEC】)", + "base_cations_sum (cmol(+)/kg)": "as_float(【BASE_CATIONS】)" + } + }, + "EUHYDI_public_v1.1.0_csv/EUHYDI_public_v1.1.0_csv/PSD_EST.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【PROFILE_ID】) + '_' + as_int_id(【SAMPLE_ID】) + '$'", + "texture": { + "clay_percentage (%)": "as_float(【USCLAY】)", + "silt_percentage (%)": "as_float(【USSILT】)", + "sand_percentage (%)": "as_float(【USSAND】)" + } + } +} diff --git a/src/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/process.py b/src/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/process.py new file mode 100644 index 0000000000000000000000000000000000000000..b3b886ffd4d20740bde0a634e6b58c17bd3e7fd6 --- /dev/null +++ b/src/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/process.py @@ -0,0 +1,136 @@ +from pathlib import Path + +import numpy as np +import pandas as pd +import zipfile + +from src.process.io import prepare_dirs, unzip, upload + +# ============================================================ +# STEP 1: Set up directories +# ============================================================ +# Local processed directory, drive download, and drive processed paths +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# ============================================================ +# STEP 2: Unzip all archives into the local processed directory +# ============================================================ +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# ============================================================ +# STEP 3: Collect all CSV files to move/upload later +# ============================================================ +move_list = [] +for csv_path in sorted(local_processed_dir.rglob("*.csv")): + move_list.append(csv_path) + +base = Path(local_processed_dir) / "EUHYDI_public_v1.1.0_csv" / "EUHYDI_public_v1.1.0_csv" + +# ============================================================ +# STEP 4A: Merge coordinates into BASIC.csv β†’ BASIC_with_coords.csv +# ============================================================ +general = pd.read_csv( + base / "GENERAL.csv", + usecols=["PROFILE_ID", "X_WGS84", "Y_WGS84", "YEAR", "MONTH", "DAY", "ISO_COUNTRY"] +) +basic = pd.read_csv(base / "BASIC.csv") + +# Merge coordinate and date info into BASIC +basic_with_coords = basic.merge(general, on="PROFILE_ID", how="left") +basic_with_coords.to_csv(base / "BASIC_with_coords.csv", index=False) +move_list += [base / "BASIC_with_coords.csv"] + +print("βœ… Added X_WGS84, Y_WGS84 to BASIC β†’ BASIC_with_coords.csv") + +# ============================================================ +# STEP 4B: Expand PSIZE.csv into PSIZE_expanded.csv +# ============================================================ +df = pd.read_csv(base / "PSIZE.csv") + + +def expand_sizes(group): + """Extract up to three (P_SIZE, P_PERCENT) pairs per sample.""" + sizes = list(group["P_SIZE"].values) + percents = list(group["P_PERCENT"].values) + while len(sizes) < 3: # pad if fewer than 3 entries + sizes.append(None) + percents.append(None) + return pd.Series({ + "P_SIZE_SMALL": sizes[0], + "P_SIZE_MEDIUM": sizes[1], + "P_SIZE_LARGE": sizes[2], + "P_PERCENT_SMALL": percents[0], + "P_PERCENT_MEDIUM": percents[1], + "P_PERCENT_LARGE": percents[2] + }) + + +out = df.groupby(["PROFILE_ID", "SAMPLE_ID"]).apply( + expand_sizes, include_groups=False +).reset_index() + +out.to_csv(base / "PSIZE_expanded.csv", index=False) +move_list += [base / "PSIZE_expanded.csv"] + +# ============================================================ +# STEP 4C: Convert COND.csv to per-sample .npz arrays +# ============================================================ +df = pd.read_csv(base / "COND.csv") + +# Output directory for COND .npz assets +cond_out_dir = Path(local_processed_dir) / "assets" / "cond" +cond_out_dir.mkdir(exist_ok=True, parents=True) + +for (pid, sid), group in df.groupby(["PROFILE_ID", "SAMPLE_ID"]): + # Drop invalid rows, convert types, and sort by VALUE + group = group.dropna(subset=["VALUE", "COND"]) + group = group.astype({"VALUE": float, "COND": float}).sort_values("VALUE") + arr = group[["VALUE", "COND"]].to_numpy() + # Save each curve as compressed npz + fname = cond_out_dir / f"{pid}_{sid}.npz" + np.savez_compressed(fname, arr=arr) + +print("βœ… Generated per-sample COND npz files") + +# Zip all COND npz into assets/cond.zip +cond_zip_path = Path(local_processed_dir) / "assets" / "cond.zip" +with zipfile.ZipFile(cond_zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z: + for npz_file in cond_out_dir.glob("*.npz"): + z.write(npz_file, arcname=npz_file.name) +move_list.append(cond_zip_path) +print(f"βœ… Created COND archive: {cond_zip_path.name}") + +# ============================================================ +# STEP 4D: Convert RET.csv to per-sample .npz arrays +# ============================================================ +df = pd.read_csv(base / "RET.csv") + +# Output directory for RET .npz assets +ret_out_dir = Path(local_processed_dir) / "assets" / "ret" +ret_out_dir.mkdir(exist_ok=True, parents=True) + +for (pid, sid), group in df.groupby(["PROFILE_ID", "SAMPLE_ID"]): + # Drop invalid rows, convert types, and sort by HEAD + group = group.dropna(subset=["HEAD", "THETA"]) + group = group.astype({"HEAD": float, "THETA": float}).sort_values("HEAD") + arr = group[["HEAD", "THETA"]].to_numpy() + # Save each retention curve as compressed npz + fname = ret_out_dir / f"{pid}_{sid}.npz" + np.savez_compressed(fname, arr=arr) + +print("βœ… Generated per-sample RET npz files") + +# Zip all RET npz into assets/ret.zip +ret_zip_path = Path(local_processed_dir) / "assets" / "ret.zip" +with zipfile.ZipFile(ret_zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z: + for npz_file in ret_out_dir.glob("*.npz"): + z.write(npz_file, arcname=npz_file.name) +move_list.append(ret_zip_path) +print(f"βœ… Created RET archive: {ret_zip_path.name}") + +# ============================================================ +# STEP 5: Upload processed outputs to drive/processed +# ============================================================ +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-landslide-susceptibility-map-elsus-v2/fuse_skip.txt b/src/esdac/european-landslide-susceptibility-map-elsus-v2/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..82a54b3c9ee28a5b1d84c945dacb3a95108b4279 --- /dev/null +++ b/src/esdac/european-landslide-susceptibility-map-elsus-v2/fuse_skip.txt @@ -0,0 +1 @@ +Covered in other datasets \ No newline at end of file diff --git a/src/esdac/european-landslide-susceptibility-map-elsus-v2/process.py b/src/esdac/european-landslide-susceptibility-map-elsus-v2/process.py new file mode 100644 index 0000000000000000000000000000000000000000..cc384f0cefb5a2f6c280542cf4b26000d5de8f5d --- /dev/null +++ b/src/esdac/european-landslide-susceptibility-map-elsus-v2/process.py @@ -0,0 +1,23 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all ASC/TIF/SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.asc")): + move_list = process_tif(tif_path, move_list) +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-map-soil-suitability-provide-platform-most-human-activities-eu28/fuse_schema.json b/src/esdac/european-map-soil-suitability-provide-platform-most-human-activities-eu28/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..743d5e31187d616507ea8e770a0c178446521dda --- /dev/null +++ b/src/esdac/european-map-soil-suitability-provide-platform-most-human-activities-eu28/fuse_schema.json @@ -0,0 +1,15 @@ +{ + "Soil_as_Platform_for_human_activities@tif": { + "function_suitability": { + "soil_suitability_for_human_activities": [ + "soil_suitability_for_human_activities", + { + "1": "Unsuitable", + "2": "Moderately suitable", + "3": "Suitable", + "4": "Highly suitable" + } + ] + } + } +} diff --git a/src/esdac/european-map-soil-suitability-provide-platform-most-human-activities-eu28/process.py b/src/esdac/european-map-soil-suitability-provide-platform-most-human-activities-eu28/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/european-map-soil-suitability-provide-platform-most-human-activities-eu28/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-soil-database-derived-data/fuse_schema.json b/src/esdac/european-soil-database-derived-data/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0db3229bf4e3dd3e6bfa370cd10cdd9b853989ed --- /dev/null +++ b/src/esdac/european-soil-database-derived-data/fuse_schema.json @@ -0,0 +1,68 @@ +{ + "STU_EU_Layers@tif": { + "CRS_STRING": "EPSG:3035", + "mass_density": { + "bulk_density_topsoil_ESDB (g/cmΒ³)": "STU_EU_S_BD", + "bulk_density_subsoil_ESDB (g/cmΒ³)": "STU_EU_T_BD" + }, + "texture": { + "clay_percentage_topsoil_ESDB (%)":"STU_EU_S_CLAY", + "clay_percentage_subsoil_ESDB (%)": "STU_EU_T_CLAY", + "silt_percentage_topsoil_ESDB (%)": "STU_EU_S_SILT", + "silt_percentage_subsoil_ESDB (%)": "STU_EU_T_SILT", + "sand_percentage_topsoil_ESDB (%)": "STU_EU_S_SAND", + "sand_percentage_subsoil_ESDB (%)": "STU_EU_T_SAND", + "coarse_percentage_topsoil_ESDB (%)": "STU_EU_S_GRAVEL", + "coarse_percentage_subsoil_ESDB (%)": "STU_EU_T_GRAVEL", + "texture_class_topsoil_ESDB": [ + "STU_EU_S_TEXT_CLS", + { + "1": "Coarse (clay < 18 % and sand > 65 %)", + "2": "Medium (18% < clay < 35% and sand > 15%, or clay < 18% and 15% < sand < 65%)", + "3": "Medium fine (clay < 35 % and sand < 15 %)", + "4": "Fine (35 % < clay < 60 %)", + "5": "Very fine (clay > 60 %)", + "7": "No texture (because of rock outcrop)", + "8": "No texture (because of organic layer)", + "9": "No texture (other cases)", + "0": "No information", + "-9000": "No information" + } + ], + "texture_class_subsoil_ESDB": [ + "STU_EU_T_TEXT_CLS", + { + "1": "Coarse (clay < 18 % and sand > 65 %)", + "2": "Medium (18% < clay < 35% and sand > 15%, or clay < 18% and 15% < sand < 65%)", + "3": "Medium fine (clay < 35 % and sand < 15 %)", + "4": "Fine (35 % < clay < 60 %)", + "5": "Very fine (clay > 60 %)", + "7": "No texture (because of rock outcrop)", + "8": "No texture (because of organic layer)", + "9": "No texture (other cases)", + "0": "No information", + "-9000": "No information" + } + ] + }, + "carbon": { + "organic_carbon_content_topsoil_ESDB (g/kg)": [ + "STU_EU_S_OC", + 10 + ], + "organic_carbon_content_subsoil_ESDB (g/kg)": [ + "STU_EU_T_OC", + 10 + ] + }, + "crop_plant": { + "effective_rooting_depth (cm)": "STU_EU_DEPTH_ROOTS" + }, + "hydraulic": { + "total_available_water_capacity_topsoil_ESDB (mm)": "STU_EU_S_TAWC", + "total_available_water_capacity_subsoil_ESDB (mm)": "STU_EU_T_TAWC", + "total_available_water_capacity_topsoil_SMU_ESDB (mm)": "SMU_EU_S_TAWC", + "total_available_water_capacity_subsoil_SMU_ESDB (mm)": "SMU_EU_T_TAWC" + } + } +} diff --git a/src/esdac/european-soil-database-derived-data/process.py b/src/esdac/european-soil-database-derived-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..d82adf1dccb7f811cfe075273aada5130485c194 --- /dev/null +++ b/src/esdac/european-soil-database-derived-data/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_rdc_rst + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all RDC files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.RDC")): + move_list = process_rdc_rst(tif_path, move_list, nodata=0.0) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-soil-database-v2-raster-library-1kmx1km/fuse_schema.json b/src/esdac/european-soil-database-v2-raster-library-1kmx1km/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..cfd2080636c25ae9e6f3ef7e10a302ebac6cc6b5 --- /dev/null +++ b/src/esdac/european-soil-database-v2-raster-library-1kmx1km/fuse_schema.json @@ -0,0 +1,2547 @@ +{ + "ESDB-Raster-Library-1k-GeoTIFF-20240507/ESDB-Raster-Library-1k-GeoTIFF-20240507@tif": { + "function_suitability": { + "soil_structure_for_agricultural_use_subsoil": [ + "STR_SUB/STR_SUB", + { + "1": "Good", + "2": "Normal", + "3": "Peaty subsoil", + "4": "Poor", + "5": "No information" + } + ], + "soil_structure_for_agricultural_use_topsoil": [ + "STR_TOP/STR_TOP", + { + "1": "Good", + "2": "Humic or Peaty topsoil", + "3": "Normal", + "4": "No information" + } + ], + "dominant_limitation_to_agricultural_use": [ + "AGLIM1/AGLIM1", + { + "0": "No information", + "1": "No limitation to agricultural use", + "2": "Gravelly (over 35% gravel diameter < 7.5 cm)", + "3": "Stony (presence of stones diameter > 7.5 cm, impracticable mechanisation)", + "4": "Lithic (coherent and hard rock within 50 cm)", + "5": "Concretionary (over 35% concretions diameter < 7.5 cm near the surface)", + "6": "Petrocalcic (cemented or indurated calcic horizon within 100 cm)", + "7": "Saline (electric conductivity > 4 mS.cm-1 within 100 cm)", + "8": "Sodic (Na/T > 6% within 100 cm)", + "9": "Glaciers and snow-caps", + "10": "Soils disturbed by man (i.e. landfills, paved surfaces, mine spoils)", + "11": "Fragipans", + "12": "Excessively drained", + "13": "Almost always flooded", + "14": "Eroded phase, erosion", + "15": "Phreatic phase (shallow water table)", + "16": "Duripan (silica and iron cemented subsoil horizon)", + "17": "Petroferric horizon", + "18": "Permafrost" + } + ], + "secondary_limitation_to_agricultural_use": [ + "AGLIM2/AGLIM2", + { + "0": "No information", + "1": "No limitation to agricultural use", + "2": "Gravelly (over 35% gravel diameter < 7.5 cm)", + "3": "Stony (presence of stones diameter > 7.5 cm, impracticable mechanisation)", + "4": "Lithic (coherent and hard rock within 50 cm)", + "5": "Concretionary (over 35% concretions diameter < 7.5 cm near the surface)", + "6": "Petrocalcic (cemented or indurated calcic horizon within 100 cm)", + "7": "Saline (electric conductivity > 4 mS.cm-1 within 100 cm)", + "8": "Sodic (Na/T > 6% within 100 cm)", + "9": "Glaciers and snow-caps", + "10": "Soils disturbed by man (i.e. landfills, paved surfaces, mine spoils)", + "11": "Fragipans", + "12": "Excessively drained", + "13": "Almost always flooded", + "14": "Eroded phase, erosion", + "15": "Phreatic phase (shallow water table)", + "16": "Duripan (silica and iron cemented subsoil horizon)", + "17": "Petroferric horizon", + "18": "Permafrost" + } + ] + }, + "topography_geology": { + "elevation_class": [ + "ALT/ALT", + { + "1": "No information", + "2": "Lowlands & intermediate", + "3": "Uplands & mountains", + "4": "No information" + } + ], + "dominant_slope_class": [ + "SLOPEDO/SLOPEDO", + { + "0": "No information", + "1": "Level (dominant slope ranging from 0 to 8%)", + "2": "Sloping (dominant slope ranging from 8 to 15%)", + "3": "Moderately steep (dominant slope ranging from 15 to 25%)", + "4": "Steep (dominant slope over 25%)" + } + ], + "secondary_slope_class": [ + "SLOPESE/SLOPESE", + { + "0": "No information", + "1": "Level (dominant slope ranging from 0 to 8%)", + "2": "Sloping (dominant slope ranging from 8 to 15%)", + "3": "Moderately steep (dominant slope ranging from 15 to 25%)", + "4": "Steep (dominant slope over 25%)" + } + ], + "root_obstacle_depth_class": [ + "ROO/ROO", + { + "0": "No information", + "1": "No obstacle to roots between 0 and 80 cm", + "2": "Obstacle to roots between 60 and 80 cm depth", + "3": "Obstacle to roots between 40 and 60 cm depth", + "4": "Obstacle to roots between 20 and 40 cm depth", + "5": "Obstacle to roots between 0 and 80 cm depth", + "6": "Obstacle to roots between 0 and 20 cm depth" + } + ], + "rock_depth_class": [ + "DR/DR", + { + "1": "Deep (80–120 cm)", + "2": "Moderate (40–80 cm)", + "3": "Shallow (< 40 cm)", + "4": "Very deep (> 120 cm)", + "5": "No information" + } + ], + "dominant_parent_material": [ + "PARMADO/PARMADO", + { + "0": "No information", + "1000": "consolidated-clastic-sedimentary rocks", + "1100": "psephite or rudite", + "1110": "conglomerate", + "1111": "pudding stone", + "1120": "breccia", + "1200": "psammite or arenite", + "1210": "sandstone", + "1211": "calcareous sandstone", + "1212": "ferruginous sandstone", + "1213": "clayey sandstone", + "1214": "quartzitiic sandstone/orthoquartzite", + "1215": "micaceous sandstone", + "1220": "arkose", + "1230": "graywacke", + "1231": "feldspathic graywacke", + "1300": "pelite, lutite or argilite", + "1310": "claystone/mudstone", + "1311": "kaolinite", + "1312": "bentonite", + "1320": "siltstone", + "1400": "facies bound rock", + "1410": "flysch", + "1411": "sandy flisch", + "1412": "clayey and silty flysch", + "1413": "conglomeratic flysch", + "1420": "molasse", + "2000": "sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "2100": "calcareous rocks", + "2110": "limestone", + "2111": "hard limestone", + "2112": "soft limestone", + "2113": "marly limestone", + "2114": "chalky limestone", + "2115": "detrital limestone", + "2116": "carbonaceous limestone", + "2117": "lacustrine or freshwater limestone", + "2118": "travertine/calcareous sinter", + "2119": "cavernous limestone", + "2120": "dolomite", + "2121": "cavernous dolomite", + "2122": "calcareous dolomite", + "2130": "marlstone", + "2140": "marl", + "2141": "chalk marl", + "2142": "gypsiferous marl", + "2150": "chalk", + "2200": "evaporites", + "2210": "gypsum", + "2220": "anhydrite", + "2230": "halite", + "2300": "siliceous rocks", + "2310": "chert, hornstone, flint", + "2320": "diatomite/radiolarite", + "3000": "igneous rocks", + "3100": "acid to intermediate plutonic rocks", + "3110": "granite", + "3120": "granodiorite", + "3130": "diorite", + "3131": "quartz diorite", + "3132": "gabbro diorite", + "3140": "syenite", + "3200": "basic plutonic rocks", + "3210": "gabbro", + "3300": "ultrabasic plutonic rocks", + "3310": "peridotite", + "3320": "pyroxenite", + "3400": "acid to intermediate volcanic rocks", + "3410": "rhyolite", + "3411": "obsidian", + "3412": "quartz porphyrite", + "3420": "dacite", + "3430": "andesite", + "3431": "porphyrite (interm,)", + "3440": "phonolite", + "3441": "tephritic phonolite", + "3450": "trachyte", + "3500": "basic to ultrabasic volcanic rocks", + "3510": "basalt", + "3520": "diabase", + "3530": "pikrite", + "3600": "dike rocks", + "3610": "aplite", + "3620": "pegmatite", + "3630": "lamprophyre", + "3700": "pyroclastic rocks (tephra)", + "3710": "tuff/tuffstone", + "3711": "agglomeratic tuff", + "3712": "block tuff", + "3713": "lapilli tuff", + "3720": "tuffite", + "3721": "sandy tuffite", + "3722": "silty tuffite", + "3723": "clayey tuffite", + "3730": "volcanic scoria/volcanic breccia", + "3740": "volcanic ash", + "3750": "ignimbrite", + "3760": "pumice", + "4000": "metamorphic rocks", + "4100": "weakly metamorphic rocks", + "4110": "(meta-)shale/argilite", + "4120": "slate", + "4121": "graphitic slate", + "4200": "acid regional metamorphic rocks", + "4210": "(meta-)quartzite", + "4211": "quartzite schist", + "4220": "phyllite", + "4230": "micaschist", + "4240": "gneiss", + "4250": "granulite (sensu stricto)", + "4260": "migmatite", + "4300": "basic regional metamorphic rocks", + "4310": "greenschist", + "4311": "prasinite", + "4312": "chlorite", + "4313": "talc schist", + "4320": "amphibolite", + "4330": "eclogite", + "4400": "ultrabasic regional metamorphic rocks", + "4410": "serpentinite", + "4411": "greenstone", + "4500": "calcareous regional metamorphic rocks", + "4510": "marble", + "4520": "calcschist, skam", + "4600": "rocks formed by contact metamorphism", + "4610": "contact slate", + "4611": "nodular slate", + "4620": "hornfels", + "4630": "calsilicate rocks", + "4700": "tectogenetic metamorphism rocks or cataclasmic metamorphism", + "4710": "tectonic breccia", + "4720": "cataclasite", + "4730": "mylonite", + "5000": "unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "5100": "marine and estuarine sands", + "5110": "pre-quaternary sand", + "5111": "tertiary sand", + "5120": "quaternary sand", + "5121": "holocene coastal sand with shells", + "5122": "delta sand", + "5200": "marine and estuarine clays and silts", + "5210": "pre-quaternary clay and silt", + "5211": "tertiary clay", + "5212": "tertiary silt", + "5220": "quaternary clay and silt", + "5221": "holocene clay", + "5222": "holocene silt", + "5300": "fluvial sands and gravels", + "5310": "river terrace sand or gravel", + "5311": "river terrace sand", + "5312": "river terrace gravel", + "5320": "floodplain sand or gravel", + "5321": "floodplain sand", + "5322": "floodplain gravel", + "5400": "fluvial clays, silts and loams", + "5410": "river clay and silt", + "5411": "terrace clay and silt", + "5412": "floodplain clay and silt", + "5420": "river loam", + "5421": "terrace loam", + "5430": "overbank deposit", + "5431": "floodplain clay and silt", + "5432": "floodplain loam", + "5500": "lake deposits", + "5510": "lake sand and delta sand", + "5520": "lake marl, bog lime", + "5530": "lake silt", + "5600": "residual and redeposited loams from silicate rocks", + "5610": "residual loam", + "5611": "stony loam", + "5612": "clayey loam", + "5620": "redeposited loam", + "5621": "running-ground", + "5700": "residual and redeposited clays from calcareous rocks", + "5710": "residual clay", + "5711": "clay with flints", + "5712": "ferruginous residual clay", + "5713": "calcareous clay", + "5714": "non-calcareous clay", + "5715": "marly clay", + "5720": "redeposited clay", + "5721": "stony clay", + "5800": "slope deposits", + "5810": "slope-wash alluvium", + "5820": "colluvial deposit", + "5830": "talus scree", + "5831": "stratified slope deposits", + "6000": "unconsolidated glacial deposits/glacial drift", + "6100": "morainic deposits", + "6110": "glacial till", + "6111": "boulder clay", + "6120": "glacial debris", + "6200": "glaciofluvial deposits", + "6210": "outwash sand, glacial sand", + "6220": "outwash gravels glacial gravels", + "6300": "glaciolacustrine deposits", + "6310": "varves", + "7000": "eolian deposits", + "7100": "loess", + "7110": "loamy loess", + "7120": "sandy loess", + "7200": "eolian sands", + "7210": "dune sand", + "7220": "cover sand", + "8000": "organic materials", + "8100": "peat (mires)", + "8110": "rainwater fed moor peat (raised bog)", + "8111": "folic peat", + "8112": "fibric peat", + "8113": "terric peat", + "8120": "groundwater fed bog peat", + "8200": "slime and ooze deposits", + "8210": "gyttja, sapropel", + "8300": "carbonaceaous rocks (caustobiolite)", + "8310": "lignite (brown coal)", + "8320": "hard coal", + "8330": "anthracite", + "9000": "anthropogenic deposits", + "9100": "redeposited natural materials", + "9110": "sand and gravel fill", + "9120": "loamy fill", + "9200": "dump deposits", + "9210": "rubble/rubbish", + "9220": "industrial ashes and slag", + "9230": "industrial sludge", + "9240": "industrial waste", + "9300": "anthropogenic organic materials" + } + ], + "dominant_parent_material_level1": [ + "PARMADO1/PARMADO1", + { + "0": "No information", + "1": "Consolidated-clastic-sedimentary rocks", + "2": "Sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "3": "Igneous rocks", + "4": "Metamorphic rocks", + "5": "Unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "6": "Unconsolidated glacial deposits/glacial drift", + "7": "Eolian deposits", + "8": "Organic materials", + "9": "Anthropogenic deposits" + } + ], + "dominant_parent_material_level2": [ + "PARMADO2/PARMADO2", + { + "0": "No information", + "10": "Consolidated-clastic-sedimentary rocks", + "11": "Psephite or rudite", + "12": "Psammite or arenite", + "13": "Pelite, lutite or argilite", + "14": "Facies bound rock", + "20": "Sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "21": "Calcareous rocks", + "22": "Evaporites", + "23": "Siliceous rocks", + "30": "Igneous rocks", + "31": "Acid to intermediate plutonic rocks", + "32": "Basic plutonic rocks", + "33": "Ultrabasic plutonic rocks", + "34": "Acid to intermediate volcanic rocks", + "35": "Basic to ultrabasic volcanic rocks", + "36": "Dike rocks", + "37": "Pyroclastic rocks (tephra)", + "40": "Metamorphic rocks", + "41": "Weakly metamorphic rocks", + "42": "Acid regional metamorphic rocks", + "43": "Basic regional metamorphic rocks", + "44": "Ultrabasic regional metamorphic rocks", + "45": "Calcareous regional metamorphic rocks", + "46": "Rocks formed by contact metamorphism", + "47": "Tectogenetic metamorphism rocks or cataclasmic metamorphism", + "50": "Unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "51": "Marine and estuarine sands", + "52": "Marine and estuarine clays and silts", + "53": "Fluvial sands and gravels", + "54": "Fluvial clays, silts and loams", + "55": "Lake deposits", + "56": "Residual and redeposited loams from silicate rocks", + "57": "Residual and redeposited clays from calcareous rocks", + "58": "Slope deposits", + "60": "Unconsolidated glacial deposits/glacial drift", + "61": "Morainic deposits", + "62": "Glaciofluvial deposits", + "63": "Glaciolacustrine deposits", + "70": "Eolian deposits", + "71": "Loess", + "72": "Eolian sands", + "80": "Organic materials", + "81": "Peat (mires)", + "82": "Slime and ooze deposits", + "83": "Carbonaceaous rocks (caustobiolite)", + "90": "Anthropogenic deposits", + "91": "Redeposited natural materials", + "92": "Dump deposits", + "93": "Anthropogenic organic materials" + } + ], + "dominant_parent_material_level3": [ + "PARMADO3/PARMADO3", + { + "0": "No information", + "100": "Consolidated-clastic-sedimentary rocks", + "110": "Psephite or rudite", + "111": "Conglomerate", + "112": "Breccia", + "120": "Psammite or arenite", + "121": "Sandstone", + "122": "Arkose", + "123": "Graywacke", + "130": "Pelite, lutite or argilite", + "131": "Claystone/mudstone", + "132": "Siltstone", + "140": "Facies bound rock", + "141": "Flysch", + "142": "Molasse", + "200": "Sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "210": "Calcareous rocks", + "211": "Limestone", + "212": "Dolomite", + "213": "Marlstone", + "214": "Marl", + "215": "Chalk", + "220": "Evaporites", + "221": "Gypsum", + "222": "Anhydrite", + "223": "Halite", + "230": "Siliceous rocks", + "231": "Chert, hornstone, flint", + "232": "Diatomite/radiolarite", + "300": "Igneous rocks", + "310": "Acid to intermediate plutonic rocks", + "311": "Granite", + "312": "Granodiorite", + "313": "Diorite", + "314": "Syenite", + "320": "Basic plutonic rocks", + "321": "Gabbro", + "330": "Ultrabasic plutonic rocks", + "331": "Peridotite", + "332": "Pyroxenite", + "340": "Acid to intermediate volcanic rocks", + "341": "Rhyolite", + "342": "Dacite", + "343": "Andesite", + "344": "Phonolite", + "345": "Trachyte", + "350": "Basic to ultrabasic volcanic rocks", + "351": "Basalt", + "352": "Diabase", + "353": "Pikrite", + "360": "Dike rocks", + "361": "Aplite", + "362": "Pegmatite", + "363": "Lamprophyre", + "370": "Pyroclastic rocks (tephra)", + "371": "Tuff/tuffstone", + "372": "Tuffite", + "373": "Volcanic scoria/volcanic breccia", + "374": "Volcanic ash", + "375": "Ignimbrite", + "376": "Pumice", + "400": "Metamorphic rocks", + "410": "Weakly metamorphic rocks", + "411": "(Meta-)shale/argilite", + "412": "Slate", + "420": "Acid regional metamorphic rocks", + "421": "(Meta-)quartzite", + "422": "Phyllite", + "423": "Micaschist", + "424": "Gneiss", + "425": "Granulite (sensu stricto)", + "426": "Migmatite", + "430": "Basic regional metamorphic rocks", + "431": "Greenschist", + "432": "Amphibolite", + "433": "Eclogite", + "440": "Ultrabasic regional metamorphic rocks", + "441": "Serpentinite", + "450": "Calcareous regional metamorphic rocks", + "451": "Marble", + "452": "Calcschist, skam", + "460": "Rocks formed by contact metamorphism", + "461": "Contact slate", + "462": "Hornfels", + "463": "Calsilicate rocks", + "470": "Tectogenetic metamorphism rocks or cataclasmic metamorphism", + "471": "Tectonic breccia", + "472": "Cataclasite", + "473": "Mylonite", + "500": "Unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "510": "Marine and estuarine sands", + "511": "Pre-quaternary sand", + "512": "Quaternary sand", + "520": "Marine and estuarine clays and silts", + "521": "Pre-quaternary clay and silt", + "522": "Quaternary clay and silt", + "530": "Fluvial sands and gravels", + "531": "River terrace sand or gravel", + "532": "Floodplain sand or gravel", + "540": "Fluvial clays, silts and loams", + "541": "River clay and silt", + "542": "River loam", + "543": "Overbank deposit", + "550": "Lake deposits", + "551": "Lake sand and delta sand", + "552": "Lake marl, bog lime", + "553": "Lake silt", + "560": "Residual and redeposited loams from silicate rocks", + "561": "Residual loam", + "562": "Redeposited loam", + "570": "Residual and redeposited clays from calcareous rocks", + "571": "Residual clay", + "572": "Redeposited clay", + "580": "Slope deposits", + "581": "Slope-wash alluvium", + "582": "Colluvial deposit", + "583": "Talus scree", + "600": "Unconsolidated glacial deposits/glacial drift", + "610": "Morainic deposits", + "611": "Glacial till", + "612": "Glacial debris", + "620": "Glaciofluvial deposits", + "621": "Outwash sand, glacial sand", + "622": "Outwash gravels glacial gravels", + "630": "Glaciolacustrine deposits", + "631": "Varves", + "700": "Eolian deposits", + "710": "Loess", + "711": "Loamy loess", + "712": "Sandy loess", + "720": "Eolian sands", + "721": "Dune sand", + "722": "Cover sand", + "800": "Organic materials", + "810": "Peat (mires)", + "811": "Rainwater fed moor peat (raised bog)", + "812": "Groundwater fed bog peat", + "820": "Slime and ooze deposits", + "821": "Gyttja, sapropel", + "830": "Carbonaceaous rocks (caustobiolite)", + "831": "Lignite (brown coal)", + "832": "Hard coal", + "833": "Anthracite", + "900": "Anthropogenic deposits", + "910": "Redeposited natural materials", + "911": "Sand and gravel fill", + "912": "Loamy fill", + "920": "Dump deposits", + "921": "Rubble/rubbish", + "922": "Industrial ashes and slag", + "923": "Industrial sludge", + "924": "Industrial waste", + "930": "Anthropogenic organic materials" + } + ], + "secondary_parent_material": [ + "PARMASE/PARMASE", + { + "0": "No information", + "1000": "consolidated-clastic-sedimentary rocks", + "1100": "psephite or rudite", + "1110": "conglomerate", + "1111": "pudding stone", + "1120": "breccia", + "1200": "psammite or arenite", + "1210": "sandstone", + "1211": "calcareous sandstone", + "1212": "ferruginous sandstone", + "1213": "clayey sandstone", + "1214": "quartzitiic sandstone/orthoquartzite", + "1215": "micaceous sandstone", + "1220": "arkose", + "1230": "graywacke", + "1231": "feldspathic graywacke", + "1300": "pelite, lutite or argilite", + "1310": "claystone/mudstone", + "1311": "kaolinite", + "1312": "bentonite", + "1320": "siltstone", + "1400": "facies bound rock", + "1410": "flysch", + "1411": "sandy flisch", + "1412": "clayey and silty flysch", + "1413": "conglomeratic flysch", + "1420": "molasse", + "2000": "sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "2100": "calcareous rocks", + "2110": "limestone", + "2111": "hard limestone", + "2112": "soft limestone", + "2113": "marly limestone", + "2114": "chalky limestone", + "2115": "detrital limestone", + "2116": "carbonaceous limestone", + "2117": "lacustrine or freshwater limestone", + "2118": "travertine/calcareous sinter", + "2119": "cavernous limestone", + "2120": "dolomite", + "2121": "cavernous dolomite", + "2122": "calcareous dolomite", + "2130": "marlstone", + "2140": "marl", + "2141": "chalk marl", + "2142": "gypsiferous marl", + "2150": "chalk", + "2200": "evaporites", + "2210": "gypsum", + "2220": "anhydrite", + "2230": "halite", + "2300": "siliceous rocks", + "2310": "chert, hornstone, flint", + "2320": "diatomite/radiolarite", + "3000": "igneous rocks", + "3100": "acid to intermediate plutonic rocks", + "3110": "granite", + "3120": "granodiorite", + "3130": "diorite", + "3131": "quartz diorite", + "3132": "gabbro diorite", + "3140": "syenite", + "3200": "basic plutonic rocks", + "3210": "gabbro", + "3300": "ultrabasic plutonic rocks", + "3310": "peridotite", + "3320": "pyroxenite", + "3400": "acid to intermediate volcanic rocks", + "3410": "rhyolite", + "3411": "obsidian", + "3412": "quartz porphyrite", + "3420": "dacite", + "3430": "andesite", + "3431": "porphyrite (interm,)", + "3440": "phonolite", + "3441": "tephritic phonolite", + "3450": "trachyte", + "3500": "basic to ultrabasic volcanic rocks", + "3510": "basalt", + "3520": "diabase", + "3530": "pikrite", + "3600": "dike rocks", + "3610": "aplite", + "3620": "pegmatite", + "3630": "lamprophyre", + "3700": "pyroclastic rocks (tephra)", + "3710": "tuff/tuffstone", + "3711": "agglomeratic tuff", + "3712": "block tuff", + "3713": "lapilli tuff", + "3720": "tuffite", + "3721": "sandy tuffite", + "3722": "silty tuffite", + "3723": "clayey tuffite", + "3730": "volcanic scoria/volcanic breccia", + "3740": "volcanic ash", + "3750": "ignimbrite", + "3760": "pumice", + "4000": "metamorphic rocks", + "4100": "weakly metamorphic rocks", + "4110": "(meta-)shale/argilite", + "4120": "slate", + "4121": "graphitic slate", + "4200": "acid regional metamorphic rocks", + "4210": "(meta-)quartzite", + "4211": "quartzite schist", + "4220": "phyllite", + "4230": "micaschist", + "4240": "gneiss", + "4250": "granulite (sensu stricto)", + "4260": "migmatite", + "4300": "basic regional metamorphic rocks", + "4310": "greenschist", + "4311": "prasinite", + "4312": "chlorite", + "4313": "talc schist", + "4320": "amphibolite", + "4330": "eclogite", + "4400": "ultrabasic regional metamorphic rocks", + "4410": "serpentinite", + "4411": "greenstone", + "4500": "calcareous regional metamorphic rocks", + "4510": "marble", + "4520": "calcschist, skam", + "4600": "rocks formed by contact metamorphism", + "4610": "contact slate", + "4611": "nodular slate", + "4620": "hornfels", + "4630": "calsilicate rocks", + "4700": "tectogenetic metamorphism rocks or cataclasmic metamorphism", + "4710": "tectonic breccia", + "4720": "cataclasite", + "4730": "mylonite", + "5000": "unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "5100": "marine and estuarine sands", + "5110": "pre-quaternary sand", + "5111": "tertiary sand", + "5120": "quaternary sand", + "5121": "holocene coastal sand with shells", + "5122": "delta sand", + "5200": "marine and estuarine clays and silts", + "5210": "pre-quaternary clay and silt", + "5211": "tertiary clay", + "5212": "tertiary silt", + "5220": "quaternary clay and silt", + "5221": "holocene clay", + "5222": "holocene silt", + "5300": "fluvial sands and gravels", + "5310": "river terrace sand or gravel", + "5311": "river terrace sand", + "5312": "river terrace gravel", + "5320": "floodplain sand or gravel", + "5321": "floodplain sand", + "5322": "floodplain gravel", + "5400": "fluvial clays, silts and loams", + "5410": "river clay and silt", + "5411": "terrace clay and silt", + "5412": "floodplain clay and silt", + "5420": "river loam", + "5421": "terrace loam", + "5430": "overbank deposit", + "5431": "floodplain clay and silt", + "5432": "floodplain loam", + "5500": "lake deposits", + "5510": "lake sand and delta sand", + "5520": "lake marl, bog lime", + "5530": "lake silt", + "5600": "residual and redeposited loams from silicate rocks", + "5610": "residual loam", + "5611": "stony loam", + "5612": "clayey loam", + "5620": "redeposited loam", + "5621": "running-ground", + "5700": "residual and redeposited clays from calcareous rocks", + "5710": "residual clay", + "5711": "clay with flints", + "5712": "ferruginous residual clay", + "5713": "calcareous clay", + "5714": "non-calcareous clay", + "5715": "marly clay", + "5720": "redeposited clay", + "5721": "stony clay", + "5800": "slope deposits", + "5810": "slope-wash alluvium", + "5820": "colluvial deposit", + "5830": "talus scree", + "5831": "stratified slope deposits", + "6000": "unconsolidated glacial deposits/glacial drift", + "6100": "morainic deposits", + "6110": "glacial till", + "6111": "boulder clay", + "6120": "glacial debris", + "6200": "glaciofluvial deposits", + "6210": "outwash sand, glacial sand", + "6220": "outwash gravels glacial gravels", + "6300": "glaciolacustrine deposits", + "6310": "varves", + "7000": "eolian deposits", + "7100": "loess", + "7110": "loamy loess", + "7120": "sandy loess", + "7200": "eolian sands", + "7210": "dune sand", + "7220": "cover sand", + "8000": "organic materials", + "8100": "peat (mires)", + "8110": "rainwater fed moor peat (raised bog)", + "8111": "folic peat", + "8112": "fibric peat", + "8113": "terric peat", + "8120": "groundwater fed bog peat", + "8200": "slime and ooze deposits", + "8210": "gyttja, sapropel", + "8300": "carbonaceaous rocks (caustobiolite)", + "8310": "lignite (brown coal)", + "8320": "hard coal", + "8330": "anthracite", + "9000": "anthropogenic deposits", + "9100": "redeposited natural materials", + "9110": "sand and gravel fill", + "9120": "loamy fill", + "9200": "dump deposits", + "9210": "rubble/rubbish", + "9220": "industrial ashes and slag", + "9230": "industrial sludge", + "9240": "industrial waste", + "9300": "anthropogenic organic materials" + } + ], + "secondary_parent_material_level1": [ + "PARMASE1/PARMASE1", + { + "0": "No information", + "1": "Consolidated-clastic-sedimentary rocks", + "2": "Sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "3": "Igneous rocks", + "4": "Metamorphic rocks", + "5": "Unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "6": "Unconsolidated glacial deposits/glacial drift", + "7": "Eolian deposits", + "8": "Organic materials", + "9": "Anthropogenic deposits" + } + ], + "secondary_parent_material_level2": [ + "PARMASE2/PARMASE2", + { + "0": "No information", + "10": "Consolidated-clastic-sedimentary rocks", + "11": "Psephite or rudite", + "12": "Psammite or arenite", + "13": "Pelite, lutite or argilite", + "14": "Facies bound rock", + "20": "Sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "21": "Calcareous rocks", + "22": "Evaporites", + "23": "Siliceous rocks", + "30": "Igneous rocks", + "31": "Acid to intermediate plutonic rocks", + "32": "Basic plutonic rocks", + "33": "Ultrabasic plutonic rocks", + "34": "Acid to intermediate volcanic rocks", + "35": "Basic to ultrabasic volcanic rocks", + "36": "Dike rocks", + "37": "Pyroclastic rocks (tephra)", + "40": "Metamorphic rocks", + "41": "Weakly metamorphic rocks", + "42": "Acid regional metamorphic rocks", + "43": "Basic regional metamorphic rocks", + "44": "Ultrabasic regional metamorphic rocks", + "45": "Calcareous regional metamorphic rocks", + "46": "Rocks formed by contact metamorphism", + "47": "Tectogenetic metamorphism rocks or cataclasmic metamorphism", + "50": "Unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "51": "Marine and estuarine sands", + "52": "Marine and estuarine clays and silts", + "53": "Fluvial sands and gravels", + "54": "Fluvial clays, silts and loams", + "55": "Lake deposits", + "56": "Residual and redeposited loams from silicate rocks", + "57": "Residual and redeposited clays from calcareous rocks", + "58": "Slope deposits", + "60": "Unconsolidated glacial deposits/glacial drift", + "61": "Morainic deposits", + "62": "Glaciofluvial deposits", + "63": "Glaciolacustrine deposits", + "70": "Eolian deposits", + "71": "Loess", + "72": "Eolian sands", + "80": "Organic materials", + "81": "Peat (mires)", + "82": "Slime and ooze deposits", + "83": "Carbonaceaous rocks (caustobiolite)", + "90": "Anthropogenic deposits", + "91": "Redeposited natural materials", + "92": "Dump deposits", + "93": "Anthropogenic organic materials" + } + ], + "secondary_parent_material_level3": [ + "PARMASE3/PARMASE3", + { + "0": "No information", + "100": "Consolidated-clastic-sedimentary rocks", + "110": "Psephite or rudite", + "111": "Conglomerate", + "112": "Breccia", + "120": "Psammite or arenite", + "121": "Sandstone", + "122": "Arkose", + "123": "Graywacke", + "130": "Pelite, lutite or argilite", + "131": "Claystone/mudstone", + "132": "Siltstone", + "140": "Facies bound rock", + "141": "Flysch", + "142": "Molasse", + "200": "Sedimentary rocks (chemically precipitated, evaporated, or organogenic or biogenic in origin)", + "210": "Calcareous rocks", + "211": "Limestone", + "212": "Dolomite", + "213": "Marlstone", + "214": "Marl", + "215": "Chalk", + "220": "Evaporites", + "221": "Gypsum", + "222": "Anhydrite", + "223": "Halite", + "230": "Siliceous rocks", + "231": "Chert, hornstone, flint", + "232": "Diatomite/radiolarite", + "300": "Igneous rocks", + "310": "Acid to intermediate plutonic rocks", + "311": "Granite", + "312": "Granodiorite", + "313": "Diorite", + "314": "Syenite", + "320": "Basic plutonic rocks", + "321": "Gabbro", + "330": "Ultrabasic plutonic rocks", + "331": "Peridotite", + "332": "Pyroxenite", + "340": "Acid to intermediate volcanic rocks", + "341": "Rhyolite", + "342": "Dacite", + "343": "Andesite", + "344": "Phonolite", + "345": "Trachyte", + "350": "Basic to ultrabasic volcanic rocks", + "351": "Basalt", + "352": "Diabase", + "353": "Pikrite", + "360": "Dike rocks", + "361": "Aplite", + "362": "Pegmatite", + "363": "Lamprophyre", + "370": "Pyroclastic rocks (tephra)", + "371": "Tuff/tuffstone", + "372": "Tuffite", + "373": "Volcanic scoria/volcanic breccia", + "374": "Volcanic ash", + "375": "Ignimbrite", + "376": "Pumice", + "400": "Metamorphic rocks", + "410": "Weakly metamorphic rocks", + "411": "(Meta-)shale/argilite", + "412": "Slate", + "420": "Acid regional metamorphic rocks", + "421": "(Meta-)quartzite", + "422": "Phyllite", + "423": "Micaschist", + "424": "Gneiss", + "425": "Granulite (sensu stricto)", + "426": "Migmatite", + "430": "Basic regional metamorphic rocks", + "431": "Greenschist", + "432": "Amphibolite", + "433": "Eclogite", + "440": "Ultrabasic regional metamorphic rocks", + "441": "Serpentinite", + "450": "Calcareous regional metamorphic rocks", + "451": "Marble", + "452": "Calcschist, skam", + "460": "Rocks formed by contact metamorphism", + "461": "Contact slate", + "462": "Hornfels", + "463": "Calsilicate rocks", + "470": "Tectogenetic metamorphism rocks or cataclasmic metamorphism", + "471": "Tectonic breccia", + "472": "Cataclasite", + "473": "Mylonite", + "500": "Unconsolidated deposits (alluvium, weathering residuum and slope deposits)", + "510": "Marine and estuarine sands", + "511": "Pre-quaternary sand", + "512": "Quaternary sand", + "520": "Marine and estuarine clays and silts", + "521": "Pre-quaternary clay and silt", + "522": "Quaternary clay and silt", + "530": "Fluvial sands and gravels", + "531": "River terrace sand or gravel", + "532": "Floodplain sand or gravel", + "540": "Fluvial clays, silts and loams", + "541": "River clay and silt", + "542": "River loam", + "543": "Overbank deposit", + "550": "Lake deposits", + "551": "Lake sand and delta sand", + "552": "Lake marl, bog lime", + "553": "Lake silt", + "560": "Residual and redeposited loams from silicate rocks", + "561": "Residual loam", + "562": "Redeposited loam", + "570": "Residual and redeposited clays from calcareous rocks", + "571": "Residual clay", + "572": "Redeposited clay", + "580": "Slope deposits", + "581": "Slope-wash alluvium", + "582": "Colluvial deposit", + "583": "Talus scree", + "600": "Unconsolidated glacial deposits/glacial drift", + "610": "Morainic deposits", + "611": "Glacial till", + "612": "Glacial debris", + "620": "Glaciofluvial deposits", + "621": "Outwash sand, glacial sand", + "622": "Outwash gravels glacial gravels", + "630": "Glaciolacustrine deposits", + "631": "Varves", + "700": "Eolian deposits", + "710": "Loess", + "711": "Loamy loess", + "712": "Sandy loess", + "720": "Eolian sands", + "721": "Dune sand", + "722": "Cover sand", + "800": "Organic materials", + "810": "Peat (mires)", + "811": "Rainwater fed moor peat (raised bog)", + "812": "Groundwater fed bog peat", + "820": "Slime and ooze deposits", + "821": "Gyttja, sapropel", + "830": "Carbonaceaous rocks (caustobiolite)", + "831": "Lignite (brown coal)", + "832": "Hard coal", + "833": "Anthracite", + "900": "Anthropogenic deposits", + "910": "Redeposited natural materials", + "911": "Sand and gravel fill", + "912": "Loamy fill", + "920": "Dump deposits", + "921": "Rubble/rubbish", + "922": "Industrial ashes and slag", + "923": "Industrial sludge", + "924": "Industrial waste", + "930": "Anthropogenic organic materials" + } + ], + "peat_presence": [ + "PEAT/PEAT", + { + "1": "No", + "2": "Yes", + "3": "No information" + } + ] + }, + "land_site": { + "regrouped_land_use": [ + "USE_/USE_", + { + "1": "No information", + "2": "Cultivated", + "3": "Halophile Grassland", + "4": "Managed Grassland", + "5": "Semi-natural", + "6": "No information" + } + ], + "dominant_land_use": [ + "USEDO/USEDO", + { + "0": "No information", + "1": "Pasture, grassland, grazing land", + "2": "Poplars", + "3": "Arable land, cereals", + "4": "Wasteland, shrub", + "5": "Forest, coppice", + "6": "Horticulture", + "7": "Vineyards", + "8": "Garrigue", + "9": "Bush, macchia", + "10": "Moor", + "11": "Halophile grassland", + "12": "Arboriculture, orchard", + "13": "Industrial crops", + "14": "Rice", + "15": "Cotton", + "16": "Vegetables", + "17": "Olive trees", + "18": "Recreation", + "19": "Extensive pasture, grazing, rough pasture", + "20": "Dehesa (extensive pastoral system in forest parks in Spain)", + "21": "Cultivos enarenados (artificial soils for orchards in SE Spain)", + "22": "Wildlife refuge, land above timberline", + "30": "No information" + } + ], + "secondary_land_use": [ + "USESE/USESE", + { + "0": "No information", + "1": "Pasture, grassland, grazing land", + "2": "Poplars", + "3": "Arable land, cereals", + "4": "Wasteland, shrub", + "5": "Forest, coppice", + "6": "Horticulture", + "7": "Vineyards", + "8": "Garrigue", + "9": "Bush, macchia", + "10": "Moor", + "11": "Halophile grassland", + "12": "Arboriculture, orchard", + "13": "Industrial crops", + "14": "Rice", + "15": "Cotton", + "16": "Vegetables", + "17": "Olive trees", + "18": "Recreation", + "19": "Extensive pasture, grazing, rough pasture", + "20": "Dehesa (extensive pastoral system in forest parks in Spain)", + "21": "Cultivos enarenados (artificial soils for orchards in SE Spain)", + "22": "Wildlife refuge, land above timberline" + } + ] + }, + "mass_density": { + "stone_volume_class": [ + "VS/VS", + { + "0": "0% stones", + "1": "0% stones", + "2": "10% stones", + "3": "15% stones", + "4": "20% stones", + "5": "No information", + "10": "10% stones", + "15": "15% stones", + "20": "20% stones" + } + ], + "packing_density_class_subsoil": [ + "PD_SUB/PD_SUB", + { + "1": "High", + "2": "Low", + "3": "Medium", + "4": "No information" + } + ], + "packing_density_class_topsoil": [ + "PD_TOP/PD_TOP", + { + "1": "Low", + "2": "Medium", + "3": "No information" + } + ] + }, + "management": { + "dominant_water_management": [ + "WM1/WM1", + { + "0": "No information", + "1": "Not applicable (no agriculture)", + "2": "No water management system", + "3": "A water management system exists to alleviate waterlogging (drainage)", + "4": "A water management system exists to alleviate drought stress (irrigation)", + "5": "A water management system exists to alleviate salinity (drainage)", + "6": "A water management system exists to alleviate both waterlogging and drought stress", + "7": "A water management system exists to alleviate both waterlogging and salinity" + } + ], + "secondary_water_management": [ + "WM2/WM2", + { + "0": "No information", + "1": "Not applicable (no agriculture)", + "2": "No water management system", + "3": "Pumping", + "4": "Ditches", + "5": "Pipe under drainage (network of drain pipes)", + "6": "Mole drainage", + "7": "Deep loosening (subsoiling)", + "8": "'Bed' system (ridge-furrow or steching)", + "9": "Flood irrigation (system of irrigation by controlled flooding as for rice)", + "10": "Overhead sprinkler (system of irrigation by sprinkling)", + "11": "Trickle irrigation" + } + ] + }, + "climate": { + "regrouped_accumulated_mean_annual_temperature_class": [ + "ATC/ATC", + { + "1": "#", + "2": "High (> 3000 Β°C days)", + "3": "Low (< 1800 Β°C days)", + "4": "Medium (1800–3000 Β°C days)", + "5": "No information" + } + ] + }, + "hydraulic": { + "water_regime": [ + "WR/WR", + { + "0": "No information", + "1": "Not wet within 80 cm for over 3 months, nor wet within 40 cm for over 1 month", + "2": "Wet within 80 cm for 3 to 6 months, but not wet within 40 cm for over 1 month", + "3": "Wet within 80 cm for over 6 months, but not wet within 40 cm for over 11 months", + "4": "Wet within 40 cm depth for over 11 months" + } + ], + "available_water_capacity_class_subsoil": [ + "AWC_SUB/AWC_SUB", + { + "1": "No data or not applicable", + "2": "High (140–190 mm/m)", + "3": "Low (< 100 mm/m)", + "4": "Medium (100–140 mm/m)", + "5": "Very high (> 190 mm/m)", + "6": "Very low (~ 0 mm/m)", + "7": "No information" + } + ], + "available_water_capacity_class_topsoil": [ + "AWC_TOP/AWC_TOP", + { + "1": "No data or not applicable", + "2": "High (140–190 mm/m)", + "3": "Medium (100–140 mm/m)", + "4": "Very high (> 190 mm/m)", + "5": "No information" + } + ], + "base_saturation_class_subsoil": [ + "BS_SUB/BS_SUB", + { + "1": "High (> 50%)", + "2": "Low (< 50%)", + "3": "No information" + } + ], + "base_saturation_class_topsoil": [ + "BS_TOP/BS_TOP", + { + "1": "High (> 75%)", + "2": "Low (< 50%)", + "3": "Medium (50–75%)", + "4": "No information" + } + ], + "soil_crusting_class": [ + "CRUSTING/CRUSTING", + { + "0": "No information", + "1": "Very weak", + "2": "Weak", + "3": "Moderate", + "4": "Strong", + "5": "Very strong" + } + ], + "depth_to_a_gleyed_horizon_class": [ + "DGH/DGH", + { + "1": "Moderate (40–80 cm)", + "2": "Shallow (< 40 cm)", + "3": "Very deep (> 120 cm)", + "4": "No information" + } + ], + "depth_to_an_impermeable_layer_class": [ + "DIMP/DIMP", + { + "1": "Deep (> 80 cm)", + "2": "Shallow (< 80 cm)", + "3": "No information" + } + ], + "easily_available_water_capacity_class_subsoil": [ + "EAWC_SUB/EAWC_SUB", + { + "1": "No data or not applicable", + "2": "High (140–190 mm/m)", + "3": "Low (< 100 mm/m)", + "4": "Medium (100–140 mm/m)", + "5": "Very high (> 190 mm/m)", + "6": "Very low (~ 0 mm/m)", + "7": "No information" + } + ], + "easily_available_water_capacity_class_topsoil": [ + "EAWC_TOP/EAWC_TOP", + { + "1": "No data or not applicable", + "2": "High (140–190 mm/m)", + "3": "Medium (100–140 mm/m)", + "4": "Very high (> 190 mm/m)", + "5": "No information" + } + ], + "hydrogeological_class": [ + "HG/HG", + { + "1": "1C", + "2": "1H", + "3": "1L", + "4": "1M", + "5": "1S", + "6": "2", + "7": "3", + "8": "4D", + "9": "4W", + "10": "No information" + } + ], + "impermeable_layer_depth_class": [ + "IL/IL", + { + "0": "No information", + "1": "No impermeable layer within 150 cm", + "2": "Impermeable layer between 80 and 150 cm", + "3": "Impermeable layer between 40 and 80 cm", + "4": "Impermeable layer within 40 cm" + } + ], + "parent_material_hydrogeological_class": [ + "PMH/PMH", + { + "1": "No information", + "2": "Porous 2 Storage ~ Permeability +", + "3": "Hard. Storage -- Permeability --", + "4": "Storage - Permeability -", + "5": "Soft. Storage -- Permeability --", + "6": "Porous - Storage ~ Permeability +", + "7": "Porous 1 Storage + Permeability +", + "8": "No information" + } + ] + }, + "fertility": { + "cation_exchange_capacity_class_subsoil": [ + "CEC_SUB/CEC_SUB", + { + "1": "High (> 40 cmol(+)/kg)", + "2": "Low (< 15 cmol(+)/kg)", + "3": "Medium (15–40 cmol(+)/kg)", + "4": "No information" + } + ], + "cation_exchange_capacity_class_topsoil": [ + "CEC_TOP/CEC_TOP", + { + "1": "High (> 40 cmol(+)/kg)", + "2": "Low (< 15 cmol(+)/kg)", + "3": "Medium (15–40 cmol(+)/kg)", + "4": "No information" + } + ] + }, + "erosion": { + "soil_profile_differentiation": [ + "DIFF/DIFF", + { + "0": "No information", + "1": "High differentiation", + "2": "Low differentiation", + "3": "No differentiation", + "4": "No information" + } + ], + "soil_erodibility_class": [ + "ERODI/ERODI", + { + "0": "No information", + "1": "Very weak", + "2": "Weak", + "3": "Moderate", + "4": "Strong", + "5": "Very strong" + } + ], + "physicochemical_factor_of_soil_crusting_and_erodibility_class": [ + "PHYSCHIM/PHYSCHIM", + { + "0": "No information", + "1": "Very favourable", + "2": "Favourable", + "3": "Medium", + "4": "Unfavourable", + "5": "Very unfavourable" + } + ], + "textural_factor_of_soil_crusting_class": [ + "TXCRUST/TXCRUST", + { + "0": "No information", + "1": "Very favourable", + "2": "Favourable", + "3": "Medium", + "4": "Unfavourable", + "5": "Very unfavourable" + } + ], + "textural_factor_of_soil_erodibility_class": [ + "TXEROD/TXEROD", + { + "0": "No information", + "1": "Very favourable", + "2": "Favourable", + "3": "Medium", + "4": "Unfavourable", + "5": "Very unfavourable" + } + ] + }, + "soil_type": { + "fao85_full_classification": [ + "FAO85FU/FAO85FU", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Acrisol", + "8": "Gleyic Acrisol", + "9": "Humic Acrisol", + "10": "Orthic Acrisol", + "11": "Calcaric Cambisol", + "12": "Chromic Cambisol", + "13": "Calcaro-Chromic Cambisol", + "14": "Dystric Cambisol", + "15": "Ando-Dystric Cambisol", + "16": "Spodo-Dystric Cambisol", + "17": "Eutric Cambisol", + "18": "Ando-Eutric Cambisol", + "19": "Calcaro-Eutric Cambisol", + "20": "Fluvi-Eutric Cambisol", + "21": "Gleyo-Eutric Cambisol", + "22": "Verti-Eutric Cambisol", + "23": "Gleyic Cambisol", + "24": "Calcaro-Gleyic Cambisol", + "25": "Stagno-Gleyic Cambisol", + "26": "Verti-Gleyic Cambisol (?)", + "27": "Humic Cambisol", + "28": "Calcic Cambisol", + "29": "Fluvi-Calcic Cambisol", + "30": "Humo-Calcic Cambisol", + "31": "Verti-Calcic Cambisol", + "32": "Vertic Cambisol", + "33": "Calcaro-Vertic Cambisol", + "34": "Gelic Cambisol", + "35": "Chernozem", + "36": "Haplic Chernozem", + "37": "Calcic Chernozem", + "38": "Vermi-Calcic Chernozem", + "39": "Calcaro-Calcic Chernozem", + "40": "Vermi-Calcaro-Calcic Chernozem", + "41": "Luvic Chernozem", + "42": "Podzoluvisol", + "43": "Dystric Podzoluvisol", + "44": "Eutric Podzoluvisol", + "45": "Gleyic Podzoluvisol", + "46": "Eutric Gleyic Podzoluvisol", + "47": "Stagno-Gleyic Podzoluvisol", + "48": "Rendzina", + "49": "Cambic Rendzina", + "50": "Orthic Rendzina", + "51": "Calcaric Gleysol", + "52": "Dystric Gleysol", + "53": "Stagno-Dystric Gleysol", + "54": "Eutric Gleysol", + "55": "Fluvi-Eutric Gleysol", + "56": "Geh", + "57": "Stagno-Eutric Gleysol", + "58": "Verti-Eutric Gleysol", + "59": "Molli-Fluvic Gleysol", + "60": "Humic Gleysol", + "61": "Histo-Humic Gleysol", + "62": "Stagno-Luvic Gleysol", + "63": "Mollic Gleysol", + "64": "Calcaro-Mollic Gleysol", + "65": "Fluvi-Mollic Gleysol", + "66": "Stagnic Gleysol", + "67": "Undefined code", + "68": "Gelic Gleysol", + "69": "Calcaric Phaeozem", + "70": "Vermi-Calcaric Phaeozem", + "71": "Fluvi-Calcaric Phaeozem", + "72": "Alkalino-Calcaric Phaeozem", + "73": "Gleyic Phaeozem", + "74": "Calcaro-Gleyic Phaeozem", + "75": "Stagno-Gleyic Phaeozem", + "76": "Haplic Phaeozem", + "77": "Luvic Phaeozem", + "78": "Verti-Luvic Phaeozem", + "79": "Orthic Phaeozem", + "80": "Lithosol", + "81": "Calcaric Lithosol", + "82": "Humo-Calcaric Lithosol", + "83": "Dystric Lithosol", + "84": "Eutric Lithosol", + "85": "Io", + "86": "Calcaric Fluvisol", + "87": "Fluvi-Calcaric Fluvisol", + "88": "Gleyo-Calcaric Fluvisol", + "89": "Dystric Fluvisol", + "90": "Gleyo-Dystric Fluvisol", + "91": "Eutric Fluvisol", + "92": "Fluvi-Eutric Fluvisol", + "93": "Gleyo-Eutric Fluvisol", + "94": "Mollic Fluvisol", + "95": "Gleyo-Mollic Fluvisol", + "96": "Thionic Fluvisol", + "97": "Haplic Kastanozem", + "98": "Calcic Kastanozem", + "99": "Vermi-Calcic Kastanozem", + "100": "Luvic Kastanozem", + "101": "Albic Luvisol", + "102": "Chromic Luvisol", + "103": "Rhodo-Chromic Luvisol", + "104": "Verti-Chromic Luvisol", + "105": "Gleyo-Dystric Luvisol", + "106": "Ferric Luvisol", + "107": "Gleyic Luvisol", + "108": "Albo-Gleyic Luvisol", + "109": "Plano-Gleyic Luvisol", + "110": "Stagno-Gleyic Luvisol", + "111": "Calcic Luvisol", + "112": "Chromo-Calcic Luvisol", + "113": "Rhodo-Chromo-Calcic Luvisol", + "114": "Orthic Luvisol", + "115": "Vertic Luvisol", + "116": "Orthic Greyzem", + "117": "Dystric Histosol", + "118": "Eutric Histosol", + "119": "Gleyic Podzol", + "120": "Histo-Gleyic Podzol", + "121": "Stagno-Gleyic Podzol", + "122": "Humic Podzol", + "123": "Ferro-Humic Podzol", + "124": "Leptic Podzol", + "125": "Humo-Leptic Podzol", + "126": "Orthic Podzol", + "127": "Ferro-Orthic Podzol", + "128": "Humo-Orthic Podzol", + "129": "Placic Podzol", + "130": "Arenosol", + "131": "Albic Arenosol", + "132": "Cambic Arenosol", + "133": "Calcaro-Cambic Arenosol", + "134": "Dystri-Cambic Arenosol", + "135": "Humic Arenosol", + "136": "Luvic Arenosol", + "137": "Dystri-Luvic Arenosol", + "138": "Calcaric Regosol", + "139": "Dystric Regosol", + "140": "Eutric Regosol", + "141": "Gelic Regosol", + "142": "Gleyic Solonetz", + "143": "Mollic Solonetz", + "144": "Orthic Solonetz", + "145": "Fluvi-Orthic Solonetz", + "146": "Humic Andosol", + "147": "Ochric Andosol", + "148": "Vitric Andosol", + "149": "Ranker", + "150": "Dystric Ranker", + "151": "Chromic Vertisol", + "152": "Calcaro-Chromic Vertisol", + "153": "Gleyic Vertisol", + "154": "Pellic Vertisol", + "155": "Gleyo-Pellic Vertisol", + "156": "Dystric Planosol", + "157": "Eutric Planosol", + "158": "Verti-Eutric Planosol", + "159": "Mollic Planosol", + "160": "Solodic Planosol", + "161": "Calcic Xerosol", + "162": "Gypsic Xerosol", + "163": "Solonchak", + "164": "Gleyic Solonchak", + "165": "Orthic Solonchak", + "166": "No information", + "222": "Soil disturbed by man", + "333": "Water body", + "444": "Marsh", + "555": "Glacier", + "666": "Rock outcrops" + } + ], + "fao85_level1_classification": [ + "FAO85LV1/FAO85LV1", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Acrisol", + "8": "Cambisol", + "9": "Chernozem", + "10": "Podzoluvisol", + "11": "Rendzina", + "12": "Gleysol", + "13": "Phaeozem", + "14": "Lithosol", + "15": "Fluvisol", + "16": "Kastanozem", + "17": "Luvisol", + "18": "Greyzem", + "19": "Histosol", + "20": "Podzol", + "21": "Arenosol", + "22": "Regosol", + "23": "Solonetz", + "24": "Andosol", + "25": "Ranker", + "26": "Vertisol", + "27": "Planosol", + "28": "Xerosol", + "29": "Solonchak", + "30": "No information" + } + ], + "fao85_level2_classification": [ + "FAO85LV2/FAO85LV2", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Acrisol", + "8": "Gleyic Acrisol", + "9": "Humic Acrisol", + "10": "Orthic Acrisol", + "11": "Calcaric Cambisol", + "12": "Chromic Cambisol", + "13": "Dystric Cambisol", + "14": "Eutric Cambisol", + "15": "Gleyic Cambisol", + "16": "Humic Cambisol", + "17": "Calcic Cambisol", + "18": "Vertic Cambisol", + "19": "Gelic Cambisol", + "20": "Chernozem", + "21": "Haplic Chernozem", + "22": "Calcic Chernozem", + "23": "Luvic Chernozem", + "24": "Podzoluvisol", + "25": "Dystric Podzoluvisol", + "26": "Eutric Podzoluvisol", + "27": "Gleyic Podzoluvisol", + "28": "Rendzina", + "29": "Cambic Rendzina", + "30": "Orthic Rendzina", + "31": "Calcaric Gleysol", + "32": "Dystric Gleysol", + "33": "Eutric Gleysol", + "34": "Fluvic Gleysol", + "35": "Humic Gleysol", + "36": "Luvic Gleysol", + "37": "Mollic Gleysol", + "38": "Stagnic Gleysol", + "39": "Thionic Gleysol", + "40": "Gelic Gleysol", + "41": "Calcaric Phaeozem", + "42": "Gleyic Phaeozem", + "43": "Haplic Phaeozem", + "44": "Luvic Phaeozem", + "45": "Orthic Phaeozem", + "46": "Lithosol", + "47": "Calcaric Lithosol", + "48": "Dystric Lithosol", + "49": "Eutric Lithosol", + "50": "Io", + "51": "Calcaric Fluvisol", + "52": "Dystric Fluvisol", + "53": "Eutric Fluvisol", + "54": "Mollic Fluvisol", + "55": "Thionic Fluvisol", + "56": "Haplic Kastanozem", + "57": "Calcic Kastanozem", + "58": "Luvic Kastanozem", + "59": "Albic Luvisol", + "60": "Chromic Luvisol", + "61": "Dystric Luvisol", + "62": "Ferric Luvisol", + "63": "Gleyic Luvisol", + "64": "Calcic Luvisol", + "65": "Orthic Luvisol", + "66": "Vertic Luvisol", + "67": "Orthic Greyzem", + "68": "Dystric Histosol", + "69": "Eutric Histosol", + "70": "Gleyic Podzol", + "71": "Humic Podzol", + "72": "Leptic Podzol", + "73": "Orthic Podzol", + "74": "Placic Podzol", + "75": "Arenosol", + "76": "Albic Arenosol", + "77": "Cambic Arenosol", + "78": "Humic Arenosol", + "79": "Luvic Arenosol", + "80": "Calcaric Regosol", + "81": "Dystric Regosol", + "82": "Eutric Regosol", + "83": "Gelic Regosol", + "84": "Gleyic Solonetz", + "85": "Mollic Solonetz", + "86": "Orthic Solonetz", + "87": "Humic Andosol", + "88": "Ochric Andosol", + "89": "Vitric Andosol", + "90": "Ranker", + "91": "Dystric Ranker", + "92": "Chromic Vertisol", + "93": "Gleyic Vertisol", + "94": "Pellic Vertisol", + "95": "Dystric Planosol", + "96": "Eutric Planosol", + "97": "Mollic Planosol", + "98": "Solodic Planosol", + "99": "Calcic Xerosol", + "100": "Gypsic Xerosol", + "101": "Solonchak", + "102": "Gleyic Solonchak", + "103": "Orthic Solonchak", + "104": "No information" + } + ], + "fao85_level3_classification": [ + "FAO85LV3/FAO85LV3", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Acrisol", + "8": "Gleyic Acrisol", + "9": "Humic Acrisol", + "10": "Orthic Acrisol", + "11": "Calcaric Cambisol", + "12": "Chromic Cambisol", + "13": "Calcaro-Chromic Cambisol", + "14": "Dystric Cambisol", + "15": "Ando-Dystric Cambisol", + "16": "Spodo-Dystric Cambisol", + "17": "Eutric Cambisol", + "18": "Ando-Eutric Cambisol", + "19": "Calcaro-Eutric Cambisol", + "20": "Fluvi-Eutric Cambisol", + "21": "Gleyo-Eutric Cambisol", + "22": "Verti-Eutric Cambisol", + "23": "Gleyic Cambisol", + "24": "Calcaro-Gleyic Cambisol", + "25": "Stagno-Gleyic Cambisol", + "26": "Verti-Gleyic Cambisol (?)", + "27": "Humic Cambisol", + "28": "Calcic Cambisol", + "29": "Fluvi-Calcic Cambisol", + "30": "Humo-Calcic Cambisol", + "31": "Verti-Calcic Cambisol", + "32": "Vertic Cambisol", + "33": "Calcaro-Vertic Cambisol", + "34": "Gelic Cambisol", + "35": "Chernozem", + "36": "Haplic Chernozem", + "37": "Calcic Chernozem", + "38": "Vermi-Calcic Chernozem", + "39": "Calcaro-Calcic Chernozem", + "40": "Luvic Chernozem", + "41": "Podzoluvisol", + "42": "Dystric Podzoluvisol", + "43": "Eutric Podzoluvisol", + "44": "Gleyic Podzoluvisol", + "45": "Eutric Gleyic Podzoluvisol", + "46": "Stagno-Gleyic Podzoluvisol", + "47": "Rendzina", + "48": "Cambic Rendzina", + "49": "Orthic Rendzina", + "50": "Calcaric Gleysol", + "51": "Dystric Gleysol", + "52": "Stagno-Dystric Gleysol", + "53": "Eutric Gleysol", + "54": "Fluvi-Eutric Gleysol", + "55": "Geh", + "56": "Stagno-Eutric Gleysol", + "57": "Verti-Eutric Gleysol", + "58": "Molli-Fluvic Gleysol", + "59": "Humic Gleysol", + "60": "Histo-Humic Gleysol", + "61": "Stagno-Luvic Gleysol", + "62": "Mollic Gleysol", + "63": "Calcaro-Mollic Gleysol", + "64": "Fluvi-Mollic Gleysol", + "65": "Stagnic Gleysol", + "66": "Undefined code", + "67": "Gelic Gleysol", + "68": "Calcaric Phaeozem", + "69": "Vermi-Calcaric Phaeozem", + "70": "Fluvi-Calcaric Phaeozem", + "71": "Alkalino-Calcaric Phaeozem", + "72": "Gleyic Phaeozem", + "73": "Calcaro-Gleyic Phaeozem", + "74": "Stagno-Gleyic Phaeozem", + "75": "Haplic Phaeozem", + "76": "Luvic Phaeozem", + "77": "Verti-Luvic Phaeozem", + "78": "Orthic Phaeozem", + "79": "Lithosol", + "80": "Calcaric Lithosol", + "81": "Humo-Calcaric Lithosol", + "82": "Dystric Lithosol", + "83": "Eutric Lithosol", + "84": "Io", + "85": "Calcaric Fluvisol", + "86": "Fluvi-Calcaric Fluvisol", + "87": "Gleyo-Calcaric Fluvisol", + "88": "Dystric Fluvisol", + "89": "Gleyo-Dystric Fluvisol", + "90": "Eutric Fluvisol", + "91": "Fluvi-Eutric Fluvisol", + "92": "Gleyo-Eutric Fluvisol", + "93": "Mollic Fluvisol", + "94": "Gleyo-Mollic Fluvisol", + "95": "Thionic Fluvisol", + "96": "Haplic Kastanozem", + "97": "Calcic Kastanozem", + "98": "Vermi-Calcic Kastanozem", + "99": "Luvic Kastanozem", + "100": "Albic Luvisol", + "101": "Chromic Luvisol", + "102": "Rhodo-Chromic Luvisol", + "103": "Verti-Chromic Luvisol", + "104": "Gleyo-Dystric Luvisol", + "105": "Ferric Luvisol", + "106": "Gleyic Luvisol", + "107": "Albo-Gleyic Luvisol", + "108": "Plano-Gleyic Luvisol", + "109": "Stagno-Gleyic Luvisol", + "110": "Calcic Luvisol", + "111": "Chromo-Calcic Luvisol", + "112": "Orthic Luvisol", + "113": "Vertic Luvisol", + "114": "Orthic Greyzem", + "115": "Dystric Histosol", + "116": "Eutric Histosol", + "117": "Gleyic Podzol", + "118": "Histo-Gleyic Podzol", + "119": "Stagno-Gleyic Podzol", + "120": "Humic Podzol", + "121": "Ferro-Humic Podzol", + "122": "Leptic Podzol", + "123": "Humo-Leptic Podzol", + "124": "Orthic Podzol", + "125": "Ferro-Orthic Podzol", + "126": "Humo-Orthic Podzol", + "127": "Placic Podzol", + "128": "Arenosol", + "129": "Albic Arenosol", + "130": "Cambic Arenosol", + "131": "Calcaro-Cambic Arenosol", + "132": "Dystri-Cambic Arenosol", + "133": "Humic Arenosol", + "134": "Luvic Arenosol", + "135": "Dystri-Luvic Arenosol", + "136": "Calcaric Regosol", + "137": "Dystric Regosol", + "138": "Eutric Regosol", + "139": "Gelic Regosol", + "140": "Gleyic Solonetz", + "141": "Mollic Solonetz", + "142": "Orthic Solonetz", + "143": "Fluvi-Orthic Solonetz", + "144": "Humic Andosol", + "145": "Ochric Andosol", + "146": "Vitric Andosol", + "147": "Ranker", + "148": "Dystric Ranker", + "149": "Chromic Vertisol", + "150": "Calcaro-Chromic Vertisol", + "151": "Gleyic Vertisol", + "152": "Pellic Vertisol", + "153": "Gleyo-Pellic Vertisol", + "154": "Dystric Planosol", + "155": "Eutric Planosol", + "156": "Verti-Eutric Planosol", + "157": "Mollic Planosol", + "158": "Solodic Planosol", + "159": "Calcic Xerosol", + "160": "Gypsic Xerosol", + "161": "Solonchak", + "162": "Gleyic Solonchak", + "163": "Orthic Solonchak", + "164": "No information", + "222": "Soil disturbed by man", + "333": "Water body", + "444": "Marsh", + "555": "Glacier", + "666": "Rock outcrops" + } + ], + "fao90_full_classification": [ + "FAO90FU/FAO90FU", + { + "1": "1 11", + "2": "2 22", + "3": "3 33", + "4": "4 44", + "5": "5 55", + "6": "6 66", + "7": "Gleyic Acrisol", + "8": "Haplic Acrisol", + "9": "Alisol", + "10": "Haplic Andosol", + "11": "Gelic Andosol", + "12": "Umbric Andosol", + "13": "Cambic Arenosol", + "14": "Calcaric Arenosol", + "15": "?-Calcaric Arenosol", + "16": "Gleyic Arenosol", + "17": "Haplic Arenosol", + "18": "Luvic Arenosol", + "19": "Aric Anthrosol", + "20": "Cumulic Anthrosol", + "21": "Haplic Chernozem", + "22": "?-Haplic Chernozem", + "23": "Calcic Chernozem", + "24": "?-Calcic Chernozem", + "25": "?-Calcic Chernozem", + "26": "?-Calcic Chernozem", + "27": "Luvic Chernozem", + "28": "Glossic Chernozem", + "29": "Haplic Calcisol", + "30": "Luvic Calcisol", + "31": "Petric Calcisol", + "32": "CMa", + "33": "Calcaric Cambisol", + "34": "Dystric Cambisol", + "35": "?-Dystric Cambisol", + "36": "?-Dystric Cambisol", + "37": "?-Dystric Cambisol", + "38": "Eutric Cambisol", + "39": "?-Eutric Cambisol", + "40": "?-Eutric Cambisol", + "41": "?-Eutric Cambisol", + "42": "?-Eutric Cambisol", + "43": "?-Eutric Cambisol", + "44": "?-Eutric Cambisol", + "45": "Gleyic Cambisol", + "46": "Gelic Cambisol", + "47": "Stagnic Cambisol", + "48": "Humic Cambisol", + "49": "Vertic Cambisol", + "50": "Chromic Cambisol", + "51": "Calcaric Fluvisol", + "52": "?-Calcaric Fluvisol", + "53": "?-Calcaric Fluvisol", + "54": "Dystric Fluvisol", + "55": "Eutric Fluvisol", + "56": "?-Eutric Fluvisol", + "57": "?-Eutric Fluvisol", + "58": "?-", + "59": "Mollic Fluvisol", + "60": "Salic Fluvisol", + "61": "Thionic Fluvisol", + "62": "Umbric Fluvisol", + "63": "Dystric Gleysol", + "64": "?-Dystric Gleysol", + "65": "Eutric Gleysol", + "66": "?-Eutric Gleysol", + "67": "GLh", + "68": "Gelic Gleysol", + "69": "Calcic Gleysol", + "70": "Mollic Gleysol", + "71": "?-Mollic Gleysol", + "72": "Umbric Gleysol", + "73": "Haplic Greyzem", + "74": "Haplic Gypsisol", + "75": "Histosol", + "76": "Fibric Histosol", + "77": "?-Fibric Histosol", + "78": "Gelic Histosol", + "79": "Folic Histosol", + "80": "Terric Histosol", + "81": "Haplic Kastanozem", + "82": "Calcic Kastanozem", + "83": "?-Calcic Kastanozem", + "84": "Luvic Kastanozem", + "85": "Dystric Leptosol", + "86": "Eutric Leptosol", + "87": "Gelic Leptosol", + "88": "Rendzic Leptosol", + "89": "Mollic Leptosol", + "90": "Lithic Leptosol", + "91": "LPr", + "92": "Umbric Leptosol", + "93": "Albic Luvisol", + "94": "?-Albic Luvisol", + "95": "LVe", + "96": "Gleyic Luvisol", + "97": "Haplic Luvisol", + "98": "Stagnic Luvisol", + "99": "?-Stagnic Luvisol", + "100": "Calcic Luvisol", + "101": "LVo", + "102": "Vertic Luvisol", + "103": "Chromic Luvisol", + "104": "?-Chromic Luvisol", + "105": "Dystric Podzoluvisol", + "106": "Eutric Podzoluvisol", + "107": "Gleyic Podzoluvisol", + "108": "Stagnic Podzoluvisol", + "109": "PFg", + "110": "Calcaric Phaeozem", + "111": "Gleyic Phaeozem", + "112": "Haplic Phaeozem", + "113": "PHi", + "114": "Stagnic Phaeozem", + "115": "Luvic Phaeozem", + "116": "?-Luvic Phaeozem", + "117": "Dystric Planosol", + "118": "Eutric Planosol", + "119": "Mollic Planosol", + "120": "Cambic Podzol", + "121": "Carbic Podzol", + "122": "Ferric Podzol", + "123": "Gleyic Podzol", + "124": "Haplic Podzol", + "125": "?-Haplic Podzol", + "126": "Gelic Podzol", + "127": "Regosol", + "128": "Calcaric Regosol", + "129": "Dystric Regosol", + "130": "Eutric Regosol", + "131": "Umbric Regosol", + "132": "Rge", + "133": "Solonchak", + "134": "Gleyic Solonchak", + "135": "Haplic Solonchak", + "136": "Gleyic Solonetz", + "137": "Haplic Solonetz", + "138": "Mollic Solonetz", + "139": "Eutric Vertisol", + "140": "?-Eutric Vertisol", + "141": "No information" + } + ], + "fao90_level1_classification": [ + "FAO90LV1/FAO90LV1", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Acrisol", + "8": "Alisol", + "9": "Andosol", + "10": "Arenosol", + "11": "Anthrosol", + "12": "Chernozem", + "13": "Calcisol", + "14": "Cambisol", + "15": "Fluvisol", + "16": "Gleysol", + "17": "Greyzem", + "18": "Gypsisol", + "19": "Histosol", + "20": "Kastanozem", + "21": "Leptosol", + "22": "Luvisol", + "23": "Podzoluvisol", + "24": "PF", + "25": "Phaeozem", + "26": "Planosol", + "27": "Podzol", + "28": "Regosol", + "29": "Rg", + "30": "Solonchak", + "31": "Solonetz", + "32": "Vertisol", + "33": "No information" + } + ], + "fao90_level2_classification": [ + "FAO90LV2/FAO90LV2", + { + "1": "1 1", + "2": "2 2", + "3": "3 3", + "4": "4 4", + "5": "5 5", + "6": "6 6", + "7": "Gleyic Acrisol", + "8": "Haplic Acrisol", + "9": "Alisol", + "10": "Haplic Andosol", + "11": "Gelic Andosol", + "12": "Umbric Andosol", + "13": "Cambic Arenosol", + "14": "Calcaric Arenosol", + "15": "Gleyic Arenosol", + "16": "Haplic Arenosol", + "17": "Luvic Arenosol", + "18": "Aric Anthrosol", + "19": "Cumulic Anthrosol", + "20": "Haplic Chernozem", + "21": "Calcic Chernozem", + "22": "Luvic Chernozem", + "23": "Glossic Chernozem", + "24": "Haplic Calcisol", + "25": "Luvic Calcisol", + "26": "Petric Calcisol", + "27": "CMa", + "28": "Calcaric Cambisol", + "29": "Dystric Cambisol", + "30": "Eutric Cambisol", + "31": "Gleyic Cambisol", + "32": "Gelic Cambisol", + "33": "Stagnic Cambisol", + "34": "Humic Cambisol", + "35": "Vertic Cambisol", + "36": "Chromic Cambisol", + "37": "Calcaric Fluvisol", + "38": "Dystric Fluvisol", + "39": "Eutric Fluvisol", + "40": "FLh", + "41": "Mollic Fluvisol", + "42": "Salic Fluvisol", + "43": "Thionic Fluvisol", + "44": "Umbric Fluvisol", + "45": "Dystric Gleysol", + "46": "Eutric Gleysol", + "47": "GLh", + "48": "Gelic Gleysol", + "49": "Calcic Gleysol", + "50": "Mollic Gleysol", + "51": "Umbric Gleysol", + "52": "Haplic Greyzem", + "53": "Haplic Gypsisol", + "54": "Histosol", + "55": "Fibric Histosol", + "56": "Gelic Histosol", + "57": "Folic Histosol", + "58": "Terric Histosol", + "59": "Haplic Kastanozem", + "60": "Calcic Kastanozem", + "61": "Luvic Kastanozem", + "62": "Dystric Leptosol", + "63": "Eutric Leptosol", + "64": "Gelic Leptosol", + "65": "Rendzic Leptosol", + "66": "Mollic Leptosol", + "67": "Lithic Leptosol", + "68": "LPr", + "69": "Umbric Leptosol", + "70": "Albic Luvisol", + "71": "LVe", + "72": "Gleyic Luvisol", + "73": "Haplic Luvisol", + "74": "Stagnic Luvisol", + "75": "Calcic Luvisol", + "76": "LVo", + "77": "Vertic Luvisol", + "78": "Chromic Luvisol", + "79": "Dystric Podzoluvisol", + "80": "Eutric Podzoluvisol", + "81": "Gleyic Podzoluvisol", + "82": "Stagnic Podzoluvisol", + "83": "PFg", + "84": "Calcaric Phaeozem", + "85": "Gleyic Phaeozem", + "86": "Haplic Phaeozem", + "87": "PHi", + "88": "Stagnic Phaeozem", + "89": "Luvic Phaeozem", + "90": "Dystric Planosol", + "91": "Eutric Planosol", + "92": "Mollic Planosol", + "93": "Cambic Podzol", + "94": "Carbic Podzol", + "95": "Ferric Podzol", + "96": "Gleyic Podzol", + "97": "Haplic Podzol", + "98": "Gelic Podzol", + "99": "Regosol", + "100": "Calcaric Regosol", + "101": "Dystric Regosol", + "102": "Eutric Regosol", + "103": "Umbric Regosol", + "104": "Rge", + "105": "Solonchak", + "106": "Gleyic Solonchak", + "107": "Haplic Solonchak", + "108": "Gleyic Solonetz", + "109": "Haplic Solonetz", + "110": "Mollic Solonetz", + "111": "Eutric Vertisol", + "112": "No information" + } + ], + "WRB_primary_adjective": [ + "WRBADJ1/WRBADJ1", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Albic", + "8": "Aridic", + "9": "Andic", + "10": "Acroxic", + "11": "Arenic", + "12": "Calcaric", + "13": "Carbic", + "14": "Calcic", + "15": "Chernic", + "16": "Chromic", + "17": "Cryic", + "18": "Dystric", + "19": "Entic", + "20": "Eutric", + "21": "Fibric", + "22": "Ferric", + "23": "Gelic", + "24": "Gleyic", + "25": "Glossic", + "26": "Haplic", + "27": "Histic", + "28": "Humic", + "29": "Leptic", + "30": "Lithic", + "31": "lv", + "32": "Mollic", + "33": "Pellic", + "34": "Placic", + "35": "Protic", + "36": "Rustic", + "37": "Rendzic", + "38": "Sapric", + "39": "Sodic", + "40": "Stagnic", + "41": "Salic", + "42": "th", + "43": "Thionic", + "44": "Turbic", + "45": "Umbric", + "46": "Vertic", + "47": "No information" + } + ], + "WRB_secondary_adjective": [ + "WRBADJ2/WRBADJ2", + { + "1": "Soil disturbed by man", + "2": "Water body", + "3": "Marsh", + "4": "Glacier", + "5": "Rock outcrops", + "6": "No information" + } + ], + "WRB_full_classification": [ + "WRBFU/WRBFU", + { + "1": "1 1 1", + "2": "2 2 22 2", + "3": "3 3 33 3", + "4": "4 4 44 4", + "5": "5 5 55 5", + "6": "6 6 66 6", + "7": "ABeun", + "8": "ABgl", + "9": "ABha", + "10": "ABhi", + "11": "ABst", + "12": "ABum", + "13": "ACgl", + "14": "ACha", + "15": "ANao", + "16": "ANdy", + "17": "ANhi", + "18": "ANhu", + "19": "ANth", + "20": "ARab", + "21": "ARha", + "22": "ARpr", + "23": "CHcc", + "24": "CHch", + "25": "CHgs", + "26": "CHha", + "27": "CHlv", + "28": "CLad", + "29": "CLha", + "30": "CLszn", + "31": "CMca", + "32": "CMcr", + "33": "CMdy", + "34": "CMeu", + "35": "CMge", + "36": "CMgl", + "37": "CMhu", + "38": "CMmo", + "39": "CMvr", + "40": "CRan", + "41": "CRcc", + "42": "CRgl", + "43": "CRha", + "44": "CRhi", + "45": "CRtu", + "46": "CRum", + "47": "FLca", + "48": "FLdy", + "49": "FLeu", + "50": "FLgl", + "51": "FLhi", + "52": "FLmo", + "53": "FLsz", + "54": "FLti", + "55": "FLum", + "56": "GLca", + "57": "GLdy", + "58": "GLeu", + "59": "GLha", + "60": "GLhi", + "61": "GLhu", + "62": "GLmo", + "63": "GLso", + "64": "GLti", + "65": "GYad", + "66": "HScy", + "67": "HSdy", + "68": "HSeu", + "69": "HSfi", + "70": "HSge", + "71": "HSsa", + "72": "HSsz", + "73": "KScc", + "74": "KSha", + "75": "KSlv", + "76": "LPca", + "77": "LPdy", + "78": "LPeu", + "79": "LPha", + "80": "LPhu", + "81": "LPli", + "82": "LPmo", + "83": "LPrz", + "84": "LPum", + "85": "LVab", + "86": "LVar", + "87": "LVcc", + "88": "LVcr", + "89": "LVdy", + "90": "LVfr", + "91": "LVgl", + "92": "LVha", + "93": "LVvr", + "94": "PHab", + "95": "PHca", + "96": "PHgl", + "97": "PHha", + "98": "PHlv", + "99": "PHso", + "100": "PLab", + "101": "PLdy", + "102": "PLeu", + "103": "PLlv", + "104": "PLmo", + "105": "PZcb", + "106": "PZet", + "107": "PZgl", + "108": "PZha", + "109": "PZhi", + "110": "PZle", + "111": "PZpi", + "112": "PZrs", + "113": "PZum", + "114": "RGca", + "115": "RGdy", + "116": "RGeu", + "117": "RGha", + "118": "Solonchak", + "119": "SCgl", + "120": "SCha", + "121": "SNgl", + "122": "SNha", + "123": "SNmo", + "124": "UMar", + "125": "UMgl", + "126": "VRcr", + "127": "VRha", + "128": "VRpe", + "129": "No information" + } + ], + "WRB_level1_class": [ + "WRBLV1/WRBLV1", + { + "1": "Town", + "2": "Soil disturbed by man", + "3": "Water body", + "4": "Marsh", + "5": "Glacier", + "6": "Rock outcrops", + "7": "Albeluvisol", + "8": "Acrisol", + "9": "Andosol", + "10": "Arenosol", + "11": "Chernozem", + "12": "Calcisol", + "13": "Cambisol", + "14": "Cryosol", + "15": "Fluvisol", + "16": "Gleysol", + "17": "Gypsisol", + "18": "Histosol", + "19": "Kastanozem", + "20": "Leptosol", + "21": "Luvisol", + "22": "Phaeozem", + "23": "Planosol", + "24": "Podzol", + "25": "Regosol", + "26": "Solonchak", + "27": "Solonetz", + "28": "Umbrisol", + "29": "Vertisol", + "30": "No information" + } + ] + }, + "mineral": { + "profile_mineralogy": [ + "MIN_/MIN_", + { + "1": "Chemical or Geochemical", + "2": "Mechanical or Physical", + "3": "Chemical and Mechanical", + "4": "No differentiation", + "5": "No information" + } + ], + "mineralogy_subsoil": [ + "MIN_SUB/MIN_SUB", + { + "1": "1/1 Min. + Oxy. & Hydroxy.", + "2": "2/1 & 2/1/1 non swelling Minerals", + "3": "2/1 & 1/1 Minerals", + "4": "Swelling & non swelling 2/1 Minerals", + "5": "Not applicable", + "6": "Andic Minerals", + "7": "Vitric Minerals", + "8": "No information" + } + ], + "mineralogy_topsoil": [ + "MIN_TOP/MIN_TOP", + { + "1": "1/1 Minerals + Quartz", + "2": "1/1 Min. + Oxy. & Hydroxy.", + "3": "2/1 & 2/1/1 non swelling Minerals", + "4": "2/1 & 1/1 Minerals", + "5": "Swelling & non swelling 2/1 Minerals", + "6": "Not applicable", + "7": "Andic Minerals", + "8": "Vitric Minerals", + "9": "No information" + } + ] + }, + "carbon": { + "organic_carbon_content_class_topsoil": [ + "OC_TOP/OC_TOP", + { + "1": "High (> 6%)", + "2": "Low (1–2%)", + "3": "Medium (2–6%)", + "4": "Very low (< 1%)", + "5": "No information" + } + ] + }, + "texture": { + "textural_change_depth_class": [ + "TXDEPCHG/TXDEPCHG", + { + "0": "No information", + "1": "Textural change between 20 and 40 cm depth", + "2": "Textural change between 40 and 60 cm depth", + "3": "Textural change between 60 and 80 cm depth", + "4": "Textural change between 80 and 120 cm depth", + "5": "No textural change between 20 and 120 cm depth", + "6": "Textural change between 20 and 60 cm depth", + "7": "Textural change between 60 and 120 cm depth" + } + ], + "dominant_textural_class_topsoil_ESDB": [ + "TXSRFDO/TXSRFDO", + { + "0": "No information", + "1": "Coarse (18% < clay and > 65% sand)", + "2": "Medium (18% < clay < 35% and β‰₯ 15% sand, or 18% < clay and 15% < sand < 65%)", + "3": "Medium fine (< 35% clay and < 15% sand)", + "4": "Fine (35% < clay < 60%)", + "5": "Very fine (clay > 60%)", + "9": "No mineral texture (Peat soils)" + } + ], + "secondary_textural_class_topsoil_ESDB": [ + "TXSUBDO/TXSUBDO", + { + "0": "No information", + "1": "Coarse (18% < clay and > 65% sand)", + "2": "Medium (18% < clay < 35% and β‰₯ 15% sand, or 18% < clay and 15% < sand < 65%)", + "3": "Medium fine (< 35% clay and < 15% sand)", + "4": "Fine (35% < clay < 60%)", + "5": "Very fine (clay > 60%)", + "9": "No mineral texture (Peat soils)" + } + ], + "secondary_textural_class_subsoil_ESDB": [ + "TXSUBSE/TXSUBSE", + { + "0": "No information", + "1": "Coarse (18% < clay and > 65% sand)", + "2": "Medium (18% < clay < 35% and β‰₯ 15% sand, or 18% < clay and 15% < sand < 65%)", + "3": "Medium fine (< 35% clay and < 15% sand)", + "4": "Fine (35% < clay < 60%)", + "5": "Very fine (clay > 60%)", + "9": "No mineral texture (Peat soils)" + } + ] + } + } +} diff --git a/src/esdac/european-soil-database-v2-raster-library-1kmx1km/process.py b/src/esdac/european-soil-database-v2-raster-library-1kmx1km/process.py new file mode 100644 index 0000000000000000000000000000000000000000..abbba82e8e1878b6cb6cf362040f1797895df5eb --- /dev/null +++ b/src/esdac/european-soil-database-v2-raster-library-1kmx1km/process.py @@ -0,0 +1,22 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all tif files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + move_list.append(tif_path.with_suffix(".txt")) + move_list.append(tif_path.with_suffix(".vat.dbf")) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/european-soil-database-v20-vector-and-attribute-data/fuse_skip.txt b/src/esdac/european-soil-database-v20-vector-and-attribute-data/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..d28d26cf93dbc649c5abf43594766d8174496f81 --- /dev/null +++ b/src/esdac/european-soil-database-v20-vector-and-attribute-data/fuse_skip.txt @@ -0,0 +1 @@ +Raster version used \ No newline at end of file diff --git a/src/esdac/european-soil-database-v20-vector-and-attribute-data/process.py b/src/esdac/european-soil-database-v20-vector-and-attribute-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..aef96441f8911984540de37954847861119e27d1 --- /dev/null +++ b/src/esdac/european-soil-database-v20-vector-and-attribute-data/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .shp files === +move_list = [] +for shp in local_processed_dir.rglob("*.shp"): + move_list = process_shp(shp, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-rainfall-erosivity-projections-2050-and-2070/fuse_skip.txt b/src/esdac/global-rainfall-erosivity-projections-2050-and-2070/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba243ba69234fff694f83a4fb538f1a70802bb87 --- /dev/null +++ b/src/esdac/global-rainfall-erosivity-projections-2050-and-2070/fuse_skip.txt @@ -0,0 +1 @@ +Projection \ No newline at end of file diff --git a/src/esdac/global-rainfall-erosivity-projections-2050-and-2070/process.py b/src/esdac/global-rainfall-erosivity-projections-2050-and-2070/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/global-rainfall-erosivity-projections-2050-and-2070/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-rainfall-erosivity/fuse_schema.json b/src/esdac/global-rainfall-erosivity/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5f013c998dca614f7e7b445788b3b4136420984f --- /dev/null +++ b/src/esdac/global-rainfall-erosivity/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "GlobalR@tif": { + "erosion": { + "r_factor_rainfall (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": "GlobalR_NoPol" + } + } +} diff --git a/src/esdac/global-rainfall-erosivity/process.py b/src/esdac/global-rainfall-erosivity/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/global-rainfall-erosivity/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-soil-biodiversity-maps-0/fuse_skip.txt b/src/esdac/global-soil-biodiversity-maps-0/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/global-soil-biodiversity-maps-0/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/global-soil-biodiversity-maps-0/process.py b/src/esdac/global-soil-biodiversity-maps-0/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/global-soil-biodiversity-maps-0/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-soil-erodibility/fuse_schema.json b/src/esdac/global-soil-erodibility/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a592c2ea0e67afc93e3e6c1a632993885f34c49d --- /dev/null +++ b/src/esdac/global-soil-erodibility/fuse_schema.json @@ -0,0 +1,11 @@ +{ + "Final_soil_erodibility_data@tif": { + "erosion": { + "k_factor_based_on_soil_texture (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "K_factor_soiltexture_Wischmeier", + "k_factor_based_on_soil_texture_uncertainty (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "K_factor_soiltexture_Wischmeier_error", + "k_factor_with_saturated_hydraulic_conductivity (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "K_factor_with_Ksat", + "k_factor_with_saturated_hydraulic_conductivity_uncertainty (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "K_factor_with_Ksat_error", + "k_factor_from_glosem_model (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "K_GloSEM_factor" + } + } +} diff --git a/src/esdac/global-soil-erodibility/process.py b/src/esdac/global-soil-erodibility/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/global-soil-erodibility/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-soil-erosion-water-2070/fuse_skip.txt b/src/esdac/global-soil-erosion-water-2070/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba243ba69234fff694f83a4fb538f1a70802bb87 --- /dev/null +++ b/src/esdac/global-soil-erosion-water-2070/fuse_skip.txt @@ -0,0 +1 @@ +Projection \ No newline at end of file diff --git a/src/esdac/global-soil-erosion-water-2070/process.py b/src/esdac/global-soil-erosion-water-2070/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/global-soil-erosion-water-2070/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-soil-erosion/fuse_skip.txt b/src/esdac/global-soil-erosion/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/global-soil-erosion/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/global-soil-erosion/process.py b/src/esdac/global-soil-erosion/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/global-soil-erosion/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-soil-organic-carbon-estimates/fuse_schema.json b/src/esdac/global-soil-organic-carbon-estimates/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..6480a76fe0020abdd0f24c90c3d88d4dd960f16d --- /dev/null +++ b/src/esdac/global-soil-organic-carbon-estimates/fuse_schema.json @@ -0,0 +1,16 @@ +{ + "HWSDa_OC_Dens_30SEC_EAST@tif": { + "CRS_STRING": "EPSG:4326", + "carbon": { + "estimated_organic_carbon_density_topsoil (t ha⁻¹)": "HWSDa_OC_Dens_TOP_30SEC_East", + "estimated_organic_carbon_density_subsoil (t ha⁻¹)": "HWSDa_OC_Dens_SUB_30SEC_East" + } + }, + "HWSDa_OC_Dens_30SEC_WEST@tif": { + "CRS_STRING": "EPSG:4326", + "carbon": { + "estimated_organic_carbon_density_topsoil (t ha⁻¹)": "HWSDa_OC_Dens_TOP_30SEC_West", + "estimated_organic_carbon_density_subsoil (t ha⁻¹)": "HWSDa_OC_Dens_SUB_30SEC_West" + } + } +} diff --git a/src/esdac/global-soil-organic-carbon-estimates/process.py b/src/esdac/global-soil-organic-carbon-estimates/process.py new file mode 100644 index 0000000000000000000000000000000000000000..8ee343bcccea3239cfb00c828c77239206b02846 --- /dev/null +++ b/src/esdac/global-soil-organic-carbon-estimates/process.py @@ -0,0 +1,63 @@ +import re +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_rdc_rst + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all RDC/RST files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.RDC")): + move_list = process_rdc_rst(tif_path, move_list) + +# === Step 4: Separate EAST/WEST === +src_root = local_processed_dir / "HWSDa_OC_Dens_30SEC" +east_dir = local_processed_dir / "HWSDa_OC_Dens_30SEC_EAST" +west_dir = local_processed_dir / "HWSDa_OC_Dens_30SEC_WEST" +east_dir.mkdir(exist_ok=True) +west_dir.mkdir(exist_ok=True) + +side_re = re.compile(r'_(East|West)(?=\.|$)', re.IGNORECASE) +renamed_map = {} + +# Iterate over actual files on disk +for file in src_root.glob("*"): + if not isinstance(file, Path) or not file.is_file(): + continue + if file.suffix.lower() not in {".tif", ".png"}: + continue + + m = side_re.search(file.name) + if not m: + continue + + side = m.group(1).lower() + target_dir = east_dir if side == "east" else west_dir + target = target_dir / file.name + + target.parent.mkdir(parents=True, exist_ok=True) + file.replace(target) + renamed_map[file] = target + +# Rebuild move_list safely +updated_move_list = [] +for item in move_list: + if isinstance(item, tuple): + src, dst = item + src = renamed_map.get(src, src) + updated_move_list.append((src, dst)) + else: + src = renamed_map.get(item, item) + updated_move_list.append(src) + +move_list = updated_move_list + +# === Step 5: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due/fuse_skip.txt b/src/esdac/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due/process.py b/src/esdac/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due/process.py new file mode 100644 index 0000000000000000000000000000000000000000..d02c9d56c9c086d1c2a6dd76a14860e860739abf --- /dev/null +++ b/src/esdac/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due/process.py @@ -0,0 +1,21 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload, unzip_nested +from src.process.processor import process_rdc_rst + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) +unzip_nested(local_processed_dir) + +# === Step 3: Process all RDC/RST files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.RDC")): + move_list = process_rdc_rst(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates/fuse_skip.txt b/src/esdac/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates/process.py b/src/esdac/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/glosem/fuse_schema.json b/src/esdac/glosem/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..443c266eb8e17d23239ab1461084b306f10103b5 --- /dev/null +++ b/src/esdac/glosem/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1/Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1@tif": { + "CRS_STRING": "EPSG:4326", + "erosion": { + "soil_displacement_estimate (t ha⁻¹ yr⁻¹)": "SOIL_DISPLACEMENT_ESTIMATE_2019" + } + } +} diff --git a/src/esdac/glosem/process.py b/src/esdac/glosem/process.py new file mode 100644 index 0000000000000000000000000000000000000000..2aaa8de2bf5b1679530b685fcf1eec1d447569dc --- /dev/null +++ b/src/esdac/glosem/process.py @@ -0,0 +1,36 @@ +import os +from pathlib import Path + +import rasterio +from rasterio.transform import from_bounds + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) +os.system("mv src/esdac/glosem/processed/Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1/" + "Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1/SOIL_DISPLACEMENT_ESTIMATE_2019.tif.ovr " + "src/esdac/glosem/processed/Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1/" + "Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1/SOIL_DISPLACEMENT_ESTIMATE_2019.tif") + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +f = local_processed_dir / "Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1" / \ + "Data2_SOIL_DISPLACEMENT_ESTIMATE_2019_1" / "SOIL_DISPLACEMENT_ESTIMATE_2019.standardized.tif" + +with rasterio.open(f, "r+") as ds: + ds.crs = "EPSG:4326" + ds.transform = from_bounds(-180, -90, 180, 90, ds.width, ds.height) +print("βœ… CRS and transform fixed:", f) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/groundwater-resources-maps-europe-0/fuse_skip.txt b/src/esdac/groundwater-resources-maps-europe-0/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/groundwater-resources-maps-europe-0/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/groundwater-resources-maps-europe-0/process.py b/src/esdac/groundwater-resources-maps-europe-0/process.py new file mode 100644 index 0000000000000000000000000000000000000000..e45b26684fe3ffce7bfe6bea04195b988c4dd7ae --- /dev/null +++ b/src/esdac/groundwater-resources-maps-europe-0/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/gully-erosion-based-lucas/fuse_schema.json b/src/esdac/gully-erosion-based-lucas/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b5ab7d75b8765b1c3b0740cc3323bdce4d43f9aa --- /dev/null +++ b/src/esdac/gully-erosion-based-lucas/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "Gully-2018/GE-LUCAS v1/GE-LUCAS v1.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "erosion": { + "gully_erosion_observed": "bool(【SURVEY_ERO】 == '1')" + } + } +} diff --git a/src/esdac/gully-erosion-based-lucas/process.py b/src/esdac/gully-erosion-based-lucas/process.py new file mode 100644 index 0000000000000000000000000000000000000000..5c24abcc06c566765fedf531a0e8a08c9fdca09a --- /dev/null +++ b/src/esdac/gully-erosion-based-lucas/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_dbf + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .dbf files === +move_list = [] +for dbf in local_processed_dir.rglob("*.dbf"): + move_list = process_dbf(dbf, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/heavy-metals-topsoils/fuse_schema.json b/src/esdac/heavy-metals-topsoils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..119b66792d45c36f31b62e6c5528f8a90a1ceb97 --- /dev/null +++ b/src/esdac/heavy-metals-topsoils/fuse_schema.json @@ -0,0 +1,49 @@ +{ + "PC_EVIMODIS@tif": { + "CRS_STRING": "EPSG:3035", + "climate": { + "three_principal_components_from_MODIS_EVI": [ + "pcevi1", + "pcevi2", + "pcevi3" + ] + } + }, + "saga_twi@tif": { + "CRS_STRING": "EPSG:3035", + "hydraulic": { + "topographic_wetness_index": "saga_twi" + } + }, + "eudem1km@tif": { + "CRS_STRING": "EPSG:3035", + "topography_geology": { + "digital_elevation_model (m)": "eudem1km" + } + }, + "nlights@tif": { + "CRS_STRING": "EPSG:3035", + "land_site": { + "nighttime_light_intensity": "nlights" + } + }, + "solin@tif": { + "CRS_STRING": "EPSG:3035", + "land_site": { + "solar_radiation_input (W/mΒ²)": "solin" + } + }, + "foregsHMC5km@tif": { + "CRS_STRING": "EPSG:3035", + "trace_elements": { + "As_concentration_5km_res (mg/kg)": "As5km.asc/As5km", + "Cd_concentration_5km_res (mg/kg)": "Cd5km.asc/Cd5km", + "Cr_concentration_5km_res (mg/kg)": "Cr5km.asc/Cr5km", + "Cu_concentration_5km_res (mg/kg)": "Cu5km.asc/Cu5km", + "Hg_concentration_5km_res (mg/kg)": "Hg5km.asc/Hg5km", + "Ni_concentration_5km_res (mg/kg)": "Ni5km.asc/Ni5km", + "Pb_concentration_5km_res (mg/kg)": "Pb5km.asc/Pb5km", + "Zn_concentration_5km_res (mg/kg)": "Zn5km.asc/Zn5km" + } + } +} diff --git a/src/esdac/heavy-metals-topsoils/process.py b/src/esdac/heavy-metals-topsoils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..861a5aa568001c838f8cba9342e433072891bc0a --- /dev/null +++ b/src/esdac/heavy-metals-topsoils/process.py @@ -0,0 +1,23 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, unzip_nested, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Recursively extract nested ZIPs +unzip_nested(local_processed_dir) + +# === Step 4: Process all .asc files (convert to .npz, .png, .standardized.tif) === +move_list = [] +for asc_path in local_processed_dir.rglob("*.asc"): + move_list = process_tif(asc_path, move_list) + +# === Step 5: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/historical-reconstruction-soil-erosion-europe/fuse_skip.txt b/src/esdac/historical-reconstruction-soil-erosion-europe/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..d3d177020430adbcc27e6c18a2c717863bff9371 --- /dev/null +++ b/src/esdac/historical-reconstruction-soil-erosion-europe/fuse_skip.txt @@ -0,0 +1 @@ +Low res, historical reconstruction \ No newline at end of file diff --git a/src/esdac/historical-reconstruction-soil-erosion-europe/process.py b/src/esdac/historical-reconstruction-soil-erosion-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/historical-reconstruction-soil-erosion-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/land-degradation-debt/fuse_skip.txt b/src/esdac/land-degradation-debt/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/land-degradation-debt/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/land-degradation-debt/process.py b/src/esdac/land-degradation-debt/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/land-degradation-debt/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/land-degradation-europe/fuse_schema.json b/src/esdac/land-degradation-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b0d31a1d69efa33b4c002a485fc1c9ba275a41e5 --- /dev/null +++ b/src/esdac/land-degradation-europe/fuse_schema.json @@ -0,0 +1,200 @@ +{ + "Land-Degradation-Europe@tif": { + "land_degradation": { + "aridity_agricultural_lands": [ + "Aridity/Aridity_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "aridity_arable_lands": [ + "Aridity/Aridity_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "groundwater_decline_agricultural_lands": [ + "Groundwater decline/Groundwater decline_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "groundwater_decline_arable_lands": [ + "Groundwater decline/Groundwater decline_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_acidification_agricultural_lands": [ + "Soil acidification/Soil acidification_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_acidification_arable_lands": [ + "Soil acidification/Soil acidification_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_compaction_agricultural_lands": [ + "Soil compaction/Soil compaction_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_compaction_arable_lands": [ + "Soil compaction/Soil compaction_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_nutrient_imbalances_agricultural_lands": [ + "Soil nutrient imbalances/SNI_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_nutrient_imbalances_arable_lands": [ + "Soil nutrient imbalances/SNI_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_organic_carbon_loss_agricultural_lands": [ + "Soil organic carbon loss/SOC loss_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_organic_carbon_loss_arable_lands": [ + "Soil organic carbon loss/SOC loss_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_pollution_heavy_metals_agricultural_lands": [ + "Soil pollution with heavy metals/SPHM_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_pollution_heavy_metals_arable_lands": [ + "Soil pollution with heavy metals/SPHM_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_pollution_pesticides_agricultural_lands": [ + "Soil pollution with pesticides/SPP_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_pollution_pesticides_arable_lands": [ + "Soil pollution with pesticides/SPP_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_salinization_agricultural_lands": [ + "Soil salinization/Soil salinization_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "soil_salinization_arable_lands": [ + "Soil salinization/Soil salinization_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "vegetation_degradation_agricultural_lands": [ + "Vegetation degradation/Vegetation degradation_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "vegetation_degradation_arable_lands": [ + "Vegetation degradation/Vegetation degradation_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "water_erosion_agricultural_lands": [ + "Water erosion/Water erosion_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "water_erosion_arable_lands": [ + "Water erosion/Water erosion_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "wind_erosion_agricultural_lands": [ + "Wind erosion/Wind erosion_agricultural lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "wind_erosion_arable_lands": [ + "Wind erosion/Wind erosion_arable lands", + { + "0": "No degradation", + "1": "Degradation present", + "2": "Severe degradation" + } + ], + "land_multi_degradation_index_agricultural_lands": "LMI/LMI_agricultural lands", + "land_multi_degradation_index_arable_lands": "LMI/LMI_arable lands" + } + } +} diff --git a/src/esdac/land-degradation-europe/process.py b/src/esdac/land-degradation-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/land-degradation-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/land-degradation-global-arable-lands/fuse_skip.txt b/src/esdac/land-degradation-global-arable-lands/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/land-degradation-global-arable-lands/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/land-degradation-global-arable-lands/process.py b/src/esdac/land-degradation-global-arable-lands/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/land-degradation-global-arable-lands/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/land-suitability-temperate-europe/fuse_schema.json b/src/esdac/land-suitability-temperate-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0605db6f9ff326f56153a1bda4fbafe16f3bee59 --- /dev/null +++ b/src/esdac/land-suitability-temperate-europe/fuse_schema.json @@ -0,0 +1,425 @@ +{ + "Land_suitability_maps@tif": { + "function_suitability": { + "arable_suitability_score": "AR", + "hemp_suitability_score": "CN", + "apricot_suitability_score": "CS", + "potato_suitability_score": "CT", + "cherry_suitability_score": "CV", + "meadow_suitability_score": "FN", + "sunflower_suitability_score": "FS", + "wheat_suitability_score": "GR", + "linseed4tow_suitability_score": "IF", + "linseed4oil_suitability_score": "IU", + "vegetables_suitability_score": "LG", + "alfalfalucerne_suitability_score": "LU", + "peabean_suitability_score": "MF", + "apple_suitability_score": "MR", + "barley_suitability_score": "OR", + "corn_suitability_score": "PB", + "peach_suitability_score": "PC", + "plum_suitability_score": "PN", + "pear_suitability_score": "PR", + "pasture_suitability_score": "PS", + "sugarbeet_suitability_score": "SF", + "soy_suitability_score": "SO", + "clover_suitability_score": "TR", + "grapes_suitability_score": "VM" + } + }, + "Limitation_maps@tif": { + "function_suitability": { + "clover_gleization_limitation_index": "TR_gleization", + "grapes_texture_H1_limitation_index": "VM_texture_H1", + "meadow_precipitation_limitation_index": "FN_precipitation", + "alfalfalucerne_gleization_limitation_index": "LU_gleization", + "potato_CaCO3_limitation_index": "CT_CaCO3", + "linseed4oil_pollution_limitation_index": "IU_pollution", + "pear_moisture_excess_limitation_index": "PR_moisture_excess", + "meadow_sal_alc_limitation_index": "FN_sal_alc", + "hemp_moisture_excess_limitation_index": "CN_moisture_excess", + "sugarbeet_temperature_limitation_index": "SF_temperature", + "wheat_water_table_limitation_index": "GR_water_table", + "linseed4tow_gleization_limitation_index": "IF_gleization", + "pasture_slope_limitation_index": "PS_slope", + "linseed4tow_precipitation_limitation_index": "IF_precipitation", + "peach_texture_H1_limitation_index": "PC_texture_H1", + "plum_porosityRH_limitation_index": "PN_porosityRH", + "apricot_gleization_limitation_index": "CS_gleization", + "barley_moisture_excess_limitation_index": "OR_moisture_excess", + "plum_pH_limitation_index": "PN_pH", + "alfalfalucerne_moisture_excess_limitation_index": "LU_moisture_excess", + "sugarbeet_pollution_limitation_index": "SF_pollution", + "clover_pollution_limitation_index": "TR_pollution", + "meadow_CaCO3_limitation_index": "FN_CaCO3", + "peach_pollution_limitation_index": "PC_pollution", + "cherry_temperature_limitation_index": "CV_temperature", + "vegetables_gleization_limitation_index": "LG_gleization", + "linseed4oil_humus_limitation_index": "IU_humus", + "pear_landslides_limitation_index": "PR_landslides", + "apricot_CaCO3_limitation_index": "CS_CaCO3", + "soy_landslides_limitation_index": "SO_landslides", + "sugarbeet_moisture_excess_limitation_index": "SF_moisture_excess", + "barley_CaCO3_limitation_index": "OR_CaCO3", + "barley_pH_limitation_index": "OR_pH", + "alfalfalucerne_pollution_limitation_index": "LU_pollution", + "apple_texture_H1_limitation_index": "MR_texture_H1", + "hemp_precipitation_limitation_index": "CN_precipitation", + "peabean_landslides_limitation_index": "MF_landslides", + "apricot_stagnogleization_limitation_index": "CS_stagnogleization", + "apple_slope_limitation_index": "MR_slope", + "grapes_sal_alc_limitation_index": "VM_sal_alc", + "soy_precipitation_limitation_index": "SO_precipitation", + "linseed4oil_texture_H1_limitation_index": "IU_texture_H1", + "peach_sal_alc_limitation_index": "PC_sal_alc", + "clover_stagnogleization_limitation_index": "TR_stagnogleization", + "potato_landslides_limitation_index": "CT_landslides", + "corn_gleization_limitation_index": "PB_gleization", + "pasture_water_table_limitation_index": "PS_water_table", + "barley_edaphic_volume_limitation_index": "OR_edaphic_volume", + "potato_precipitation_limitation_index": "CT_precipitation", + "cherry_gleization_limitation_index": "CV_gleization", + "sunflower_texture_H1_limitation_index": "FS_texture_H1", + "hemp_stagnogleization_limitation_index": "CN_stagnogleization", + "grapes_pollution_limitation_index": "VM_pollution", + "meadow_pollution_limitation_index": "FN_pollution", + "linseed4tow_water_table_limitation_index": "IF_water_table", + "vegetables_edaphic_volume_limitation_index": "LG_edaphic_volume", + "alfalfalucerne_floods_frequency_limitation_index": "LU_floods_frequency", + "barley_landslides_limitation_index": "OR_landslides", + "linseed4tow_sal_alc_limitation_index": "IF_sal_alc", + "meadow_temperature_limitation_index": "FN_temperature", + "grapes_edaphic_volume_limitation_index": "VM_edaphic_volume", + "wheat_edaphic_volume_limitation_index": "GR_edaphic_volume", + "clover_pH_limitation_index": "TR_pH", + "sugarbeet_texture_H1_limitation_index": "SF_texture_H1", + "pear_edaphic_volume_limitation_index": "PR_edaphic_volume", + "apricot_water_table_limitation_index": "CS_water_table", + "barley_stagnogleization_limitation_index": "OR_stagnogleization", + "corn_precipitation_limitation_index": "PB_precipitation", + "corn_water_table_limitation_index": "PB_water_table", + "pasture_pollution_limitation_index": "PS_pollution", + "corn_CaCO3_limitation_index": "PB_CaCO3", + "meadow_texture_H1_limitation_index": "FN_texture_H1", + "soy_floods_frequency_limitation_index": "SO_floods_frequency", + "apple_humus_limitation_index": "MR_humus", + "vegetables_temperature_limitation_index": "LG_temperature", + "apricot_sal_alc_limitation_index": "CS_sal_alc", + "linseed4oil_slope_limitation_index": "IU_slope", + "alfalfalucerne_temperature_limitation_index": "LU_temperature", + "peabean_floods_frequency_limitation_index": "MF_floods_frequency", + "pasture_sal_alc_limitation_index": "PS_sal_alc", + "clover_water_table_limitation_index": "TR_water_table", + "meadow_edaphic_volume_limitation_index": "FN_edaphic_volume", + "vegetables_floods_frequency_limitation_index": "LG_floods_frequency", + "wheat_porosityRH_limitation_index": "GR_porosityRH", + "apple_moisture_excess_limitation_index": "MR_moisture_excess", + "barley_water_table_limitation_index": "OR_water_table", + "soy_edaphic_volume_limitation_index": "SO_edaphic_volume", + "hemp_pollution_limitation_index": "CN_pollution", + "clover_edaphic_volume_limitation_index": "TR_edaphic_volume", + "vegetables_sal_alc_limitation_index": "LG_sal_alc", + "pasture_humus_limitation_index": "PS_humus", + "hemp_gleization_limitation_index": "CN_gleization", + "pasture_porosityRH_limitation_index": "PS_porosityRH", + "pear_precipitation_limitation_index": "PR_precipitation", + "plum_precipitation_limitation_index": "PN_precipitation", + "pasture_landslides_limitation_index": "PS_landslides", + "plum_CaCO3_limitation_index": "PN_CaCO3", + "vegetables_texture_H1_limitation_index": "LG_texture_H1", + "apricot_texture_H1_limitation_index": "CS_texture_H1", + "wheat_CaCO3_limitation_index": "GR_CaCO3", + "pear_humus_limitation_index": "PR_humus", + "apple_precipitation_limitation_index": "MR_precipitation", + "linseed4tow_texture_H1_limitation_index": "IF_texture_H1", + "peabean_edaphic_volume_limitation_index": "MF_edaphic_volume", + "linseed4tow_CaCO3_limitation_index": "IF_CaCO3", + "cherry_moisture_excess_limitation_index": "CV_moisture_excess", + "cherry_CaCO3_limitation_index": "CV_CaCO3", + "wheat_landslides_limitation_index": "GR_landslides", + "sunflower_precipitation_limitation_index": "FS_precipitation", + "peach_gleization_limitation_index": "PC_gleization", + "peabean_stagnogleization_limitation_index": "MF_stagnogleization", + "barley_temperature_limitation_index": "OR_temperature", + "cherry_sal_alc_limitation_index": "CV_sal_alc", + "sunflower_edaphic_volume_limitation_index": "FS_edaphic_volume", + "potato_pollution_limitation_index": "CT_pollution", + "soy_CaCO3_limitation_index": "SO_CaCO3", + "potato_edaphic_volume_limitation_index": "CT_edaphic_volume", + "alfalfalucerne_texture_H1_limitation_index": "LU_texture_H1", + "clover_texture_H1_limitation_index": "TR_texture_H1", + "grapes_gleization_limitation_index": "VM_gleization", + "pear_pollution_limitation_index": "PR_pollution", + "clover_temperature_limitation_index": "TR_temperature", + "vegetables_stagnogleization_limitation_index": "LG_stagnogleization", + "sunflower_slope_limitation_index": "FS_slope", + "sunflower_sal_alc_limitation_index": "FS_sal_alc", + "clover_slope_limitation_index": "TR_slope", + "alfalfalucerne_water_table_limitation_index": "LU_water_table", + "soy_stagnogleization_limitation_index": "SO_stagnogleization", + "vegetables_water_table_limitation_index": "LG_water_table", + "cherry_texture_H1_limitation_index": "CV_texture_H1", + "sunflower_gleization_limitation_index": "FS_gleization", + "plum_moisture_excess_limitation_index": "PN_moisture_excess", + "barley_floods_frequency_limitation_index": "OR_floods_frequency", + "vegetables_humus_limitation_index": "LG_humus", + "linseed4tow_edaphic_volume_limitation_index": "IF_edaphic_volume", + "peach_CaCO3_limitation_index": "PC_CaCO3", + "wheat_pollution_limitation_index": "GR_pollution", + "corn_texture_H1_limitation_index": "PB_texture_H1", + "apricot_temperature_limitation_index": "CS_temperature", + "hemp_slope_limitation_index": "CN_slope", + "corn_temperature_limitation_index": "PB_temperature", + "barley_porosityRH_limitation_index": "OR_porosityRH", + "hemp_floods_frequency_limitation_index": "CN_floods_frequency", + "apple_gleization_limitation_index": "MR_gleization", + "vegetables_precipitation_limitation_index": "LG_precipitation", + "grapes_slope_limitation_index": "VM_slope", + "linseed4oil_gleization_limitation_index": "IU_gleization", + "meadow_water_table_limitation_index": "FN_water_table", + "linseed4tow_temperature_limitation_index": "IF_temperature", + "apricot_precipitation_limitation_index": "CS_precipitation", + "pear_sal_alc_limitation_index": "PR_sal_alc", + "alfalfalucerne_stagnogleization_limitation_index": "LU_stagnogleization", + "peabean_moisture_excess_limitation_index": "MF_moisture_excess", + "corn_edaphic_volume_limitation_index": "PB_edaphic_volume", + "sunflower_pollution_limitation_index": "FS_pollution", + "pasture_temperature_limitation_index": "PS_temperature", + "peach_precipitation_limitation_index": "PC_precipitation", + "peabean_CaCO3_limitation_index": "MF_CaCO3", + "clover_floods_frequency_limitation_index": "TR_floods_frequency", + "plum_sal_alc_limitation_index": "PN_sal_alc", + "potato_porosityRH_limitation_index": "CT_porosityRH", + "meadow_gleization_limitation_index": "FN_gleization", + "grapes_humus_limitation_index": "VM_humus", + "peabean_porosityRH_limitation_index": "MF_porosityRH", + "corn_pollution_limitation_index": "PB_pollution", + "vegetables_slope_limitation_index": "LG_slope", + "sunflower_moisture_excess_limitation_index": "FS_moisture_excess", + "sugarbeet_gleization_limitation_index": "SF_gleization", + "plum_pollution_limitation_index": "PN_pollution", + "hemp_humus_limitation_index": "CN_humus", + "pear_porosityRH_limitation_index": "PR_porosityRH", + "clover_humus_limitation_index": "TR_humus", + "apricot_floods_frequency_limitation_index": "CS_floods_frequency", + "soy_porosityRH_limitation_index": "SO_porosityRH", + "alfalfalucerne_CaCO3_limitation_index": "LU_CaCO3", + "pasture_pH_limitation_index": "PS_pH", + "hemp_texture_H1_limitation_index": "CN_texture_H1", + "sunflower_humus_limitation_index": "FS_humus", + "cherry_water_table_limitation_index": "CV_water_table", + "plum_landslides_limitation_index": "PN_landslides", + "pasture_precipitation_limitation_index": "PS_precipitation", + "corn_moisture_excess_limitation_index": "PB_moisture_excess", + "apricot_pollution_limitation_index": "CS_pollution", + "peach_pH_limitation_index": "PC_pH", + "apple_pollution_limitation_index": "MR_pollution", + "corn_sal_alc_limitation_index": "PB_sal_alc", + "sugarbeet_water_table_limitation_index": "SF_water_table", + "pear_slope_limitation_index": "PR_slope", + "wheat_temperature_limitation_index": "GR_temperature", + "pear_pH_limitation_index": "PR_pH", + "sugarbeet_CaCO3_limitation_index": "SF_CaCO3", + "corn_pH_limitation_index": "PB_pH", + "linseed4oil_edaphic_volume_limitation_index": "IU_edaphic_volume", + "hemp_sal_alc_limitation_index": "CN_sal_alc", + "linseed4oil_porosityRH_limitation_index": "IU_porosityRH", + "hemp_pH_limitation_index": "CN_pH", + "linseed4tow_slope_limitation_index": "IF_slope", + "cherry_slope_limitation_index": "CV_slope", + "sugarbeet_stagnogleization_limitation_index": "SF_stagnogleization", + "barley_gleization_limitation_index": "OR_gleization", + "cherry_pollution_limitation_index": "CV_pollution", + "apple_porosityRH_limitation_index": "MR_porosityRH", + "soy_slope_limitation_index": "SO_slope", + "soy_temperature_limitation_index": "SO_temperature", + "alfalfalucerne_humus_limitation_index": "LU_humus", + "sunflower_CaCO3_limitation_index": "FS_CaCO3", + "linseed4oil_moisture_excess_limitation_index": "IU_moisture_excess", + "cherry_precipitation_limitation_index": "CV_precipitation", + "sunflower_porosityRH_limitation_index": "FS_porosityRH", + "linseed4tow_floods_frequency_limitation_index": "IF_floods_frequency", + "sugarbeet_humus_limitation_index": "SF_humus", + "meadow_moisture_excess_limitation_index": "FN_moisture_excess", + "linseed4tow_pollution_limitation_index": "IF_pollution", + "plum_slope_limitation_index": "PN_slope", + "meadow_pH_limitation_index": "FN_pH", + "wheat_slope_limitation_index": "GR_slope", + "corn_stagnogleization_limitation_index": "PB_stagnogleization", + "plum_temperature_limitation_index": "PN_temperature", + "apple_water_table_limitation_index": "MR_water_table", + "apple_sal_alc_limitation_index": "MR_sal_alc", + "plum_edaphic_volume_limitation_index": "PN_edaphic_volume", + "pear_temperature_limitation_index": "PR_temperature", + "peach_porosityRH_limitation_index": "PC_porosityRH", + "peabean_pH_limitation_index": "MF_pH", + "wheat_floods_frequency_limitation_index": "GR_floods_frequency", + "grapes_CaCO3_limitation_index": "VM_CaCO3", + "linseed4tow_moisture_excess_limitation_index": "IF_moisture_excess", + "sunflower_floods_frequency_limitation_index": "FS_floods_frequency", + "grapes_precipitation_limitation_index": "VM_precipitation", + "grapes_stagnogleization_limitation_index": "VM_stagnogleization", + "peabean_humus_limitation_index": "MF_humus", + "potato_sal_alc_limitation_index": "CT_sal_alc", + "grapes_porosityRH_limitation_index": "VM_porosityRH", + "cherry_stagnogleization_limitation_index": "CV_stagnogleization", + "clover_CaCO3_limitation_index": "TR_CaCO3", + "soy_moisture_excess_limitation_index": "SO_moisture_excess", + "hemp_landslides_limitation_index": "CN_landslides", + "peach_slope_limitation_index": "PC_slope", + "pasture_floods_frequency_limitation_index": "PS_floods_frequency", + "hemp_edaphic_volume_limitation_index": "CN_edaphic_volume", + "peabean_temperature_limitation_index": "MF_temperature", + "hemp_CaCO3_limitation_index": "CN_CaCO3", + "potato_moisture_excess_limitation_index": "CT_moisture_excess", + "plum_texture_H1_limitation_index": "PN_texture_H1", + "wheat_moisture_excess_limitation_index": "GR_moisture_excess", + "peabean_sal_alc_limitation_index": "MF_sal_alc", + "pear_stagnogleization_limitation_index": "PR_stagnogleization", + "peach_humus_limitation_index": "PC_humus", + "vegetables_CaCO3_limitation_index": "LG_CaCO3", + "meadow_floods_frequency_limitation_index": "FN_floods_frequency", + "linseed4tow_landslides_limitation_index": "IF_landslides", + "wheat_texture_H1_limitation_index": "GR_texture_H1", + "potato_stagnogleization_limitation_index": "CT_stagnogleization", + "linseed4oil_stagnogleization_limitation_index": "IU_stagnogleization", + "alfalfalucerne_landslides_limitation_index": "LU_landslides", + "clover_landslides_limitation_index": "TR_landslides", + "potato_water_table_limitation_index": "CT_water_table", + "sugarbeet_pH_limitation_index": "SF_pH", + "vegetables_landslides_limitation_index": "LG_landslides", + "vegetables_moisture_excess_limitation_index": "LG_moisture_excess", + "plum_floods_frequency_limitation_index": "PN_floods_frequency", + "peabean_slope_limitation_index": "MF_slope", + "pasture_texture_H1_limitation_index": "PS_texture_H1", + "apple_stagnogleization_limitation_index": "MR_stagnogleization", + "wheat_pH_limitation_index": "GR_pH", + "apricot_landslides_limitation_index": "CS_landslides", + "linseed4oil_sal_alc_limitation_index": "IU_sal_alc", + "sugarbeet_porosityRH_limitation_index": "SF_porosityRH", + "pasture_moisture_excess_limitation_index": "PS_moisture_excess", + "wheat_humus_limitation_index": "GR_humus", + "peach_floods_frequency_limitation_index": "PC_floods_frequency", + "pear_CaCO3_limitation_index": "PR_CaCO3", + "linseed4oil_temperature_limitation_index": "IU_temperature", + "peabean_gleization_limitation_index": "MF_gleization", + "sugarbeet_slope_limitation_index": "SF_slope", + "sunflower_water_table_limitation_index": "FS_water_table", + "soy_gleization_limitation_index": "SO_gleization", + "pear_gleization_limitation_index": "PR_gleization", + "soy_sal_alc_limitation_index": "SO_sal_alc", + "plum_humus_limitation_index": "PN_humus", + "alfalfalucerne_slope_limitation_index": "LU_slope", + "cherry_landslides_limitation_index": "CV_landslides", + "hemp_temperature_limitation_index": "CN_temperature", + "soy_humus_limitation_index": "SO_humus", + "peach_temperature_limitation_index": "PC_temperature", + "apple_pH_limitation_index": "MR_pH", + "cherry_humus_limitation_index": "CV_humus", + "potato_gleization_limitation_index": "CT_gleization", + "corn_landslides_limitation_index": "PB_landslides", + "linseed4tow_humus_limitation_index": "IF_humus", + "grapes_water_table_limitation_index": "VM_water_table", + "meadow_porosityRH_limitation_index": "FN_porosityRH", + "peach_edaphic_volume_limitation_index": "PC_edaphic_volume", + "meadow_landslides_limitation_index": "FN_landslides", + "grapes_moisture_excess_limitation_index": "VM_moisture_excess", + "corn_porosityRH_limitation_index": "PB_porosityRH", + "grapes_temperature_limitation_index": "VM_temperature", + "meadow_slope_limitation_index": "FN_slope", + "peach_water_table_limitation_index": "PC_water_table", + "cherry_porosityRH_limitation_index": "CV_porosityRH", + "peach_moisture_excess_limitation_index": "PC_moisture_excess", + "hemp_water_table_limitation_index": "CN_water_table", + "wheat_sal_alc_limitation_index": "GR_sal_alc", + "clover_moisture_excess_limitation_index": "TR_moisture_excess", + "potato_slope_limitation_index": "CT_slope", + "sunflower_temperature_limitation_index": "FS_temperature", + "potato_pH_limitation_index": "CT_pH", + "peach_stagnogleization_limitation_index": "PC_stagnogleization", + "sugarbeet_sal_alc_limitation_index": "SF_sal_alc", + "apple_edaphic_volume_limitation_index": "MR_edaphic_volume", + "peabean_precipitation_limitation_index": "MF_precipitation", + "alfalfalucerne_sal_alc_limitation_index": "LU_sal_alc", + "linseed4tow_pH_limitation_index": "IF_pH", + "barley_sal_alc_limitation_index": "OR_sal_alc", + "linseed4oil_precipitation_limitation_index": "IU_precipitation", + "vegetables_pH_limitation_index": "LG_pH", + "peabean_pollution_limitation_index": "MF_pollution", + "sugarbeet_landslides_limitation_index": "SF_landslides", + "linseed4oil_water_table_limitation_index": "IU_water_table", + "pasture_CaCO3_limitation_index": "PS_CaCO3", + "sugarbeet_edaphic_volume_limitation_index": "SF_edaphic_volume", + "barley_texture_H1_limitation_index": "OR_texture_H1", + "plum_stagnogleization_limitation_index": "PN_stagnogleization", + "alfalfalucerne_edaphic_volume_limitation_index": "LU_edaphic_volume", + "plum_gleization_limitation_index": "PN_gleization", + "apricot_porosityRH_limitation_index": "CS_porosityRH", + "vegetables_porosityRH_limitation_index": "LG_porosityRH", + "potato_temperature_limitation_index": "CT_temperature", + "linseed4oil_pH_limitation_index": "IU_pH", + "sugarbeet_precipitation_limitation_index": "SF_precipitation", + "apple_floods_frequency_limitation_index": "MR_floods_frequency", + "alfalfalucerne_precipitation_limitation_index": "LU_precipitation", + "meadow_stagnogleization_limitation_index": "FN_stagnogleization", + "apricot_slope_limitation_index": "CS_slope", + "clover_porosityRH_limitation_index": "TR_porosityRH", + "pear_floods_frequency_limitation_index": "PR_floods_frequency", + "barley_slope_limitation_index": "OR_slope", + "barley_pollution_limitation_index": "OR_pollution", + "alfalfalucerne_porosityRH_limitation_index": "LU_porosityRH", + "clover_sal_alc_limitation_index": "TR_sal_alc", + "corn_humus_limitation_index": "PB_humus", + "cherry_pH_limitation_index": "CV_pH", + "alfalfalucerne_pH_limitation_index": "LU_pH", + "apple_CaCO3_limitation_index": "MR_CaCO3", + "linseed4oil_floods_frequency_limitation_index": "IU_floods_frequency", + "potato_floods_frequency_limitation_index": "CT_floods_frequency", + "linseed4tow_porosityRH_limitation_index": "IF_porosityRH", + "wheat_precipitation_limitation_index": "GR_precipitation", + "corn_slope_limitation_index": "PB_slope", + "cherry_floods_frequency_limitation_index": "CV_floods_frequency", + "peabean_water_table_limitation_index": "MF_water_table", + "grapes_pH_limitation_index": "VM_pH", + "apricot_edaphic_volume_limitation_index": "CS_edaphic_volume", + "linseed4oil_CaCO3_limitation_index": "IU_CaCO3", + "hemp_porosityRH_limitation_index": "CN_porosityRH", + "pasture_stagnogleization_limitation_index": "PS_stagnogleization", + "barley_humus_limitation_index": "OR_humus", + "apricot_moisture_excess_limitation_index": "CS_moisture_excess", + "apricot_humus_limitation_index": "CS_humus", + "pasture_gleization_limitation_index": "PS_gleization", + "grapes_landslides_limitation_index": "VM_landslides", + "wheat_stagnogleization_limitation_index": "GR_stagnogleization", + "grapes_floods_frequency_limitation_index": "VM_floods_frequency", + "peach_landslides_limitation_index": "PC_landslides", + "sunflower_stagnogleization_limitation_index": "FS_stagnogleization", + "wheat_gleization_limitation_index": "GR_gleization", + "potato_texture_H1_limitation_index": "CT_texture_H1", + "pear_water_table_limitation_index": "PR_water_table", + "sunflower_pH_limitation_index": "FS_pH", + "cherry_edaphic_volume_limitation_index": "CV_edaphic_volume", + "linseed4tow_stagnogleization_limitation_index": "IF_stagnogleization", + "plum_water_table_limitation_index": "PN_water_table", + "apple_temperature_limitation_index": "MR_temperature", + "clover_precipitation_limitation_index": "TR_precipitation", + "sunflower_landslides_limitation_index": "FS_landslides", + "pasture_edaphic_volume_limitation_index": "PS_edaphic_volume", + "potato_humus_limitation_index": "CT_humus", + "corn_floods_frequency_limitation_index": "PB_floods_frequency", + "soy_pH_limitation_index": "SO_pH", + "soy_texture_H1_limitation_index": "SO_texture_H1", + "vegetables_pollution_limitation_index": "LG_pollution", + "apricot_pH_limitation_index": "CS_pH", + "soy_water_table_limitation_index": "SO_water_table", + "sugarbeet_floods_frequency_limitation_index": "SF_floods_frequency", + "pear_texture_H1_limitation_index": "PR_texture_H1", + "barley_precipitation_limitation_index": "OR_precipitation", + "soy_pollution_limitation_index": "SO_pollution", + "linseed4oil_landslides_limitation_index": "IU_landslides", + "apple_landslides_limitation_index": "MR_landslides", + "meadow_humus_limitation_index": "FN_humus", + "peabean_texture_H1_limitation_index": "MF_texture_H1" + } + } +} diff --git a/src/esdac/land-suitability-temperate-europe/gen_fuse.py b/src/esdac/land-suitability-temperate-europe/gen_fuse.py new file mode 100644 index 0000000000000000000000000000000000000000..49d5f75e196ecc93d744266b5e6fc0e7dabc3d8d --- /dev/null +++ b/src/esdac/land-suitability-temperate-europe/gen_fuse.py @@ -0,0 +1,58 @@ +import json +from pathlib import Path + +crop_codes = { + "AR": "arable", + "CN": "hemp", + "CS": "apricot", + "CT": "potato", + "CV": "cherry", + "FN": "meadow", + "FS": "sunflower", + "GR": "wheat", + "IF": "linseed4tow", + "IU": "linseed4oil", + "LG": "vegetables", + "LU": "alfalfalucerne", + "MF": "peabean", + "MR": "apple", + "OR": "barley", + "PB": "corn", + "PC": "peach", + "PN": "plum", + "PR": "pear", + "PS": "pasture", + "SF": "sugarbeet", + "SO": "soy", + "TR": "clover", + "VM": "grapes" +} + +data_root = Path("datasets/esdac/land-suitability-temperate-europe/processed") +suit_dir = data_root / "Land_suitability_maps" +limit_dir = data_root / "Limitation_maps" + +out_dict = { + "Land_suitability_maps@tif": {"function_suitability": {}}, + "Limitation_maps@tif": {"function_suitability": {}} +} + +# Land suitability maps +suit = out_dict["Land_suitability_maps@tif"]["function_suitability"] +for code, name in crop_codes.items(): + tif_path = suit_dir / f"{code}.standardized.tif" + if tif_path.exists(): + suit[f"{name}_suitability_score"] = code + +# Limitation maps +limit = out_dict["Limitation_maps@tif"]["function_suitability"] +for tif in limit_dir.glob("*.tif"): + stem = tif.stem.replace(".standardized", "") # e.g. CN_CaCO3.standardized + code = stem[:2] + if code in crop_codes: + name = crop_codes[code] + suffix = stem[3:] + limit[f"{name}_{suffix}_limitation_index"] = stem + +with open("src/esdac/land-suitability-temperate-europe/fuse_schema.json", "w") as f: + json.dump(out_dict, f, indent=2) diff --git a/src/esdac/land-suitability-temperate-europe/process.py b/src/esdac/land-suitability-temperate-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/land-suitability-temperate-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/ls-factor-slope-length-and-steepness-factor-eu/fuse_schema.json b/src/esdac/ls-factor-slope-length-and-steepness-factor-eu/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..eaab783ffb4f788d4cce8ee8a8df749c5b68158c --- /dev/null +++ b/src/esdac/ls-factor-slope-length-and-steepness-factor-eu/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "LS100m@tif": { + "erosion": { + "ls_factor_slope_length_and_steepness": "EU_LS_Mosaic_100m" + } + } +} diff --git a/src/esdac/ls-factor-slope-length-and-steepness-factor-eu/process.py b/src/esdac/ls-factor-slope-length-and-steepness-factor-eu/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/ls-factor-slope-length-and-steepness-factor-eu/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/lucas-2009-topsoil-data/fuse_schema.json b/src/esdac/lucas-2009-topsoil-data/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..d41e7e71440104812d31f400ac8149c0242419a6 --- /dev/null +++ b/src/esdac/lucas-2009-topsoil-data/fuse_schema.json @@ -0,0 +1,117 @@ +{ + "LUCAS_21681_points/NoSensitive.csv": { + "ENTRY_KEY": "'_'.join(['lucas2009', as_int_id(【POINT_ID】), as_int_id(【sample_ID】)])", + "LAT_LONG": "(as_float(【LAT】), as_float(【LONG】))", + "COUNTRY_CODE": "【nuts0】", + "SAMPLE_DATE": "datetime.strptime(【SURV_DATE】, '%d%b%Y:%H:%M:%S').strftime('%d-%m-%Y') if 【SURV_DATE】 else 'XX-XX-2009'", + "SAMPLE_DEPTH_RANGE_CM": "[0, 20]", + "texture": { + "clay_percentage (%)": "as_float(【clay】)", + "silt_percentage (%)": "as_float(【silt】)", + "sand_percentage (%)": "as_float(【sand】)", + "coarse_percentage (%)": "as_float(【coarse】)" + }, + "chemical": { + "pH_in_H2O": "as_float(【pH_in_H2O】)", + "pH_in_CaCl2": "as_float(【pH_in_CaCl】)" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC】)", + "CaCO3_content (g/kg)": "as_float(【CaCO3】)" + }, + "fertility": { + "N_extractable (g/kg)": "as_float(【N】)", + "K_extractable (mg/kg)": "as_float(【K】)", + "P_extractable (mg/kg)": "as_float(【P_x】)", + "P_total_content (mg/kg)": "as_float(【PTotal】)", + "cation_exchange_capacity (cmol(+)/kg)": "as_float(【CEC】)" + }, + "soil_type": { + "WRB_soil_group": "【WRB_LV1】" + }, + "management": { + "water_management_measure": "resolve_code(【WM_WATER_M】, codebook='lucas_mix', top_key='water_management_measure', ignore_error_codes=['0'])", + "water_management_irrigation_source": "resolve_code(【WM_SRC_IRR】, codebook='lucas_mix', top_key='water_management_irrigation_source', ignore_error_codes=['0', '6'])", + "water_management_irrigation_type": "resolve_code(【WM_TYP_IRR】, codebook='lucas_mix', top_key='water_management_irrigation_type', ignore_error_codes=['0', '6'])", + "water_management_delivery": "resolve_code(【WM_DELIVER】, codebook='lucas_mix', top_key='water_management_delivery', ignore_error_codes=['0', '4'])" + }, + "land_site": { + "land_cover_primary": "resolve_code(【LC1】, codebook='lucas_lc', ignore_error_codes=['8', 'MISSING'])", + "land_cover_secondary": "resolve_code(【LC2】, codebook='lucas_lc', ignore_error_codes=['8', 'MISSING'])", + "land_use_primary": "resolve_code(re.sub('^U', '', str(【LU1】)), codebook='lucas_lu', ignore_error_codes=['8', 'MISSING'])", + "land_use_secondary": "resolve_code(re.sub('^U', '', str(【LU2】)), codebook='lucas_lu', ignore_error_codes=['8', 'MISSING'])", + "plot_area_size (ha)": "resolve_code(【AREA_SIZE】, codebook='lucas_mix', top_key='plot_area_size (ha)', ignore_error_codes=['0'])", + "tree_height_maturity (m)": "resolve_code(【TREES_HEIG】, codebook='lucas_mix', top_key='tree_height_maturity (m)', ignore_error_codes=['0'])", + "sign_of_grazing": "resolve_code(【LAND_MNGT】, codebook='lucas_mix', top_key='sign_of_grazing', ignore_error_codes=['3', '0'])", + "sign_of_ploughing": "resolve_code(【SOIL_PLOUG】, codebook='lucas_mix', top_key='sign_of_ploughing', ignore_error_codes=['0'])", + "cover_of_residual_crop (%)": "resolve_code(【SOIL_CROP】, codebook='lucas_mix', top_key='cover_of_residual_crop (%)', ignore_error_codes=['0'])", + "cover_of_stones (%)": "resolve_code(【SOIL_STONE】, codebook='lucas_mix', top_key='cover_of_stones (%)', ignore_error_codes=['0'])" + }, + "climate": { + "annual_precipitation (mm)": "as_float(【AnnualPrec】)" + }, + "topography_geology": { + "elevation (m)": "as_float(【Elevation(m)】)", + "slope (deg)": "as_float(【slope (Degrees)】)", + "parent_material_primary": "【ParMat1】", + "parent_material_secondary": "【ParMat2】" + }, + "ASSETS": { + "psd_spectrum": "f\"psd/{'_'.join(['lucas2009', as_int_id(【POINT_ID】), as_int_id(【sample_ID】)])}.npz\"" + } + }, + "LUCAS_Romania_Bulgaria_2012/Bulgaria.csv": { + "ENTRY_KEY": "'_'.join(['lucas2012BG', as_int_id(【POINT_ID】), as_int_id(【SoilID】)])", + "LAT_LONG": "(as_float(【GPS_Y_LAT】), as_float(【GPS_X_LONG】))", + "COUNTRY_CODE": "'BG'", + "SAMPLE_DATE": "'XX-XX-2012'", + "SAMPLE_DEPTH_RANGE_CM": "[0, 20]", + "texture": { + "clay_percentage (%)": "as_float(【clay】)", + "silt_percentage (%)": "as_float(【silt】)", + "sand_percentage (%)": "as_float(【sand】)", + "coarse_percentage (%)": "as_float(【coarse】)" + }, + "chemical": { + "pH_in_H2O": "as_float(【pHinH2O】)", + "pH_in_CaCl2": "as_float(【pHinCaCl2】)" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC】)", + "CaCO3_content (g/kg)": "as_float(【CaCO3】)" + }, + "fertility": { + "N_extractable (g/kg)": "as_float(【N】)", + "K_extractable (mg/kg)": "as_float(【K】)", + "P_extractable (mg/kg)": "as_float(【P】)", + "cation_exchange_capacity (cmol(+)/kg)": "as_float(【CEC】)" + } + }, + "LUCAS_Romania_Bulgaria_2012/Romania.csv": { + "ENTRY_KEY": "'_'.join(['lucas2012RO', as_int_id(【POINTID】), as_int_id(【SoilID】)])", + "LAT_LONG": "(as_float(【GPSYLAT】), as_float(【GPSXLONG】))", + "COUNTRY_CODE": "'RO'", + "SAMPLE_DATE": "'XX-XX-2012'", + "SAMPLE_DEPTH_RANGE_CM": "[0, 20]", + "texture": { + "clay_percentage (%)": "as_float(【clay】)", + "silt_percentage (%)": "as_float(【silt】)", + "sand_percentage (%)": "as_float(【sand】)", + "coarse_percentage (%)": "as_float(【coarse】)" + }, + "chemical": { + "pH_in_H2O": "as_float(【pHinH2O】)", + "pH_in_CaCl2": "as_float(【pHinCaCl2】)" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC】)", + "CaCO3_content (g/kg)": "as_float(【CaCO3】)" + }, + "fertility": { + "N_extractable (g/kg)": "as_float(【N】)", + "K_extractable (mg/kg)": "as_float(【K】)", + "P_extractable (mg/kg)": "as_float(【P】)", + "cation_exchange_capacity (cmol(+)/kg)": "as_float(【CEC】)" + } + } +} diff --git a/src/esdac/lucas-2009-topsoil-data/process.py b/src/esdac/lucas-2009-topsoil-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..45187440c31ddc833a87988c6be1a58a196bc965 --- /dev/null +++ b/src/esdac/lucas-2009-topsoil-data/process.py @@ -0,0 +1,86 @@ +import re +from pathlib import Path + +import numpy as np +import pandas as pd + +from src.process.io import prepare_dirs, unzip, copy_to_local, upload +from src.process.processor import process_excel + +# === Step 1: Set up local and drive directory paths === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files and copy specific files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) +copy_to_local(local_processed_dir, drive_download_dir, files=[ + "LUCAS.SOIL_corr.csv" +]) + +# === Step 3: Convert Excel file to CSV(s) in place === +process_excel(local_processed_dir / "LUCAS_TOPSOIL_v1/LUCAS_TOPSOIL_v1.xlsx") + +# === Step 4: Collect all generated CSVs and standardize delimiters +move_list = [] +for csv_path in local_processed_dir.rglob("*.csv"): + try: + # Read with automatic delimiter detection (comma, pipe, etc.) + with open(csv_path, "r", encoding="utf-8") as f: + sample = f.read(2048) + + delimiter = "|" if sample.count("|") > sample.count(",") else "," + df = pd.read_csv(csv_path, delimiter=delimiter) + df.to_csv(csv_path, index=False) # overwrite with standard comma separator + + move_list.append(csv_path) + print(f"βœ… Standardized CSV: {csv_path.name}") + + except Exception as e: + print(f"❌ Failed to process CSV {csv_path.name}: {e}") + +# === Step 5: Process assets === +spec_path = local_processed_dir / "LUCAS.SOIL_corr.csv" +out_path = local_processed_dir / "assets/psd" +out_path.mkdir(parents=True, exist_ok=True) + +df = pd.read_csv(spec_path).drop(columns=["Unnamed: 0"]) + +# Extract all "spc." columns +spc_cols = [c for c in df.columns if c.startswith("spc.")] +# Parse numeric axis values from the column names +x_vals = np.array([float(c.replace("spc.", "")) for c in spc_cols]) + +# Sort columns by numeric x +sort_idx = np.argsort(x_vals) +x_vals = x_vals[sort_idx] +spc_cols_sorted = [spc_cols[i] for i in sort_idx] + +for _, row in df.iterrows(): + point_id = row["POINT_ID"] + sample_id = row["sample.ID"] + sample_id = re.sub(r"\D", "", str(sample_id)) + + y_vals = row[spc_cols_sorted].to_numpy(dtype=np.float32) + arr = np.column_stack([x_vals, y_vals]) + + fname = out_path / f"lucas2009_{point_id}_{sample_id}.npz" + np.savez(fname, arr=arr) +print(f"βœ… PSD Spectrum data saved in assets") + +# === Step 6: Zip PSD folder and upload only the zip === +psd_dir = local_processed_dir / "assets/psd" +zip_path = local_processed_dir / "assets/psd.zip" + +# Create zip archive +import zipfile +with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z: + for npz_file in psd_dir.glob("*.npz"): + # arcname removes full path β†’ keeps folder structure clean + z.write(npz_file, arcname=npz_file.name) + +print(f"βœ… Created ZIP archive: {zip_path.name}") +move_list.append(zip_path) + +# === Step 7: Upload to drive/processed === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/lucas-2015-topsoil-data-switzerland/fuse_schema.json b/src/esdac/lucas-2015-topsoil-data-switzerland/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..41a5a3538e2299235b995d9b4c40c42142943ee4 --- /dev/null +++ b/src/esdac/lucas-2015-topsoil-data-switzerland/fuse_schema.json @@ -0,0 +1,32 @@ +{ + "LUCAS_2015_Topsoil_data_of_Switzerland-with-coordinates.xlsx_.csv": { + "ENTRY_KEY": "'_'.join(['lucas2015CH', as_int_id(【Sampling location】), as_int_id(【Soil_ID】)])", + "LAT_LONG": "(as_float(【GPS_ LAT】), as_float(【GPS_LONG】))", + "COUNTRY_CODE": "'CH'", + "SAMPLE_DATE": "'XX-XX-2015'", + "SAMPLE_DEPTH_RANGE_CM": "[0, 20]", + "texture": { + "clay_percentage (%)": "as_float(【Clay/ g kg–1】)", + "silt_percentage (%)": "as_float(【Silt/ g kg–1】)", + "sand_percentage (%)": "as_float(【Sand/ g kg–1】)", + "coarse_percentage (%)": "None" + }, + "chemical": { + "pH_in_H2O": "as_float(【pH in H2O】)", + "pH_in_CaCl2": "as_float(【pH in CaCl2】)", + "electrical_conductivity (mS/m)": "as_float(【Electrical conductivity/ mS m–1】)" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【Organic carbon/ g kg–1】)", + "CaCO3_content (g/kg)": "as_float(【Calcium carbonate/ g kg–1】)" + }, + "fertility": { + "N_extractable (g/kg)": "as_float(【Nitrogen/ g kg–1】)", + "K_extractable (mg/kg)": "as_float(【Potassium/ mg kg–1】)", + "P_extractable (mg/kg)": "as_float(【Phosphorus/ mg kg–1】)" + }, + "land_site": { + "land_cover_primary": "【Land cover】" + } + } +} diff --git a/src/esdac/lucas-2015-topsoil-data-switzerland/process.py b/src/esdac/lucas-2015-topsoil-data-switzerland/process.py new file mode 100644 index 0000000000000000000000000000000000000000..75efc5ae58d2c6751630a19dcc4bb82ff090b295 --- /dev/null +++ b/src/esdac/lucas-2015-topsoil-data-switzerland/process.py @@ -0,0 +1,19 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, copy_to_local, upload +from src.process.processor import process_excel + +# === Step 1: Prepare directory paths for local and drive folders === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Copy specific file(s) from drive/download to local/processed === +name = "LUCAS_2015_Topsoil_data_of_Switzerland-with-coordinates.xlsx_.xlsx" +copy_to_local(local_processed_dir, drive_download_dir, files=[name]) + +# === Step 3: Process the Excel file into CSV === +move_list = process_excel(local_processed_dir / name, skip_rows=1) + +# === Step 4: Upload processed files to drive/processed === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/lucas-2018-topsoil-data/fuse_schema.json b/src/esdac/lucas-2018-topsoil-data/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..8074cf604fd30ce547fde26217c1cd7535e03d8c --- /dev/null +++ b/src/esdac/lucas-2018-topsoil-data/fuse_schema.json @@ -0,0 +1,106 @@ +{ + "LUCAS-SOIL-2018-data-report-readme-v2/LUCAS-SOIL-2018-v2/LUCAS-SOIL-2018.csv": { + "ENTRY_KEY": "'_'.join(['lucas2018', as_int_id(【POINTID】), '0'])", + "LAT_LONG": "(as_float(【TH_LAT】), as_float(【TH_LONG】))", + "COUNTRY_CODE": "【NUTS_0】", + "SAMPLE_DATE": "datetime.strptime(【SURVEY_DATE】, '%d-%m-%y').strftime('%d-%m-%Y') if 【SURVEY_DATE】 else 'XX-XX-2018'", + "SAMPLE_DEPTH_RANGE_CM": "[0, 20]", + "chemical": { + "pH_in_H2O": "as_float(【pH_H2O】)", + "pH_in_CaCl2": "as_float(【pH_CaCl2】)", + "electrical_conductivity (mS/m)": "as_float(【EC】)" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC】)", + "organic_carbon_content_20_30cm (g/kg)": "as_float(【OC (20-30 cm)】)", + "CaCO3_content (g/kg)": "as_float(【CaCO3】)" + }, + "fertility": { + "N_extractable (g/kg)": "as_float(【N】)", + "K_extractable (mg/kg)": "as_float(【K】)", + "P_extractable (mg/kg)": "as_float(【P】)" + }, + "mineral": { + "oxalate_extractable_Al (g/kg)": "as_float(【Ox_Al】)", + "oxalate_extractable_Fe (g/kg)": "as_float(【Ox_Fe】)" + }, + "land_site": { + "land_cover_primary": "resolve_code(【LC】, codebook='lucas_lc', ignore_error_codes=['8', 'MISSING'])", + "land_use_primary": "resolve_code(re.sub('^U', '', str(【LU】)), codebook='lucas_lu', ignore_error_codes=['8', 'MISSING'])" + }, + "topography_geology": { + "elevation (m)": "as_float(【Elev】)" + } + }, + "BD_LUCAS_data_for_paper/BD2018_data_for_paper.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "mass_density": { + "bulk_density (g/cmΒ³)": "as_float(【BDsample_0】)", + "bulk_density_fine (g/cmΒ³)": "as_float(【BDfine_0_2】)", + "course_mass_faction": "as_float(【coarse_mas】)", + "course_vol_faction": "as_float(【coarse_vol】)" + } + }, + "LUCAS-SOIL-2018-data-report-readme-v2/LUCAS-SOIL-2018-v2/BulkDensity_2018_final-2.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "mass_density": { + "bulk_density_0_10cm (g/cmΒ³)": "as_float(【BD 0-10】)", + "bulk_density_10_20cm (g/cmΒ³)": "as_float(【BD 10-20】)", + "bulk_density_20_30cm (g/cmΒ³)": "as_float(【BD 20-30】)", + "bulk_density (g/cmΒ³)": "as_float(【BD 0-20】)" + } + }, + "LUCAS-SOIL-2018-data-report-readme-v2/LUCAS-SOIL-2018-v2/LUCAS2018_ORG.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "management": { + "organic_cultivation": "【SURVEY_SOIL_ORG_CULTIVATED】=='1'", + "organic_cultivation_depth_center (cm)": "as_float(【SURVEY_SOIL_ORG_DEPTH_P_CM】)", + "organic_cultivation_depth_north (cm)": "as_float(【SURVEY_SOIL_ORG_DEPTH_N_CM】)", + "organic_cultivation_depth_east (cm)": "as_float(【SURVEY_SOIL_ORG_DEPTH_E_CM】)", + "organic_cultivation_depth_south (cm)": "as_float(【SURVEY_SOIL_ORG_DEPTH_S_CM】)", + "organic_cultivation_depth_west (cm)": "as_float(【SURVEY_SOIL_ORG_DEPTH_W_CM】)" + } + }, + "LUCAS-SOIL-2018-data-report-readme-v2/LUCAS-SOIL-2018-v2/LUCAS2018_EROSION.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "erosion": { + "erosion_signs": "【SURVEY_EROSION_SIGNS】=='1'", + "erosion_sheet_general": "【SURVEY_EROSION_SHEET】=='1'", + "erosion_sheet_point": "【SURVEY_EROSION_SHEET_P】=='1'", + "erosion_sheet_NESW": "[【SURVEY_EROSION_SHEET_N】=='1', 【SURVEY_EROSION_SHEET_E】=='1', 【SURVEY_EROSION_SHEET_S】=='1', 【SURVEY_EROSION_SHEET_W】=='1']", + "erosion_sheet_NESW_dists (m)": "[as_float(【SURVEY_EROSION_SHEET_N_DIST_M】), as_float(【SURVEY_EROSION_SHEET_E_DIST_M】), as_float(【SURVEY_EROSION_SHEET_S_DIST_M】), as_float(【SURVEY_EROSION_SHEET_W_DIST_M】)]", + "erosion_rill_general": "【SURVEY_EROSION_RILL】=='1'", + "erosion_rill_point": "【SURVEY_EROSION_RILL_P】=='1'", + "erosion_rill_NESW": "[【SURVEY_EROSION_RILL_N】=='1', 【SURVEY_EROSION_RILL_E】=='1', 【SURVEY_EROSION_RILL_S】=='1', 【SURVEY_EROSION_RILL_W】=='1']", + "erosion_rill_NESW_dists (m)": "[as_float(【SURVEY_EROSION_RILL_N_DIST_M】), as_float(【SURVEY_EROSION_RILL_E_DIST_M】), as_float(【SURVEY_EROSION_RILL_S_DIST_M】), as_float(【SURVEY_EROSION_RILL_W_DIST_M】)]", + "erosion_gully_general": "【SURVEY_EROSION_GULLY】=='1'", + "erosion_gully_point": "【SURVEY_EROSION_GULLY_P】=='1'", + "erosion_gully_NESW": "[【SURVEY_EROSION_GULLY_N】=='1', 【SURVEY_EROSION_GULLY_E】=='1', 【SURVEY_EROSION_GULLY_S】=='1', 【SURVEY_EROSION_GULLY_W】=='1']", + "erosion_gully_NESW_dists (m)": "[as_float(【SURVEY_EROSION_GULLY_N_DIST_M】), as_float(【SURVEY_EROSION_GULLY_E_DIST_M】), as_float(【SURVEY_EROSION_GULLY_S_DIST_M】), as_float(【SURVEY_EROSION_GULLY_W_DIST_M】)]", + "erosion_mass_general": "【SURVEY_EROSION_MASS】=='1'", + "erosion_mass_point": "【SURVEY_EROSION_MASS_P】=='1'", + "erosion_mass_NESW": "[【SURVEY_EROSION_MASS_N】=='1', 【SURVEY_EROSION_MASS_E】=='1', 【SURVEY_EROSION_MASS_S】=='1', 【SURVEY_EROSION_MASS_W】=='1']", + "erosion_mass_NESW_dists (m)": "[as_float(【SURVEY_EROSION_MASS_N_DIST_M】), as_float(【SURVEY_EROSION_MASS_E_DIST_M】), as_float(【SURVEY_EROSION_MASS_S_DIST_M】), as_float(【SURVEY_EROSION_MASS_W_DIST_M】)]", + "erosion_deposition_general": "【SURVEY_EROSION_DEP】=='1'", + "erosion_deposition_point": "【SURVEY_EROSION_DEP_P】=='1'", + "erosion_deposition_NESW": "[【SURVEY_EROSION_DEP_N】=='1', 【SURVEY_EROSION_DEP_E】=='1', 【SURVEY_EROSION_DEP_S】=='1', 【SURVEY_EROSION_DEP_W】=='1']", + "erosion_deposition_NESW_dists (m)": "[as_float(【SURVEY_EROSION_DEP_N_DIST_M】), as_float(【SURVEY_EROSION_DEP_E_DIST_M】), as_float(【SURVEY_EROSION_DEP_S_DIST_M】), as_float(【SURVEY_EROSION_DEP_W_DIST_M】)]", + "erosion_wind_general": "【SURVEY_EROSION_WIND】=='1'", + "erosion_wind_point": "【SURVEY_EROSION_WIND_P】=='1'", + "erosion_wind_NESW": "[【SURVEY_EROSION_WIND_N】=='1', 【SURVEY_EROSION_WIND_E】=='1', 【SURVEY_EROSION_WIND_S】=='1', 【SURVEY_EROSION_WIND_W】=='1']", + "erosion_wind_NESW_dists (m)": "[as_float(【SURVEY_EROSION_WIND_N_DIST_M】), as_float(【SURVEY_EROSION_WIND_E_DIST_M】), as_float(【SURVEY_EROSION_WIND_S_DIST_M】), as_float(【SURVEY_EROSION_WIND_W_DIST_M】)]", + "erosion_rill_gully_north": "【SURVEY_EROSION_RILLGULLY_N】=='1'" + } + }, + "LUCAS_Text_All_10032025/LUCAS_Text_All_10032025.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINTID】) + '_.*$'", + "texture": { + "clay_percentage (%)": "as_float(【Clay】)", + "silt_percentage (%)": "as_float(【Silt】)", + "sand_percentage (%)": "as_float(【Sand】)", + "coarse_percentage (%)": "as_float(【Coarse】)", + "USDA_class": "【USDA】", + "ISSS_class": "【ISSS】" + } + } +} diff --git a/src/esdac/lucas-2018-topsoil-data/process.py b/src/esdac/lucas-2018-topsoil-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..24c964769b09e2f10f48194ac1f0eb21ddd13c4d --- /dev/null +++ b/src/esdac/lucas-2018-topsoil-data/process.py @@ -0,0 +1,14 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload + +# === Step 1: Set up local and drive directory paths === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 4: Collect all generated CSVs and upload to drive/processed === +move_list = list(local_processed_dir.rglob("*.csv")) +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/lucas2015-topsoil-data/fuse_schema.json b/src/esdac/lucas2015-topsoil-data/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..401c28f77552b7fb8d5c36a635208c2cdb76e08d --- /dev/null +++ b/src/esdac/lucas2015-topsoil-data/fuse_schema.json @@ -0,0 +1,69 @@ +{ + "LUCAS2015_topsoildata_20200323/LUCAS_Topsoil_2015_20200323-shapefile/LUCAS_Topsoil_2015_20200323.shp.csv": { + "ENTRY_KEY": "'_'.join(['lucas2015', as_int_id(【Point_ID】), '0'])", + "LAT_LONG": "tuple(map(float, 【geometry】.replace('POINT','').replace('(','').replace(')','').split()))[::-1]", + "COUNTRY_CODE": "【NUTS_0】", + "SAMPLE_DATE": "'XX-XX-2015'", + "SAMPLE_DEPTH_RANGE_CM": "[0, 20]", + "texture": { + "clay_percentage (%)": "as_float(【Clay】)", + "silt_percentage (%)": "as_float(【Silt】)", + "sand_percentage (%)": "as_float(【Sand】)", + "coarse_percentage (%)": "as_float(【Coarse】)" + }, + "chemical": { + "pH_in_H2O": "as_float(【pH_H20】)", + "pH_in_CaCl2": "as_float(【pH_CaCl2】)", + "electrical_conductivity (mS/m)": "as_float(【EC】)" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC】)", + "CaCO3_content (g/kg)": "as_float(【CaCO3】)" + }, + "fertility": { + "N_extractable (g/kg)": "as_float(【N】)", + "K_extractable (mg/kg)": "as_float(【K】)", + "P_extractable (mg/kg)": "as_float(【P】)" + }, + "land_site": { + "land_cover_primary": "resolve_code(【LC】, codebook='lucas_lc', ignore_error_codes=['8', 'MISSING'])", + "land_use_primary": "resolve_code(re.sub('^U', '', str(【LU】)), codebook='lucas_lu', ignore_error_codes=['8', 'MISSING'])" + }, + "ASSETS": { + "psd_spectrum": "f\"psd/{'_'.join(['lucas2015', as_int_id(【Point_ID】), '0'])}.npz\"" + } + }, + "LUCAS2015_AncillaryData_20201007.csv": { + "ENTRY_KEY": "'^' + '_'.join(['lucas2015', as_int_id(【POI】), '0']) + '$'", + "soil_type": { + "WRB_soil_group": "【Soil_Group】" + }, + "climate": { + "biogeography_type": "【BioGeo】", + "annual_mean_temperature (Β°C)": "as_float(【BIO1】)", + "mean_diurnal_range (Β°C)": "as_float(【BIO2】)", + "isothermality (%)": "as_float(【BIO3】)", + "temperature_seasonality (SD Γ—100, Β°C)": "as_float(【BIO4】)", + "max_temperature_of_warmest_month (Β°C)": "as_float(【BIO5】)", + "min_temperature_of_coldest_month (Β°C)": "as_float(【BIO6】)", + "temperature_annual_range (Β°C)": "as_float(【BIO7】)", + "mean_temperature_of_wettest_quarter (Β°C)": "as_float(【BIO8】)", + "mean_temperature_of_driest_quarter (Β°C)": "as_float(【BIO9】)", + "mean_temperature_of_warmest_quarter (Β°C)": "as_float(【BIO10】)", + "mean_temperature_of_coldest_quarter (Β°C)": "as_float(【BIO11】)", + "annual_precipitation (mm)": "as_float(【BIO12】)", + "precipitation_of_wettest_month (mm)": "as_float(【BIO13】)", + "precipitation_of_driest_month (mm)": "as_float(【BIO14】)", + "precipitation_seasonality (CV, %)": "as_float(【BIO15】)", + "precipitation_of_wettest_quarter (mm)": "as_float(【BIO16】)", + "precipitation_of_driest_quarter (mm)": "as_float(【BIO17】)", + "precipitation_of_warmest_quarter (mm)": "as_float(【BIO18】)", + "precipitation_of_coldest_quarter (mm)": "as_float(【BIO19】)" + }, + "topography_geology": { + "elevation (m)": "as_float(【Elevation】)", + "slope (deg)": "as_float(【Slope】)", + "aspect (deg)": "as_float(【Aspect】)" + } + } +} diff --git a/src/esdac/lucas2015-topsoil-data/process.py b/src/esdac/lucas2015-topsoil-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..512d9a0aa96eba413e977d559ee6d975dfb32332 --- /dev/null +++ b/src/esdac/lucas2015-topsoil-data/process.py @@ -0,0 +1,83 @@ +from pathlib import Path + +import numpy as np +import pandas as pd + +from src.process.io import prepare_dirs, unzip, unzip_nested, upload, copy_to_local +from src.process.processor import process_shp + +# === Step 1: Set up local and drive directory paths === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) +unzip_nested(local_processed_dir) +copy_to_local(local_processed_dir, drive_download_dir, files=[ + "LUCAS2015_AncillaryData_20201007.csv" +]) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Process assets === +spec_dir = local_processed_dir / "LUCAS2015_spectra/LUCAS2015_Soil_Spectra_EU28" +out_path = local_processed_dir / "assets/psd" +out_path.mkdir(parents=True, exist_ok=True) + +for spec_path in sorted(spec_dir.rglob("*.csv")): + df = pd.read_csv(spec_path) + + # Identify metadata columns to exclude + meta_cols = {"source", "SampleID", "PointID", "NUTS_0", "SampleN"} + + # Extract spectral columns (handle both "spc.xxx" and plain numeric names) + spc_cols = [ + c for c in df.columns + if c not in meta_cols and (c.replace(".", "", 1).isdigit() or c.startswith("spc.")) + ] + + # Parse numeric axis values from the column names + x_vals = np.array([ + float(c.replace("spc.", "")) if c.startswith("spc.") else float(c) + for c in spc_cols + ]) + + # Sort columns by numeric x + sort_idx = np.argsort(x_vals) + x_vals = x_vals[sort_idx] + spc_cols_sorted = [spc_cols[i] for i in sort_idx] + + for _, row in df.iterrows(): + point_id = row["PointID"] + sample_id = '0' # main data csv does not contain sample id + + y_vals = row[spc_cols_sorted].to_numpy(dtype=np.float32) + arr = np.column_stack([x_vals, y_vals]) + + fname = out_path / f"lucas2015_{point_id}_{sample_id}.npz" + np.savez(fname, psd=arr) + + print(f"βœ… PSD Spectrum data saved in assets") + +# === NEW Step: Zip PSD folder and upload only the zip === +psd_dir = out_path +zip_path = local_processed_dir / "assets/psd.zip" + +import zipfile +with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z: + for npz_file in psd_dir.glob("*.npz"): + z.write(npz_file, arcname=npz_file.name) + +print(f"βœ… Created ZIP archive: {zip_path.name}") +move_list.append(zip_path) # only zip is uploaded + +# === Step 5: Collect all generated CSVs and upload to drive/processed === +csvs = list(local_processed_dir.rglob("*.csv")) +for csv_path in csvs: + move_list.append(csv_path) + +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil/fuse_schema.json b/src/esdac/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..2128c8314f0f95e96d0aee736b9dc98a1befa095 --- /dev/null +++ b/src/esdac/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "rawmaterial_from_soils_10_52@tif": { + "function_suitability": { + "raw_material_availability_index_organics (0-10)": "rawmaterial_from_soils_10_52__org", + "raw_material_availability_index_construction_material (0-10)": "rawmaterial_from_soils_10_52__constr" + } + } +} diff --git a/src/esdac/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil/process.py b/src/esdac/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil/process.py new file mode 100644 index 0000000000000000000000000000000000000000..6b05e29a51f5d3cd1101222a9113f18a3a4efcf7 --- /dev/null +++ b/src/esdac/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil/process.py @@ -0,0 +1,26 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import rasterize_shp, process_shp, process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Rasterize to tif === +shp_path = local_processed_dir / "rawmaterial_from_soils_10_52/rawmaterial_from_soils_10_52.shp" +columns_to_rasterize = ["org", "constr"] +rasterize_shp(shp_path, columns_to_rasterize, resolution_m=1000) +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 5: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0/fuse_schema.json b/src/esdac/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..fc378c95af9c499e34bbe36dee780a12f99f0455 --- /dev/null +++ b/src/esdac/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0/fuse_schema.json @@ -0,0 +1,26 @@ +{ + ".@tif": { + "trace_elements": { + "As_concentration_1km_res (mg/kg)": "As/As_EU27", + "As_concentration_variance_1km_res (mgΒ²/kgΒ²)": "As/As_var_EU27", + "Cd_concentration_1km_res (mg/kg)": "Cd/Cd_EU27", + "Cd_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Cd/Cd_var_EU27", + "Cr_concentration_1km_res (mg/kg)": "Cr/Cr_EU27", + "Cr_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Cr/Cr_var_EU27", + "Cu_concentration_1km_res (mg/kg)": "Cu/Cu_EU27", + "Cu_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Cu/Cu_var_EU27", + "Hg_concentration_1km_res (mg/kg)": "Hg/Hg_EU27", + "Hg_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Hg/Hg_var_EU27", + "Pb_concentration_1km_res (mg/kg)": "Pb/Pb_EU27", + "Pb_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Pb/Pb_var_EU27", + "Mn_concentration_1km_res (mg/kg)": "Mn/Mn_EU27", + "Mn_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Mn/Mn_var_EU27", + "Sb_concentration_1km_res (mg/kg)": "Sb/Sb_EU27", + "Sb_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Sb/Sb_var_EU27", + "Co_concentration_1km_res (mg/kg)": "Co/Co_EU27", + "Co_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Co/Co_var_EU27", + "Ni_concentration_1km_res (mg/kg)": "Ni/Ni_EU27", + "Ni_concentration_variance_1km_res (mgΒ²/kgΒ²)": "Ni/Ni_var_EU27" + } + } +} diff --git a/src/esdac/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0/process.py b/src/esdac/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0/process.py new file mode 100644 index 0000000000000000000000000000000000000000..545c630f0416ad593ff4c89222cffc0709ac127f --- /dev/null +++ b/src/esdac/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/maps-indicators-soil-hydraulic-properties-europe/fuse_schema.json b/src/esdac/maps-indicators-soil-hydraulic-properties-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..33662b1d213150061b8169e987b0eb1bd9e123c1 --- /dev/null +++ b/src/esdac/maps-indicators-soil-hydraulic-properties-europe/fuse_schema.json @@ -0,0 +1,10 @@ +{ + "FS_water_2016_07@tif": { + "hydraulic": { + "saturated_water_content (cmΒ³ cm⁻³)": "ths_fao_octop", + "field_capacity_water_content (cmΒ³ cm⁻³)": "fc_fao", + "wilting_point_water_content (cmΒ³ cm⁻³)": "wp_fao", + "saturated_hydraulic_conductivity (cm/day)": "ks_fao_octop" + } + } +} diff --git a/src/esdac/maps-indicators-soil-hydraulic-properties-europe/process.py b/src/esdac/maps-indicators-soil-hydraulic-properties-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..681c85978d1cd38a4cb10e8f7ba9aaef2e3d3a6d --- /dev/null +++ b/src/esdac/maps-indicators-soil-hydraulic-properties-europe/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0/fuse_schema.json b/src/esdac/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..1254a63b72d3ae90e50fd96b16312fdf596e3198 --- /dev/null +++ b/src/esdac/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0/fuse_schema.json @@ -0,0 +1,10 @@ +{ + "SF_cultural/SF_cultural@tif": { + "function_suitability": { + "cultural_material_preservation_capacity_index_bones (0-3)": "SF_cultural__bones", + "cultural_material_preservation_capacity_index_metals (0-3)": "SF_cultural__metals", + "cultural_material_preservation_capacity_index_organics (0-3)": "SF_cultural__organics", + "cultural_material_preservation_capacity_index_stratigraphic_evidence (0-3)": "SF_cultural__strati" + } + } +} diff --git a/src/esdac/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0/process.py b/src/esdac/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0/process.py new file mode 100644 index 0000000000000000000000000000000000000000..731487439bf4fa5d1f23dd504df8a26a01c5d909 --- /dev/null +++ b/src/esdac/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0/process.py @@ -0,0 +1,27 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp, rasterize_shp, process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list, drop_geometry=False) + +# === Step 4: Rasterize to tif === +shp_path = local_processed_dir / "SF_cultural/SF_cultural/SF_cultural.shp" +columns_to_rasterize = ["bones", "metals", "organics", "strati"] +rasterize_shp(shp_path, columns_to_rasterize, resolution_m=1000) +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 5: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/maps-storing-and-filtering-capacity-soils-europe/fuse_schema.json b/src/esdac/maps-storing-and-filtering-capacity-soils-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0ddb9860f7268f2257ffdb7a22984f1b127bd275 --- /dev/null +++ b/src/esdac/maps-storing-and-filtering-capacity-soils-europe/fuse_schema.json @@ -0,0 +1,16 @@ +{ + "ESDB_STORING_FILTERING_MAPS-without-intermediate-params@tif": { + "function_suitability": { + "storing_capacity_index_cation (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__STOR_CAPCA", + "filter_capacity_index_cation (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__FILT_CAPCA", + "storing_capacity_index_anion (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__STOR_CAPAN", + "filter_capacity_index_anion (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__FILT_CAPAN", + "storing_capacity_index_solids_pathogens (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__STOR_CAPSO", + "filter_capacity_index_solids_pathogens (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__FILT_CAPSO", + "storing_capacity_index_non_polar_organics (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__STOR_CAPNP", + "filter_capacity_index_non_polar_organics (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__FILT_CAPNP", + "storing_capacity_index_non_aqueous_phase_liquids (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__STOR_NAPL", + "filter_capacity_index_non_aqueous_phase_liquids (0-10)": "ESDB_STORING_FILTERING_MAPS-without-intermediate-params__FILT_NAPL" + } + } +} diff --git a/src/esdac/maps-storing-and-filtering-capacity-soils-europe/process.py b/src/esdac/maps-storing-and-filtering-capacity-soils-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..3932ffd4baf2ea96895cacb38f116cc6f4d71eb4 --- /dev/null +++ b/src/esdac/maps-storing-and-filtering-capacity-soils-europe/process.py @@ -0,0 +1,36 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp, rasterize_shp, process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Rasterize to tif === +shp_path = local_processed_dir / "ESDB_STORING_FILTERING_MAPS-without-intermediate-params/ESDB_STORING_FILTERING_MAPS-without-intermediate-params.shp" +columns_to_rasterize = ["STOR_CAPCA", + "FILT_CAPCA", + "STOR_CAPAN", + "FILT_CAPAN", + "STOR_CAPSO", + "FILT_CAPSO", + "STOR_CAPNP", + "FILT_CAPNP", + "STOR_NAPL", + "FILT_NAPL"] +rasterize_shp(shp_path, columns_to_rasterize, resolution_m=1000) +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 5: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/mercury-content-european-union-topsoil/fuse_schema.json b/src/esdac/mercury-content-european-union-topsoil/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..6f1809a0cf831d8080d3c629a5eb0f9078394963 --- /dev/null +++ b/src/esdac/mercury-content-european-union-topsoil/fuse_schema.json @@ -0,0 +1,9 @@ +{ + ".@tif": { + "trace_elements": { + "Hg_concentration (Β΅g/kg)": "Hg_dnn_250m_EU/Hg_dnn_mukg_250m_nn.tif_Masked_NODATA_COM", + "Hg_residual (Β΅g/kg)": "Hg_res_mukg_250m/Hg_res_mukg_250m.tif_Masked_NODATA_COM", + "Hg_stock (g ha⁻¹)": "Fig4_Hg_stock/Hg_g_ha_masked_withUK" + } + } +} diff --git a/src/esdac/mercury-content-european-union-topsoil/process.py b/src/esdac/mercury-content-european-union-topsoil/process.py new file mode 100644 index 0000000000000000000000000000000000000000..938fc5f20c476599e49347593ff6092ce73ba69f --- /dev/null +++ b/src/esdac/mercury-content-european-union-topsoil/process.py @@ -0,0 +1,27 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directory paths === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process only the specified TIFF files into standardized .tif and preview .png === +tif_to_keep = [ + "Hg_dnn_mukg_250m_nn.tif_Masked_NODATA_COM.tif", + "Hg_g_ha_masked_withUK.tif", + "Hg_res_mukg_250m.tif_Masked_NODATA_COM.tif" +] + +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + if tif.name in tif_to_keep: + move_list = process_tif(tif, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/multiple-concurrent-soil-erosion-processes/fuse_schema.json b/src/esdac/multiple-concurrent-soil-erosion-processes/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..dbce7ea37b3948546dd41397b51dde45b1809aeb --- /dev/null +++ b/src/esdac/multiple-concurrent-soil-erosion-processes/fuse_schema.json @@ -0,0 +1,11 @@ +{ + ".@tif": { + "erosion": { + "net_erosion_rate (t ha⁻¹ yr⁻¹)": "All_ErosionProcess/Multiple_concurrent_soil_erosion_processes", + "erosion_rate_by_water (t ha⁻¹ yr⁻¹)": "Water/Water", + "erosion_rate_by_tillage (t ha⁻¹ yr⁻¹)": "Tillage/Tillage", + "erosion_rate_by_harvest_root_crop (t ha⁻¹ yr⁻¹)": "SLCH/SLCH", + "erosion_rate_by_wind (t ha⁻¹ yr⁻¹)": "Wind/Wind" + } + } +} diff --git a/src/esdac/multiple-concurrent-soil-erosion-processes/process.py b/src/esdac/multiple-concurrent-soil-erosion-processes/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/multiple-concurrent-soil-erosion-processes/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/n2o-emissions-agricultural-soils-europe/fuse_schema.json b/src/esdac/n2o-emissions-agricultural-soils-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..ed1d374c0913a5bed634ffa60548877a3d1ec72f --- /dev/null +++ b/src/esdac/n2o-emissions-agricultural-soils-europe/fuse_schema.json @@ -0,0 +1,9 @@ +{ + "LUCAS_points_N2O.csv": { + "ENTRY_KEY": "'^.*_.*_' + as_int_id(【sample_ID】, allow_letters=True) + '$'", + "greenhouse": { + "N2O_emission_rate_mean (kg N ha⁻¹ yr⁻¹)": "as_float(【N2Oavg】)", + "N2O_emission_rate_std (kg N ha⁻¹ yr⁻¹)": "as_float(【N2Osd】)" + } + } +} diff --git a/src/esdac/n2o-emissions-agricultural-soils-europe/process.py b/src/esdac/n2o-emissions-agricultural-soils-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..eb52350661a098d334901f945e6a9c1e86e66a97 --- /dev/null +++ b/src/esdac/n2o-emissions-agricultural-soils-europe/process.py @@ -0,0 +1,22 @@ +from pathlib import Path + +from packaging.tags import cpython_tags + +from src.process.io import prepare_dirs, copy_to_local, upload +from src.process.processor import process_dbf + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Copy .dbf to files to local === +copy_to_local(local_processed_dir, drive_download_dir, ["LUCAS_points_N2O.dbf"]) + +# === Step 3: Process all .dbf files === +move_list = [] +for dbf in local_processed_dir.rglob("*.dbf"): + move_list = process_dbf(dbf, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/natural-susceptibility-soil-compaction-europe/fuse_schema.json b/src/esdac/natural-susceptibility-soil-compaction-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..cd36f2be8975ffe760a768e7cb20e1eb78180684 --- /dev/null +++ b/src/esdac/natural-susceptibility-soil-compaction-europe/fuse_schema.json @@ -0,0 +1,17 @@ +{ + "ESDB_soil_compaction_pack/soil_compaction_pack/data@tif": { + "land_degradation": { + "natural_susceptibility_soil_compaction": [ + "natural_soil_susc_EU27_laea1052__Evaluation", + { + "0": "no soil (water bodies, glaciers, rock outcrops)", + "1": "low susceptibility to compaction", + "2": "medium susceptibility to compaction", + "3": "high susceptibility to compaction", + "4": "very high susceptibility to compaction", + "9": "no evaluation possible (towns, disturbed soils, marsh)" + } + ] + } + } +} diff --git a/src/esdac/natural-susceptibility-soil-compaction-europe/fuse_skip.txt b/src/esdac/natural-susceptibility-soil-compaction-europe/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..23308d4b23bf93c192bc90d7ec6b49326ef01f95 --- /dev/null +++ b/src/esdac/natural-susceptibility-soil-compaction-europe/fuse_skip.txt @@ -0,0 +1 @@ +TBC: polygon-based \ No newline at end of file diff --git a/src/esdac/natural-susceptibility-soil-compaction-europe/process.py b/src/esdac/natural-susceptibility-soil-compaction-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..397b1eef93954fb0ea812fcbbe743f4ae60056ce --- /dev/null +++ b/src/esdac/natural-susceptibility-soil-compaction-europe/process.py @@ -0,0 +1,29 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp, rasterize_shp, process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Rasterize to tif === +shp_path = local_processed_dir / "ESDB_soil_compaction_pack/soil_compaction_pack/data/natural_soil_susc_EU27_laea1052.shp" +columns_to_rasterize = ["Evaluation"] +rasterize_shp(shp_path, columns_to_rasterize, resolution_m=1000) +shp_path = local_processed_dir / "ESDB_soil_compaction_pack/soil_compaction_pack/data/natural_soil_susc_Eurasia_laea1052.shp" +columns_to_rasterize = ["Evaluation"] +rasterize_shp(shp_path, columns_to_rasterize, resolution_m=1000) +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 5: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/observedtypical-soc-index/fuse_schema.json b/src/esdac/observedtypical-soc-index/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..e3a3e1ad4434c86e434b41d00b52d4480182e38c --- /dev/null +++ b/src/esdac/observedtypical-soc-index/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "Observed-Typical SOC@tif": { + "carbon": { + "observed_vs_typical_soc_index": "obs_typ_soc_index_eu_100m", + "observed_vs_typical_soc_index_confidence_zone": "pcz_eu_100m" + } + } +} diff --git a/src/esdac/observedtypical-soc-index/process.py b/src/esdac/observedtypical-soc-index/process.py new file mode 100644 index 0000000000000000000000000000000000000000..7763a99a9fe77b76c0efdab576f3404e31191435 --- /dev/null +++ b/src/esdac/observedtypical-soc-index/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/octop-topsoil-organic-carbon-content-europe/fuse_schema.json b/src/esdac/octop-topsoil-organic-carbon-content-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..49e86b2cadd373c78d2eb9037c2d7ab3f56134b7 --- /dev/null +++ b/src/esdac/octop-topsoil-organic-carbon-content-europe/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "octop_80_89_v121_ascii@tif": { + "CRS_STRING": "+proj=laea +lat_0=48 +lon_0=9 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs", + "carbon": { + "organic_carbon_content_topsoil (g/kg)": "octop_V121" + } + } +} diff --git a/src/esdac/octop-topsoil-organic-carbon-content-europe/process.py b/src/esdac/octop-topsoil-organic-carbon-content-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..3bc7a6fc89dc341912fefefcb72ad86e10be8bd1 --- /dev/null +++ b/src/esdac/octop-topsoil-organic-carbon-content-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all ASC files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.asc")): + move_list = process_tif(tif_path, move_list, nodata=0.) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/pan-european-soc-stock-agricultural-soils/fuse_schema.json b/src/esdac/pan-european-soc-stock-agricultural-soils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..949f50d55e57a73d25e4919b7d19193dfddcd769 --- /dev/null +++ b/src/esdac/pan-european-soc-stock-agricultural-soils/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "C_eroded@tif": { + "carbon": { + "eroded_carbon_stock (t ha⁻¹)": "C_eroded_prj", + "eroded_soc_content (g/kg)": "SOC_erd" + } + } +} diff --git a/src/esdac/pan-european-soc-stock-agricultural-soils/process.py b/src/esdac/pan-european-soc-stock-agricultural-soils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..1115b43462bbee2b2b26e1738a4690a1f1241a49 --- /dev/null +++ b/src/esdac/pan-european-soc-stock-agricultural-soils/process.py @@ -0,0 +1,22 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_dbf + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif and .dbf files === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) +for dbf in local_processed_dir.rglob("*.dbf"): + move_list = process_dbf(dbf, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/pan-european-soil-erosion-risk-assessment-pesera/fuse_schema.json b/src/esdac/pan-european-soil-erosion-risk-assessment-pesera/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..3e47bd55f159e9f0751ceea036991f7494563632 --- /dev/null +++ b/src/esdac/pan-european-soil-erosion-risk-assessment-pesera/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "Geotiff@tif": { + "erosion": { + "net_erosion_rate_PESERA (t ha⁻¹ yr⁻¹)": "PESERA" + } + } +} diff --git a/src/esdac/pan-european-soil-erosion-risk-assessment-pesera/process.py b/src/esdac/pan-european-soil-erosion-risk-assessment-pesera/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/pan-european-soil-erosion-risk-assessment-pesera/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/phosphorus-budget-and-p-stocks/fuse_schema.json b/src/esdac/phosphorus-budget-and-p-stocks/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..6c40fb015d2ced5d0e6335694c9d0f020a14954f --- /dev/null +++ b/src/esdac/phosphorus-budget-and-p-stocks/fuse_schema.json @@ -0,0 +1,13 @@ +{ + "P_Budget@tif": { + "fertility": { + "P_available_stock (kg ha⁻¹)": "Fig1_Available_P_stock", + "P_total_stock_topsoil (kg ha⁻¹)": "Fig2_Total P stock in the agricultural topsoil" + } + }, + "P_Total_S1@tif": { + "fertility": { + "P_total_concentration_topsoil (mg/kg)": "Fig.S1_Total_phosphorus_concentrations" + } + } +} diff --git a/src/esdac/phosphorus-budget-and-p-stocks/process.py b/src/esdac/phosphorus-budget-and-p-stocks/process.py new file mode 100644 index 0000000000000000000000000000000000000000..a04ea6abbe038ec863ebc2869830691e44abffc9 --- /dev/null +++ b/src/esdac/phosphorus-budget-and-p-stocks/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/phosphorus-cycle-european-agricultural-soils/fuse_schema.json b/src/esdac/phosphorus-cycle-european-agricultural-soils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..e9ccd6a8fd9d58814cfbfb07864554aa1a81ad27 --- /dev/null +++ b/src/esdac/phosphorus-cycle-european-agricultural-soils/fuse_schema.json @@ -0,0 +1,51 @@ +{ + "Current_EU.tif_@tif": { + "fertility": { + "P_budget_current (kg P ha⁻¹ yr⁻¹)": "Current_EU" + } + }, + "Data_Figures/QGIS_files@tif": { + "fertility": { + "P_budget_2030_min (kg P ha⁻¹ yr⁻¹)": "EU_budget_2030_min.tif/EU_budget_2030_min", + "P_budget_2050_min (kg P ha⁻¹ yr⁻¹)": "EU_budget_2050_min.tif/EU_budget_2050_min", + "P_erosion_2030_min (kg P ha⁻¹ yr⁻¹)": "EU_erosion_2030_min.tif/EU_erosion_2030_min", + "P_erosion_2050_min (kg P ha⁻¹ yr⁻¹)": "EU_erosion_2050_min.tif/EU_erosion_2050_min", + "P_export_2030_max (kg P ha⁻¹ yr⁻¹)": "EU_export_2030_max.tif/EU_export_2030_max", + "P_export_2050_max (kg P ha⁻¹ yr⁻¹)": "EU_export_2050_max.tif/EU_export_2050_max", + "P_risk_of_high_erosion": [ + "high_eros.tif/high_eros", + { + "0": false, + "1": true + } + ], + "P_risk_of_leaching": [ + "Pav_leach.tif/Pav_leach", + { + "0": false, + "1": true + } + ], + "P_available_fraction_of_total (%)": "Pav_of_Ptot.tif/Pav_of_Ptot", + "P_availability_sufficiency_index": "Pav_sufficient.tif/Pav_sufficient", + "P_available_Olsen (mg/kg)": "Pavail_Olsen.tif/Pavail_Olsen", + "P_budget_high_sorption_scenario (kg P ha⁻¹ yr⁻¹)": "Pbudget_highSorp.tif/Pbudget_highSorp", + "P_budget_low_sorption_scenario (kg P ha⁻¹ yr⁻¹)": "Pbudget_lowSorp.tif/Pbudget_lowSorp", + "P_periodic_loss (kg P ha⁻¹ yr⁻¹)": "Perod.tif/Perod", + "P_export (kg P ha⁻¹ yr⁻¹)": "Pexport.tif/Pexport", + "P_input_mineral_fertiliser (kg P ha⁻¹ yr⁻¹)": "Pin_mineral.tif/Pin_mineral", + "P_input_organic (kg P ha⁻¹ yr⁻¹)": "Pin_organic.tif/Pin_organic", + "P_input_total (kg P ha⁻¹ yr⁻¹)": "Pinput.tif/Pinput", + "P_total_concentration (mg/kg)": "Ptot.tif/Ptot" + } + }, + "Projections/Projection@tif": { + "fertility": { + "P_budget_bau_scenario (kg P ha⁻¹ yr⁻¹)": "BAU_EU.tif/BAU_EU", + "P_budget_climate_change_scenario (kg P ha⁻¹ yr⁻¹)": "CC_EU.tif/CC_EU", + "P_budget_climate_change_with_nfix (kg P ha⁻¹ yr⁻¹)": "CC_nfix_EU.tif/CC_nfix_EU", + "P_budget_low_mineral_input (kg P ha⁻¹ yr⁻¹)": "lowPmin_EU.tif/lowPmin_EU", + "P_budget_low_organic_input (kg P ha⁻¹ yr⁻¹)": "lowPorg_EU.tif/lowPorg_EU" + } + } +} diff --git a/src/esdac/phosphorus-cycle-european-agricultural-soils/process.py b/src/esdac/phosphorus-cycle-european-agricultural-soils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..712d998ab60de7e0cf88ba82dbafa598db160cbc --- /dev/null +++ b/src/esdac/phosphorus-cycle-european-agricultural-soils/process.py @@ -0,0 +1,21 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload, unzip_nested +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) +unzip_nested(local_processed_dir) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/post-fire-soil-erosion-europe/fuse_skip.txt b/src/esdac/post-fire-soil-erosion-europe/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..7718bd09dccce875f801366bca8c8570da442e63 --- /dev/null +++ b/src/esdac/post-fire-soil-erosion-europe/fuse_skip.txt @@ -0,0 +1 @@ +Small region \ No newline at end of file diff --git a/src/esdac/post-fire-soil-erosion-europe/process.py b/src/esdac/post-fire-soil-erosion-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/post-fire-soil-erosion-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/potential-threats-soil-biodiversity-europe/fuse_schema.json b/src/esdac/potential-threats-soil-biodiversity-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5d0608e31fe048b224aae283074f8c692512cb75 --- /dev/null +++ b/src/esdac/potential-threats-soil-biodiversity-europe/fuse_schema.json @@ -0,0 +1,25 @@ +{ + "final_maps@tif": { + "biodiversity": { + "soil_biological_functions_threat_index": "soil_biological_functions_threat", + "soil_fauna_threat_index": "soil_fauna_threat", + "soil_microorganisms_threat_index": "soil_microorganisms_threat" + } + }, + "Input_layers@tif": { + "biodiversity": { + "climate_change_pressure_index": "climate_change", + "soil_compaction_pressure_index": "compaction", + "soil_erosion_pressure_index": "erosion", + "genetically_modified_organism_use_pressure_index": "GMO_use", + "habitat_fragmentation_pressure_index": "habitat_fragmentation", + "industrial_pollution_pressure_index": "industrial_pollution", + "intensive_human_exploitation_pressure_index": "intensive_human_exploitation", + "invasive_species_pressure_index": "invasive_species", + "land_use_change_pressure_index": "land_use_change", + "organic_matter_decline_pressure_index": "organic_matter_decline", + "radioactivity_pressure_index": "radioactivity", + "soil_salinity_pressure_index": "salinity" + } + } +} diff --git a/src/esdac/potential-threats-soil-biodiversity-europe/process.py b/src/esdac/potential-threats-soil-biodiversity-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/potential-threats-soil-biodiversity-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1/fuse_skip.txt b/src/esdac/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..09130966c1e943a1d151ce760b5b4f9d8cb0b7d0 --- /dev/null +++ b/src/esdac/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1/fuse_skip.txt @@ -0,0 +1 @@ +Prediction \ No newline at end of file diff --git a/src/esdac/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1/process.py b/src/esdac/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1/process.py new file mode 100644 index 0000000000000000000000000000000000000000..aef96441f8911984540de37954847861119e27d1 --- /dev/null +++ b/src/esdac/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .shp files === +move_list = [] +for shp in local_processed_dir.rglob("*.shp"): + move_list = process_shp(shp, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/priming-effects-soils-across-europe/fuse_schema.json b/src/esdac/priming-effects-soils-across-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..1db267c5ae564b617667f8b57c2da1a8d2a5afc9 --- /dev/null +++ b/src/esdac/priming-effects-soils-across-europe/fuse_schema.json @@ -0,0 +1,18 @@ +{ + "Table_S1_Repository_merged.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【LUCAS.ID】) + '_.*$'", + "biomass": { + "bacterial_biomass (nmol FAME g⁻¹ soil)": "as_float(【Bacteria nmol FAME g-1 soil】)", + "fungal_biomass (nmol FAME g⁻¹ soil)": "as_float(【Fungi nmol FAME g-1 soil】)" + }, + "carbon": { + "priming_effect_35day_cumulative (Β΅g C g⁻¹ soil)": "as_float(【Priming mug C g-1 soil】)", + "basal_respiration_35day_cumulative (Β΅g C g⁻¹ soil)": "as_float(【Basal respiration mug C g-1 soil】)", + "glucose_mineralization_pct (%)": "as_float(【Glucose mineralization %】)" + }, + "soil_type": { + "seminatural_soil_type": "【Type of seminatural soil】", + "cropland_soil_type": "【Type of cropland soil】" + } + } +} diff --git a/src/esdac/priming-effects-soils-across-europe/process.py b/src/esdac/priming-effects-soils-across-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..f38de75e337b3898ae228f4e56fd3d287a6aa838 --- /dev/null +++ b/src/esdac/priming-effects-soils-across-europe/process.py @@ -0,0 +1,40 @@ +from pathlib import Path + +import pandas as pd + +from src.process.io import prepare_dirs, copy_to_local, upload +from src.process.processor import process_excel + +# === Step 1: Prepare directory paths for local and drive folders === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Copy specific file(s) from drive/download to local/processed === +copy_to_local(local_processed_dir, drive_download_dir, files=["Table_S1_Repository.xlsx"]) + +# Load the Excel file +input_path = local_processed_dir / "Table_S1_Repository.xlsx" +output_path = local_processed_dir / "Table_S1_Repository_merged.xlsx" + +# Read first three rows separately +header_part1 = pd.read_excel(input_path, header=None, nrows=3) + +# Merge row 2 and 3 (index 1 and 2 since row 1 is empty) +header_row1 = header_part1.iloc[1].fillna("") +header_row2 = header_part1.iloc[2].fillna("") +merged_header = [f"{a.strip()} {b.strip()}".strip().replace("Β΅", "mu") + for a, b in zip(header_row1, header_row2)] + +# Read the rest of the data starting from row 4 (skip first 3) +data = pd.read_excel(input_path, skiprows=3, header=None) +data.columns = merged_header + +# Write back to a new Excel file +data.to_excel(output_path, index=False) + +# === Step 3: Process the Excel file into CSV === +move_list = process_excel(local_processed_dir / "Table_S1_Repository_merged.xlsx") + +# === Step 4: Upload processed files to drive/processed === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/rainfall-erosivity-european-union-and-switzerland/fuse_schema.json b/src/esdac/rainfall-erosivity-european-union-and-switzerland/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..eff7294617e825e1b30563504b1c0f315290de6d --- /dev/null +++ b/src/esdac/rainfall-erosivity-european-union-and-switzerland/fuse_schema.json @@ -0,0 +1,38 @@ +{ + ".@tif": { + "erosion": { + "r_factor_monthly_JAN_to_DEC (MJ mm ha⁻¹ h⁻¹ month⁻¹)": [ + "12_monthly_R-factors/R-factor January", + "12_monthly_R-factors/R-factor February", + "12_monthly_R-factors/R-factor March", + "12_monthly_R-factors/R-factor April", + "12_monthly_R-factors/R-factor May", + "12_monthly_R-factors/R-factor June", + "12_monthly_R-factors/R-factor July", + "12_monthly_R-factors/R-factor August", + "12_monthly_R-factors/R-factor September", + "12_monthly_R-factors/R-factor October", + "12_monthly_R-factors/R-factor November", + "12_monthly_R-factors/R-factor December" + ], + "r_factor_decadal_1960_to_2020 (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": [ + "PastReconstruction/Decadal_R_maps-1960-2011/Decadal_R_maps/R1961_1970", + "PastReconstruction/Decadal_R_maps-1960-2011/Decadal_R_maps/R1971_1980", + "PastReconstruction/Decadal_R_maps-1960-2011/Decadal_R_maps/R1981_1990", + "PastReconstruction/Decadal_R_maps-1960-2011/Decadal_R_maps/R1991_2000", + "PastReconstruction/Decadal_R_maps-1960-2011/Decadal_R_maps/R2001_2010", + "PastReconstruction/Decadal_R_maps-1960-2011/Decadal_R_maps/R2011_2018" + ], + "r_factor_erosivity_density (MJ ha⁻¹ h⁻¹ per mm precipitation)": "ErosivityDensity/Erosivity_Density", + "r_factor_erosivity_ratio": "MonthlyR_Indicators/Erosivity ratio", + "r_factor_weighted_density": "MonthlyR_Indicators/Weighted Density", + "r_factor_trend_linear (MJ mm ha⁻¹ h⁻¹ per year)": "PastReconstruction/Fig8-Trend/LTrend_cut", + "r_factor_mann_kendall": "PastReconstruction/Fig9-Mann-Kendall/mannk_cut", + "r_factor_total_mean (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": "RTot/Rtot", + "r_factor_total_std (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": "RTot/SError", + "r_factor_annual_mean (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": "R_factorEU/Rf_gp1", + "r_factor_annual_std (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": "StandardError/StandardError", + "r_factor_2050_projection (MJ mm ha⁻¹ h⁻¹ yr⁻¹)": "Rfactor_2050_GPR/2050 GPR" + } + } +} diff --git a/src/esdac/rainfall-erosivity-european-union-and-switzerland/process.py b/src/esdac/rainfall-erosivity-european-union-and-switzerland/process.py new file mode 100644 index 0000000000000000000000000000000000000000..cf2c2dfbc5a97a118e4fb4244a039ba695271dde --- /dev/null +++ b/src/esdac/rainfall-erosivity-european-union-and-switzerland/process.py @@ -0,0 +1,20 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload, unzip_nested +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) +unzip_nested(local_processed_dir) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european/fuse_schema.json b/src/esdac/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..dd2ced7e7a6c7cfa9de012f8eecefc92b4c3b9f6 --- /dev/null +++ b/src/esdac/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european/fuse_schema.json @@ -0,0 +1,9 @@ +{ + "esdac_biomass_production_geotiff/esdac_biomass_production_geotiff@tif": { + "biomass": { + "soil_biomass_productivity_grassland": "sqi_fig4_grass1", + "soil_biomass_productivity_cropland": "sqi_fig5_crop1", + "soil_biomass_productivity_forest": "sqi_fig6_forest1" + } + } +} diff --git a/src/esdac/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european/process.py b/src/esdac/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-bulk-density-europe/fuse_schema.json b/src/esdac/soil-bulk-density-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5277c024abe59728bbd1860da89195e36f141f10 --- /dev/null +++ b/src/esdac/soil-bulk-density-europe/fuse_schema.json @@ -0,0 +1,10 @@ +{ + "Public@tif": { + "mass_density": { + "packing_density (g/cmΒ³)": "packing_density", + "bulk_density_0_10cm (g/cmΒ³)": "BD0_10_GEE_cubist_laea_masked", + "bulk_density_10_20cm (g/cmΒ³)": "BD10_20_GEE_cubist_laea_masked", + "bulk_density (g/cmΒ³)": "mean_BD0_10.BD10_20_GEE_cubist_laea" + } + } +} diff --git a/src/esdac/soil-bulk-density-europe/process.py b/src/esdac/soil-bulk-density-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..d391f5434aba56e137b6d6caf9a9f04981c3450c --- /dev/null +++ b/src/esdac/soil-bulk-density-europe/process.py @@ -0,0 +1,21 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list) +move_list += [local_processed_dir / "bulk_density/bulk_density.csv"] + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-carbon-risk-index/fuse_schema.json b/src/esdac/soil-carbon-risk-index/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f34cd5355b04cb27c0329737392faf6496941093 --- /dev/null +++ b/src/esdac/soil-carbon-risk-index/fuse_schema.json @@ -0,0 +1,73 @@ +{ + "20250326_NComms/Main_outputs/VNIRSOCfractions.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "carbon": { + "organic_carbon_volume_fraction": "as_float(【OC_fractions】)", + "organic_carbon_particulate_matter (g/kg)": "as_float(【OC_pom_g_kg】)", + "organic_carbon_silt_clay (g/kg)": "as_float(【OC_sc_g_kg】)" + } + }, + "20250326_NComms/Files/Inputs/manual_df_Q2_clean.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "carbon": { + "organic_carbon_volume_fraction": "as_float(【OC_fractions】)", + "organic_carbon_particulate_matter (g/kg)": "as_float(【OC_pom_g_kg】)", + "organic_carbon_silt_clay (g/kg)": "as_float(【OC_sc_g_kg】)", + "delta_SOC_GAM (g C kg⁻¹ yr⁻¹)": "as_float(【Delta_GAM_g_kg_y】)" + }, + "mass_density": { + "fine_mass_fraction": "as_float(【fine_fraction】)" + } + }, + "20250326_NComms/Files/Inputs/LCS.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_.*$'", + "climate": { + "mean_annual_temperature (Β°C)": "as_float(【MAT】)", + "mean_annual_precipitation (mm yr⁻¹)": "as_float(【RAIN】)", + "N_deposition_wet_dry (kg N ha⁻¹ yr⁻¹)": "as_float(【Ndep_WD_tx】)" + }, + "erosion": { + "net_erosion_rate (t ha⁻¹ yr⁻¹)": "as_float(【EROS】)" + }, + "hydraulic": { + "water_table_depth (cm)": "as_float(【WT】)" + }, + "carbon": { + "organic_carbon_particulate_matter (g/kg)": "as_float(【OC_pom_g_kg】)", + "organic_carbon_silt_clay (g/kg)": "as_float(【OC_sc_g_kg】)", + "organic_carbon_total (g/kg)": "as_float(【OC_tf】)" + }, + "fertility": { + "N_total (g/kg)": "as_float(【N_tf】)", + "N_particulate_matter (g/kg)": "as_float(【N_pom_g_kg】)", + "N_silt_clay (g/kg)": "as_float(【N_sc_g_kg】)" + } + }, + "20250326_NComms/Files/Literature_data/SITE.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【PointID】) + '_.*$'", + "climate": { + "soil_climate_zone": "【Soil_climate_zone】" + }, + "soil_type": { + "BZE_soil_type": "【Specific soil subtype】" + }, + "land_site": { + "groundwater_class": "【Groundwater class】", + "groundwater_level (cm)": "as_float(【Groundwater level】)", + "thickness_of_fen_or_bog (cm)": "【Thickness of fen or bog】", + "thickness_of_peat (cm)": "【Thickness of peat】" + }, + "carbon": { + "carbon_stock_topsoil (Mg C ha⁻¹)": "as_float(【CS_0_30】)", + "carbon_stock_subsoil (Mg C ha⁻¹)": "as_float(【CS_30_100】)" + } + }, + "20250326_NComms/Files/Inputs@tif": { + "land_degradation": { + "aridity_index_mean_cropland": "aridity_cropped_mean" + }, + "function_suitability": { + "net_primary_productivity_mean_cropland (g C m⁻² yr⁻¹)": "npp_cropped_mean" + } + } +} diff --git a/src/esdac/soil-carbon-risk-index/process.py b/src/esdac/soil-carbon-risk-index/process.py new file mode 100644 index 0000000000000000000000000000000000000000..7cd948866ea484e0380d0fc8c8e71592c95af57b --- /dev/null +++ b/src/esdac/soil-carbon-risk-index/process.py @@ -0,0 +1,29 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_excel + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif and .xlsx files === +move_list = [] + +# Process each .tif file: generate a standardized .tif and preview .png +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# Process each .xlsx file: convert to .csv with optional header detection +for ex in local_processed_dir.rglob("*.xlsx"): + move_list = process_excel(ex, move_list=move_list) + +# Include any standalone CSVs in the upload list +move_list += list(local_processed_dir.rglob("*.csv")) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-degradation-indicators-eu/fuse_schema.json b/src/esdac/soil-degradation-indicators-eu/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b1f1a279f7519d4ec269fe9146023f65370771da --- /dev/null +++ b/src/esdac/soil-degradation-indicators-eu/fuse_schema.json @@ -0,0 +1,13 @@ +{ + "Soil-Degradation-19@tif": { + "land_degradation": { + "soil_degradation_indicator": [ + "multiband", + { + "0": false, + "1": true + } + ] + } + } +} diff --git a/src/esdac/soil-degradation-indicators-eu/process.py b/src/esdac/soil-degradation-indicators-eu/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/soil-degradation-indicators-eu/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-erodibility-k-factor-high-resolution-dataset-europe/fuse_schema.json b/src/esdac/soil-erodibility-k-factor-high-resolution-dataset-europe/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..398414f1972effead159894db63ba84f85794a32 --- /dev/null +++ b/src/esdac/soil-erodibility-k-factor-high-resolution-dataset-europe/fuse_schema.json @@ -0,0 +1,8 @@ +{ + ".@tif": { + "erosion": { + "k_factor_lucas (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "K_factorExtr/K_new", + "k_factor_lucas_with_stoniness (t ha h ha⁻¹ MJ⁻¹ mm⁻¹)": "Kst_Factorextr/Kst_correct" + } + } +} diff --git a/src/esdac/soil-erodibility-k-factor-high-resolution-dataset-europe/process.py b/src/esdac/soil-erodibility-k-factor-high-resolution-dataset-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..fe088b4190f4c11d71ccba490767c8f14f3598ed --- /dev/null +++ b/src/esdac/soil-erodibility-k-factor-high-resolution-dataset-europe/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-erosion-forestland-europe-using-rusle2015/fuse_schema.json b/src/esdac/soil-erosion-forestland-europe-using-rusle2015/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f4221407273f9591b603d3ed51cbfbefc80df9a7 --- /dev/null +++ b/src/esdac/soil-erosion-forestland-europe-using-rusle2015/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "Forest_SoilLoss@tif": { + "erosion": { + "soil_loss_forestland (t ha⁻¹ yr⁻¹)": "RUSLE_EU_Forest" + } + } +} diff --git a/src/esdac/soil-erosion-forestland-europe-using-rusle2015/process.py b/src/esdac/soil-erosion-forestland-europe-using-rusle2015/process.py new file mode 100644 index 0000000000000000000000000000000000000000..94c452087113c42cdc5a976012ab47f96b444d9e --- /dev/null +++ b/src/esdac/soil-erosion-forestland-europe-using-rusle2015/process.py @@ -0,0 +1,21 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF/SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-erosion-water-rusle2015/fuse_schema.json b/src/esdac/soil-erosion-water-rusle2015/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..71fc72a2b6838f532975a997b7ba9bc5aa3b452c --- /dev/null +++ b/src/esdac/soil-erosion-water-rusle2015/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "RUSLE_2016_v2@tif": { + "erosion": { + "soil_loss_by_water (t ha⁻¹ yr⁻¹)": "NEW_RUSLE2016" + } + } +} diff --git a/src/esdac/soil-erosion-water-rusle2015/process.py b/src/esdac/soil-erosion-water-rusle2015/process.py new file mode 100644 index 0000000000000000000000000000000000000000..fe088b4190f4c11d71ccba490767c8f14f3598ed --- /dev/null +++ b/src/esdac/soil-erosion-water-rusle2015/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-function-data-measured-lucas-2018-sites-across-eu/fuse_schema.json b/src/esdac/soil-function-data-measured-lucas-2018-sites-across-eu/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..295e1e1b6860a57ea90b5846b8491a62fdfe5564 --- /dev/null +++ b/src/esdac/soil-function-data-measured-lucas-2018-sites-across-eu/fuse_schema.json @@ -0,0 +1,32 @@ +{ + "LUCAS_2018_soil_functions.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【LUCAS_ID】) + '_.*$'", + "function_suitability": { + "water_holding_capacity (%)": "as_float(【WHC】)", + "mean_weight_diameter_of_aggregates (mm)": "as_float(【Mean_width_diameter】)", + "water_stable_aggregates (%)": "as_float(【Water_stable_aggregates】)", + "xylosidase (Β΅mol g⁻¹ h⁻¹)": "as_float(【Xylosidase】)", + "cellulase (Β΅mol g⁻¹ h⁻¹)": "as_float(【Cellulase】)", + "n_acetylglucosaminidase (Β΅mol g⁻¹ h⁻¹)": "as_float(【N_actylglucosaminidase】)", + "beta_glucosidase (Β΅mol g⁻¹ h⁻¹)": "as_float(【Beta_glucosidase】)", + "acid_phosphatase (Β΅mol g⁻¹ h⁻¹)": "as_float(【Acid_phosphatase】)", + "basal_respiration (Β΅g COβ‚‚ g⁻¹ h⁻¹)": "as_float(【Basal_respiration】)", + "substrate_induced_respiration (Β΅g COβ‚‚ g⁻¹ h⁻¹)": "as_float(【MIRR】)", + "microbial_biomass_carbon (Β΅g C g⁻¹)": "as_float(【Cmic】)", + "metabolic_quotient_qO2 (Β΅g COβ‚‚ Β΅g⁻¹ Cmic h⁻¹)": "as_float(【qO2】)" + }, + "biomass": { + "gram_positive_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Gram_positive】)", + "gram_negative_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Gram_negative】)", + "fungal_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Fungi】)", + "bacterial_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Bacteria】)", + "saturated_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Saturated】)", + "monosaturated_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Monosaturated】)", + "actinobacterial_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Actinobacteria】)", + "total_biomass (nmol FAME g⁻¹ soil)": "as_float(【FAME_Total】)", + "ratio_gram_pos_neg": "as_float(【FAME_ratio_Gram_pos_neg】)", + "ratio_fungi_bacteria": "as_float(【FAME_ratio_Fungi_Bacteria】)", + "ratio_saturated_monosaturated": "as_float(【FAME_ratio_Sat_Monosat】)" + } + } +} diff --git a/src/esdac/soil-function-data-measured-lucas-2018-sites-across-eu/process.py b/src/esdac/soil-function-data-measured-lucas-2018-sites-across-eu/process.py new file mode 100644 index 0000000000000000000000000000000000000000..87e5e5220ea4316e97ab9d110e2f0f69c666d700 --- /dev/null +++ b/src/esdac/soil-function-data-measured-lucas-2018-sites-across-eu/process.py @@ -0,0 +1,17 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, copy_to_local, upload + +# === Step 1: Set up local and drive directory paths === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Copy specified CSV file from drive/download to local/processed === +copy_to_local(local_processed_dir, drive_download_dir, files=["LUCAS_2018_soil_functions.csv"]) + +# === Step 3: Collect all CSV files for upload === +move_list = list(local_processed_dir.rglob("*.csv")) + +# === Step 4: Upload processed files to drive/processed and clean up local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-ghg-fluxes-using-lucas-soil-daycent/fuse_schema.json b/src/esdac/soil-ghg-fluxes-using-lucas-soil-daycent/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a29588fb8a878d7787d4ffeb7f7579f08cbf40e0 --- /dev/null +++ b/src/esdac/soil-ghg-fluxes-using-lucas-soil-daycent/fuse_schema.json @@ -0,0 +1,31 @@ +{ + "BMP_PRJ/BASE/N2O_allAR.csv": { + "ENTRY_KEY": "'^.*_.*_' + as_int_id(【sample_ID】,allow_letters=True) + '$'", + "greenhouse": { + "N2O_flux_mean (kg N ha⁻¹ yr⁻¹)": "as_float(【N2Oavg】)", + "N2O_flux_std (kg N ha⁻¹ yr⁻¹)": "as_float(【N2Osd】)", + "CH4_flux (kg CHβ‚„ ha⁻¹ yr⁻¹)": "as_float(【CH4】)" + }, + "carbon": { + "gross_respiration_2011_to_2014 (g C m⁻² yr⁻¹)": "[as_float(【gr2011】), as_float(【gr2012】), as_float(【gr2013】), as_float(【gr2014】)]", + "SOC_stock_total (Mg C ha⁻¹)": "as_float(【SOCN】)", + "SOC_stock_subsoil (Mg C ha⁻¹)": "as_float(【SOC3】)", + "initial_carbon_concentration (g/kg)": "as_float(【Ci】)" + }, + "function_suitability": { + "net_primary_production_2011 (g C m⁻² yr⁻¹)": "as_float(【NPP2011】)", + "net_primary_production_2012 (g C m⁻² yr⁻¹)": "as_float(【NPP2012】)", + "net_primary_production_2013 (g C m⁻² yr⁻¹)": "as_float(【NPP2013】)", + "net_primary_production_2014 (g C m⁻² yr⁻¹)": "as_float(【NPP2014】)" + }, + "fertility": { + "N_fertilizer_input (kg N ha⁻¹ yr⁻¹)": "as_float(【Nfert】)", + "N_biological_fixation (kg N ha⁻¹ yr⁻¹)": "as_float(【Nfix】)", + "N_deposition (kg N ha⁻¹ yr⁻¹)": "as_float(【Ndep】)" + }, + "management": { + "irrigation (mm yr⁻¹)": "as_float(【irri】)", + "crop_rotation_code": "【rot】" + } + } +} diff --git a/src/esdac/soil-ghg-fluxes-using-lucas-soil-daycent/process.py b/src/esdac/soil-ghg-fluxes-using-lucas-soil-daycent/process.py new file mode 100644 index 0000000000000000000000000000000000000000..5c24abcc06c566765fedf531a0e8a08c9fdca09a --- /dev/null +++ b/src/esdac/soil-ghg-fluxes-using-lucas-soil-daycent/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_dbf + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .dbf files === +move_list = [] +for dbf in local_processed_dir.rglob("*.dbf"): + move_list = process_dbf(dbf, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-loss-due-crop-harvesting-european-union/fuse_schema.json b/src/esdac/soil-loss-due-crop-harvesting-european-union/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a73f8ca52a9cc25447b50f4a457573dd28cd6b48 --- /dev/null +++ b/src/esdac/soil-loss-due-crop-harvesting-european-union/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "SLCH@tif": { + "erosion": { + "soil_loss_by_crop_harvesting (t ha⁻¹ yr⁻¹)": "SLCH" + } + } +} diff --git a/src/esdac/soil-loss-due-crop-harvesting-european-union/process.py b/src/esdac/soil-loss-due-crop-harvesting-european-union/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/soil-loss-due-crop-harvesting-european-union/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-microbial-biomass-and-respiration/fuse_schema.json b/src/esdac/soil-microbial-biomass-and-respiration/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..9487835dc2b0ef920d0d324e8295b91c5a653023 --- /dev/null +++ b/src/esdac/soil-microbial-biomass-and-respiration/fuse_schema.json @@ -0,0 +1,40 @@ +{ + ".@tif": { + "biomass": { + "basal_respiration_general (Β΅L Oβ‚‚ g⁻¹ soil h⁻¹)": "GEB_maps/bas_gen_annual_mean_t.tif", + "basal_respiration_landcover (Β΅L Oβ‚‚ g⁻¹ soil h⁻¹)": "GEB_maps/bas_lc_annual_mean_t", + "microbial_biomass_general (Β΅g Cβ‚˜α΅’c g⁻¹ soil)": "GEB_maps/Cmic_gen_annual_mean_t.tif", + "microbial_biomass_landcover (Β΅g Cβ‚˜α΅’c g⁻¹ soil)": "GEB_maps/Cmic_lc_annual_mean_t.tif", + "respiratory_quotient_general (Β΅L Oβ‚‚ Β΅g⁻¹ Cβ‚˜α΅’c h⁻¹)": "GEB_maps/qO2_annual_mean_gen", + "respiratory_quotient_landcover (Β΅L Oβ‚‚ Β΅g⁻¹ Cβ‚˜α΅’c h⁻¹)": "GEB_maps/qO2_annual_mean_lc.tif", + "basal_respiration_JAN_to_DEC (Β΅L Oβ‚‚ g⁻¹ soil h⁻¹)": [ + "Monthly_Bas_Cmic/untransformed_pred_bas_jan", + "Monthly_Bas_Cmic/untransformed_pred_bas_feb", + "Monthly_Bas_Cmic/untransformed_pred_bas_march", + "Monthly_Bas_Cmic/untransformed_pred_bas_april", + "Monthly_Bas_Cmic/untransformed_pred_bas_may", + "Monthly_Bas_Cmic/untransformed_pred_bas_june", + "Monthly_Bas_Cmic/untransformed_pred_bas_july", + "Monthly_Bas_Cmic/untransformed_pred_bas_aug", + "Monthly_Bas_Cmic/untransformed_pred_bas_sep", + "Monthly_Bas_Cmic/untransformed_pred_bas_oct", + "Monthly_Bas_Cmic/untransformed_pred_bas_nov", + "Monthly_Bas_Cmic/untransformed_pred_bas_dec" + ], + "microbial_biomass_JAN_to_DEC (Β΅g Cβ‚˜α΅’c g⁻¹ soil)": [ + "Monthly_Bas_Cmic/untransformed_pred_cmicjan", + "Monthly_Bas_Cmic/untransformed_pred_cmicfeb", + "Monthly_Bas_Cmic/untransformed_pred_cmicmarch", + "Monthly_Bas_Cmic/untransformed_pred_cmicapril", + "Monthly_Bas_Cmic/untransformed_pred_cmicmay", + "Monthly_Bas_Cmic/untransformed_pred_cmicjune", + "Monthly_Bas_Cmic/untransformed_pred_cmicjuly", + "Monthly_Bas_Cmic/untransformed_pred_cmicaug", + "Monthly_Bas_Cmic/untransformed_pred_cmicsep", + "Monthly_Bas_Cmic/untransformed_pred_cmicoct", + "Monthly_Bas_Cmic/untransformed_pred_cmicnov", + "Monthly_Bas_Cmic/untransformed_pred_cmicdec" + ] + } + } +} diff --git a/src/esdac/soil-microbial-biomass-and-respiration/process.py b/src/esdac/soil-microbial-biomass-and-respiration/process.py new file mode 100644 index 0000000000000000000000000000000000000000..fe088b4190f4c11d71ccba490767c8f14f3598ed --- /dev/null +++ b/src/esdac/soil-microbial-biomass-and-respiration/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-organic-carbon-losses-lateral-transfer-oceans/fuse_skip.txt b/src/esdac/soil-organic-carbon-losses-lateral-transfer-oceans/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/soil-organic-carbon-losses-lateral-transfer-oceans/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/soil-organic-carbon-losses-lateral-transfer-oceans/process.py b/src/esdac/soil-organic-carbon-losses-lateral-transfer-oceans/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/soil-organic-carbon-losses-lateral-transfer-oceans/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-organic-carbon-saturation-capacity/fuse_schema.json b/src/esdac/soil-organic-carbon-saturation-capacity/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..3fbf78802d1321008fe43aaf7baea4dbc4d6f540 --- /dev/null +++ b/src/esdac/soil-organic-carbon-saturation-capacity/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "Carbon_pool/carbon_pool@tif": { + "carbon": { + "SOC_saturation_ratio": "SOCp_all_bil7" + } + } +} diff --git a/src/esdac/soil-organic-carbon-saturation-capacity/process.py b/src/esdac/soil-organic-carbon-saturation-capacity/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/soil-organic-carbon-saturation-capacity/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-organic-carbon-soc-projections-europe/fuse_skip.txt b/src/esdac/soil-organic-carbon-soc-projections-europe/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba243ba69234fff694f83a4fb538f1a70802bb87 --- /dev/null +++ b/src/esdac/soil-organic-carbon-soc-projections-europe/fuse_skip.txt @@ -0,0 +1 @@ +Projection \ No newline at end of file diff --git a/src/esdac/soil-organic-carbon-soc-projections-europe/process.py b/src/esdac/soil-organic-carbon-soc-projections-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..fe088b4190f4c11d71ccba490767c8f14f3598ed --- /dev/null +++ b/src/esdac/soil-organic-carbon-soc-projections-europe/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-organic-matter-som-fractions/fuse_schema.json b/src/esdac/soil-organic-matter-som-fractions/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0a1d72bfc4c3b95bf5c8fdbc41f03dfed52436b8 --- /dev/null +++ b/src/esdac/soil-organic-matter-som-fractions/fuse_schema.json @@ -0,0 +1,13 @@ +{ + "NGEO/SOM_fraction.csv": { + "ENTRY_KEY": "'^.*_' + as_int_id(【POINT_ID】) + '_' + as_int_id(【sample_ID】) + '$'", + "fertility": { + "N_particulate_matter (g/kg)": "as_float(【N_pom_g_kg】)", + "N_mineral_associated (g/kg)": "as_float(【N_sc_g_kg】)" + }, + "carbon": { + "organic_carbon_particulate_matter (g/kg)": "as_float(【OC_pom_g_kg】)", + "organic_carbon_mineral_associated (g/kg)": "as_float(【OC_sc_g_kg】)" + } + } +} diff --git a/src/esdac/soil-organic-matter-som-fractions/process.py b/src/esdac/soil-organic-matter-som-fractions/process.py new file mode 100644 index 0000000000000000000000000000000000000000..3c42e467f7419ba0a7834a29311d499ba1e1df7d --- /dev/null +++ b/src/esdac/soil-organic-matter-som-fractions/process.py @@ -0,0 +1,27 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_excel + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif and .xlsx files === +move_list = [] + +# Process each .tif file: generate a standardized .tif and preview .png +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) +for ex in local_processed_dir.rglob("*.xlsx"): + move_list = process_excel(ex, move_list=move_list) + +# Include any standalone CSVs in the upload list +move_list += list(local_processed_dir.rglob("*.csv")) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-ph-europe/fuse_skip.txt b/src/esdac/soil-ph-europe/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..26c46530aa5fdd8edc8365b5e28e46858676ea7b --- /dev/null +++ b/src/esdac/soil-ph-europe/fuse_skip.txt @@ -0,0 +1 @@ +Used in LUCAS \ No newline at end of file diff --git a/src/esdac/soil-ph-europe/process.py b/src/esdac/soil-ph-europe/process.py new file mode 100644 index 0000000000000000000000000000000000000000..e45b26684fe3ffce7bfe6bea04195b988c4dd7ae --- /dev/null +++ b/src/esdac/soil-ph-europe/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all SHP files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/soil-profile-analytical-database-2/fuse_skip.txt b/src/esdac/soil-profile-analytical-database-2/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..aad0d17ba0d486550df904d954b0945f191503ae --- /dev/null +++ b/src/esdac/soil-profile-analytical-database-2/fuse_skip.txt @@ -0,0 +1 @@ +TBC: good data, but no location \ No newline at end of file diff --git a/src/esdac/soil-profile-analytical-database-2/process.py b/src/esdac/soil-profile-analytical-database-2/process.py new file mode 100644 index 0000000000000000000000000000000000000000..265c20661c03324ee4004e72504a450ab9ea3ce8 --- /dev/null +++ b/src/esdac/soil-profile-analytical-database-2/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_dbf + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all DBF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.dbf")): + move_list = process_dbf(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/spade-14/fuse_skip.txt b/src/esdac/spade-14/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..aad0d17ba0d486550df904d954b0945f191503ae --- /dev/null +++ b/src/esdac/spade-14/fuse_skip.txt @@ -0,0 +1 @@ +TBC: good data, but no location \ No newline at end of file diff --git a/src/esdac/spade-14/process.py b/src/esdac/spade-14/process.py new file mode 100644 index 0000000000000000000000000000000000000000..b23fe8bbaaa0ac2747e7d52bcff30cbc28f0f025 --- /dev/null +++ b/src/esdac/spade-14/process.py @@ -0,0 +1,22 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_excel + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all EXCEL files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.xlsx")): + move_list = process_excel(tif_path, move_list=move_list) +for tif_path in sorted(local_processed_dir.rglob("*.xls")): + move_list = process_excel(tif_path, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/spadem/fuse_schema.json b/src/esdac/spadem/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..be0fc28aa785e586f1b9d0c1fb2cd1f5d697c516 --- /dev/null +++ b/src/esdac/spadem/fuse_schema.json @@ -0,0 +1,85 @@ +{ + "DAT_HOR_with_coords.csv": { + "ENTRY_KEY": "'_'.join(['SPADEM', as_int_id(【PLOT_ID】), as_int_id(【HOR_ID】)])", + "LAT_LONG": "(as_float(【LAT_COOR_V】), as_float(【LON_COOR_V】))", + "COUNTRY_CODE": "【CNTY_C】", + "SAMPLE_DATE": "'XX-XX-2004'", + "SAMPLE_DEPTH_RANGE_CM": "[as_float(【HOR_BEG_V】), as_float(【HOR_END_V】)]", + "texture": { + "clay_percentage (%)": "as_float(【CLAY_V】)", + "silt_fine_percentage (%)": "as_float(【SILT1_V】)", + "silt_coarse_percentage (%)": "as_float(【SILT2_V】)", + "sand_fine_percentage (%)": "as_float(【SAND1_V】)", + "sand_intermediate_percentage (%)": "as_float(【SAND2_V】)", + "sand_coarse_percentage (%)": "as_float(【SAND3_V】)", + "silt_percentage (%)": "as_float(【SILT1_V】) + as_float(【SILT2_V】)", + "sand_percentage (%)": "as_float(【SAND1_V】) + as_float(【SAND2_V】) + as_float(【SAND3_V】)", + "clay_upper_diameter (Β΅m)": "as_float(【CLAY_ESD】)", + "silt_fine_upper_diameter (Β΅m)": "as_float(【SILT1_ESD】)", + "silt_coarse_upper_diameter (Β΅m)": "as_float(【SILT2_ESD】)", + "sand_fine_upper_diameter (Β΅m)": "as_float(【SAND1_ESD】)", + "sand_intermediate_upper_diameter (Β΅m)": "as_float(【SAND2_ESD】)", + "sand_coarse_upper_diameter (Β΅m)": "as_float(【SAND3_ESD】)" + }, + "soil_type": { + "gravel_type": "resolve_code(【GRAV_C】, codebook='spade', top_key='gravel_type')", + "structure_type": "resolve_code(【STRU_C】, codebook='spade', top_key='structure_type')" + }, + "carbon": { + "organic_carbon_content (g/kg)": "as_float(【OC_V】)", + "CaCO3_content (g/kg)": "as_float(【CACO3_V】)", + "CaSO4_content (g/kg)": "as_float(【CASO4_V】)" + }, + "organic_matter": { + "organic_matter_content (g/kg)": "as_float(【OM_V】)" + }, + "fertility": { + "N_content (g/kg)": "as_float(【N_V】)", + "Ca_exchangeable (cmol(+)/kg)": "as_float(【EXC_CA_V】)", + "Mg_exchangeable (cmol(+)/kg)": "as_float(【EXC_MG_V】)", + "Ca_Mg_sum_exchangeable (cmol(+)/kg)": "as_float(【EXC_CAMG_V】)", + "K_exchangeable (cmol(+)/kg)": "as_float(【EXC_K_V】)", + "Na_exchangeable (cmol(+)/kg)": "as_float(【EXC_NA_V】)", + "cation_exchange_capacity (cmol(+)/kg)": "as_float(【CEC_V】)" + }, + "chemical": { + "pH_in_H2O": "as_float(【PH_V】) if 【PH_M】 not in ['A13', 'A14'] else None", + "pH_in_KCl": "as_float(【PH_V】) if 【PH_M】 in ['A14'] else None", + "pH_in_CaCl2": "as_float(【PH_V】) if 【PH_M】 in ['A13'] else None", + "electrical_conductivity (mS/m)": "as_float(【EC_V】, factor=10)", + "Na_adsorption_ratio": "as_float(【SAR_V】)" + }, + "hydraulic": { + "base_saturation (%)": "as_float(【BS_V】)", + "water_content_at_suctions (cmΒ³ cm⁻³)": "[as_float(【WC1_V】, 0.01), as_float(【WC2_V】, 0.01), as_float(【WC3_V】, 0.01), as_float(【WC4_V】, 0.01)]", + "suctions_for_water_content_measurement (kPa)": "[as_float(【WC1_KPA】), as_float(【WC2_KPA】), as_float(【WC3_KPA】), as_float(【WC4_KPA】)]", + "field_capacity_water_content (cmΒ³ cm⁻³)": "as_float(【WCFC_V】, 0.01)", + "suction_for_field_capacity_measurement (kPa)": "as_float(【WCFC_KPA】)" + }, + "mass_density": { + "porosity_volume_percentage (%)": "as_float(【POR_V】)", + "bulk_density (g/cmΒ³)": "as_float(【BD_V】)" + } + }, + "DAT_PLOT.csv": { + "ENTRY_KEY": "'^' + '_'.join(['SPADEM', as_int_id(【PLOT_ID】), '.*$'])", + "topography_geology": { + "elevation (m)": "as_float(【ALT_V】)", + "dominant_parent_material": "【PM_NAME】", + "depth_to_root_limiting_layer (cm)": "as_float(【D_ROO_V】)", + "depth_to_continuous_rock_layer (cm)": "as_float(【D_ROC_V】)", + "depth_to_other_restrictive_layer (cm)": "as_float(【D_OTH_V】)" + }, + "soil_type": { + "soil_name_SPADE": "【SOIL_NAME】", + "WRB_soil_group": "resolve_code(【SOIL_C】, codebook='spade', top_key='WRB_soil_group', ignore_error_codes=['zzz'])" + }, + "land_site": { + "groundwater_table_level_normal (cm)": "as_float(【GWL_NM_V】)", + "groundwater_table_level_normal_class": "resolve_code(【GWL_NM_C】, codebook='spade', top_key='groundwater_table_level')", + "groundwater_table_level_high_class": "resolve_code(【GWL_HI_C】, codebook='spade', top_key='groundwater_table_level')", + "groundwater_table_level_low_class": "resolve_code(【GWL_LO_C】, codebook='spade', top_key='groundwater_table_level')", + "land_use_primary": "resolve_code(【LU_CLC_C】, codebook='lucas_lu')" + } + } +} diff --git a/src/esdac/spadem/process.py b/src/esdac/spadem/process.py new file mode 100644 index 0000000000000000000000000000000000000000..f4fd34eaaef76b99fceea2038b52ffeb4018cca0 --- /dev/null +++ b/src/esdac/spadem/process.py @@ -0,0 +1,79 @@ +import subprocess +from io import StringIO +from pathlib import Path + +import pandas as pd + +from src.process.io import prepare_dirs, unzip, upload + +# === Step 1: Set up directory paths === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip MDB archive from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=["SPADE_M_v2.zip"]) + +# === Step 3: Extract table names from the MDB file === +mdb_path = local_processed_dir / "SPADE_M_v2/SPADE_M_v2.mdb" +tables_output = subprocess.check_output(["mdb-tables", "-1", mdb_path]) +table_names = [t for t in tables_output.decode().strip().split("\n") if t.strip()] + +# === Step 4: Export each table to CSV === +move_list = [] +for table in table_names: + try: + print(f"πŸ“„ Exporting table: {table}") + csv_output = subprocess.check_output(["mdb-export", mdb_path, table]) + df = pd.read_csv(StringIO(csv_output.decode()), low_memory=False) + + csv_path = local_processed_dir / f"{table}.csv" + df.to_csv(csv_path, index=False) + move_list.append(csv_path) + print(f"βœ… Saved {csv_path.name}") + + except subprocess.CalledProcessError as e: + print(f"❌ Failed to export table {table}: {e}") + +# === Step 5: Broadcast lat long +plot_csv = local_processed_dir / "DAT_PLOT.csv" +hor_csv = local_processed_dir / "DAT_HOR.csv" + +# Load both CSVs +df_plot = pd.read_csv(plot_csv) +df_hor = pd.read_csv(hor_csv) + +# === Remove corrupted plots === +bad_plots = [193, 197, 391] # bad lat, long +df_plot = df_plot[~df_plot["PLOT_ID"].isin(bad_plots)] +df_hor = df_hor[~df_hor["PLOT_ID"].isin(bad_plots)] +print(f"πŸ—‘οΈ Removed bad PLOT_IDs {bad_plots} from both datasets") + +# Merge coordinates into horizon data +df_hor = df_hor.merge( + df_plot[["PLOT_ID", "LON_COOR_V", "LAT_COOR_V", "CNTY_C"]], + on="PLOT_ID", + how="left" +) + +# Fill NaN with 0 in selected columns before saving +cols_to_fill = [ + "CLAY_V", + "SILT1_V", + "SILT2_V", + "SAND1_V", + "SAND2_V", + "SAND3_V", +] + +for col in cols_to_fill: + if col in df_hor.columns: + df_hor[col] = df_hor[col].fillna(0) + +# Save to the same directory +out_path = hor_csv.parent / "DAT_HOR_with_coords.csv" +df_hor.to_csv(out_path, index=False) +move_list.append(out_path) + +# === Step 6: Upload to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/status.json b/src/esdac/status.json new file mode 100644 index 0000000000000000000000000000000000000000..0296e1047cdb717e4f0d1c390880315295a04d4b --- /dev/null +++ b/src/esdac/status.json @@ -0,0 +1,1550 @@ +[ + { + "name": "3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution", + "title": "3D Soil Hydraulic Database of Europe at 1 km and 250 m resolution", + "url": "https://esdac.jrc.ec.europa.eu/content/3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A consistent spatial soil hydraulic database at 7 soil depths up to 2 m calculated for Europe based on SoilGrids250m and 1 km datasets and pedotransfer functions trained on the European Hydropedological Data Inventory. Saturated water content, water content at field capacity and wilting point, saturated hydraulic conductivity and Mualem-van Genuchten parameters for the description of the moisture retention, and unsaturated hydraulic conductivity curves have been predicted. The derived 3D soil hydraulic layers (EU-SoilHydroGrids ver1.0) can be used for environmental modelling purposes at catchment or continental scale in Europe. Currently, only EU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties with full European coverage up to 2 m depth at 250 m resolution." + }, + { + "name": "arsenic-european-topsoils", + "title": "Arsenic in European topsoils", + "url": "https://esdac.jrc.ec.europa.eu/content/arsenic-european-topsoils", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Maps of Arsenic (As) in EU topsoils at 250m resolution. In addition, we provide the exceedance probabilities for the threshold of 1.5, 20 and 45 mg kg-1 . We also provide comparison with GEMAS data on As." + }, + { + "name": "bacterial-fungal-biomass-fatty-acid-methyl-esters", + "title": "Bacterial & fungal biomass (fatty acid methyl esters)", + "url": "https://esdac.jrc.ec.europa.eu/content/bacterial-fungal-biomass-fatty-acid-methyl-esters", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Land-use- and climate-mediated variations in soil bacterial and fungal biomass across Europe and their driving factors. These are data accompanying a study that was carried out by a number of researchers from various organizations, including staff from the European Commission Joint Research Centre" + }, + { + "name": "biochar-meta-analysis-database", + "title": "Biochar Meta-analysis Database", + "url": "https://esdac.jrc.ec.europa.eu/content/biochar-meta-analysis-database", + "request_needed": false, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Data (2011) associated to the publication \"A quantitative review of the effects of biochar application to soils on crop productivity using meta-analysis. Agriculture, Ecosystems and Environment. Volume 144, Issue 1, November 2011, Pages 175-187\"" + }, + { + "name": "biodiversity-factor-soil-erosion", + "title": "Biodiversity factor in soil erosion", + "url": "https://esdac.jrc.ec.europa.eu/content/biodiversity-factor-soil-erosion", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A biological factor to be included in soil erosion modelling. The available data for Earthwork diversity (richness and abundance) introduced a new \"earthworm factor\" to be incorporated in soil erosion modelling." + }, + { + "name": "cadmium-topsoils-european-union", + "title": "Cadmium in topsoils of the European Union", + "url": "https://esdac.jrc.ec.europa.eu/content/cadmium-topsoils-european-union", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Cadmium content (mg kg-1) in the European Union topsoils applying an ensemble of machine learning models. The map of Cd distribution at a resolution of 100 m." + }, + { + "name": "caesium-137-and-plutonium-239240-european-topsoils", + "title": "Caesium-137 and Plutonium-239+240 in European topsoils", + "url": "https://esdac.jrc.ec.europa.eu/content/caesium-137-and-plutonium-239240-european-topsoils", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Plutonium and Cesium inventories in European topsoils. Includes also the Chernobyl-derived 137Cs and Global-derived 137Cs" + }, + { + "name": "carbon-budget-eu-agricultural-soils", + "title": "Carbon budget in the EU agricultural soils", + "url": "https://esdac.jrc.ec.europa.eu/content/carbon-budget-eu-agricultural-soils", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Cumulative C budget over the period 2016-20100 in the EU agricultural soils under the accelerated and current soil erosion scenarios." + }, + { + "name": "carbon-mitigation-potential-and-radiative-agricultural-land-management", + "title": "Carbon mitigation potential and radiative agricultural land management", + "url": "https://esdac.jrc.ec.europa.eu/content/carbon-mitigation-potential-and-radiative-agricultural-land-management", + "request_needed": true, + "status": "SKIPPED", + "notes": "Contains only long-term scenario projections (2030–2100)", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Maximising climate mitigation potential by carbon and radiative agricultural land management with cover crops" + }, + { + "name": "chemical-properties-european-scale-based-lucas-topsoil-data", + "title": "Maps of Soil Chemical properties at European scale based on LUCAS 2009/2012 topsoil data", + "url": "https://esdac.jrc.ec.europa.eu/content/chemical-properties-european-scale-based-lucas-topsoil-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This group of datasets contains 8 chemical properties: pH, pH (CaCl), Cation Exchange Capacity (CEC), Calcium carbonates (CaCO3), C:N ratio, Nitrogen (N), Phosphorus (P) and Potassium (K) using soil point data from the LUCAS 2009/2012 soil surveys (around 22,000 points) for EU-26 (not included Cyprus and Croatia). The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. Resolution: 500m. Format: TIFF; projection information: ETRS89 / LAEA Europe" + }, + { + "name": "clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2", + "title": "Clay mineral inventory in soils of Europe based on LUCAS 2015 survey soil samples", + "url": "https://esdac.jrc.ec.europa.eu/content/clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "These data are the input for the report \"Clay mineral inventory in soils of Europe based on LUCAS 2015 survey soil samples\" (2024). Clay minerals are a key factor in mineral soils as they are controlling physic, chemical and biological soil properties. The X-ray diffraction (XRD) analysis has been widely used to identify and quantify minerals in earth science The aim of the research reported is to describe the clay minerals in soils of Europe and United Kingdom by using soil samples from the Land Use/Cover Area Frame Survey (LUCAS) topsoil database sampled in 2015. A subset of 388 soil samples were selected from LUCAS 2015 topsoil survey." + }, + { + "name": "climate-change-regional-indicators-change-and-extremes-europe", + "title": "Climate Change Regional Indicators of Change and Extremes for Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/climate-change-regional-indicators-change-and-extremes-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Indicators of climate change and extremes from regional models coming from 3 sources: PRUDENCE project of the Danish Meteorological Institute, consortial simulation of the Climate Limited-area Modelling" + }, + { + "name": "copper-distribution-topsoils", + "title": "Copper distribution in topsoils in the European Union", + "url": "https://esdac.jrc.ec.europa.eu/content/copper-distribution-topsoils", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Copper distribution in European Union topsoils based on LUCAS points. The data are at 500m resolution and have been the result of advanced interpolation modelling." + }, + { + "name": "cover-crops-accross-europe", + "title": "Cover Crops across Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/cover-crops-accross-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Disaggregated map of cover crops occurrence for Europe and the United Kingdom. Using a disaggregation model, we combined satellite data (Sentinel-1) with aggregated survey data to generate a high-resolution map (100m) of cover crops for Europe and the United Kingdom for the reference year of 2016." + }, + { + "name": "cover-management-factor-c-factor-eu", + "title": "Cover Management factor (C-factor) for the EU", + "url": "https://esdac.jrc.ec.europa.eu/content/cover-management-factor-c-factor-eu", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset (GIS map) (2015) represents the Cover Management factor (C-factor), one of the input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28; resolution 100m. The C-factor was estimated for a) arable lands based on crop composition and for b) all other land uses (non-arable) based on the vegetation density and land cover type. The management practices (reduced tillage/no till, plant residues and winter cover crops) were taken into account in estimating C-factor in arable lands." + }, + { + "name": "database-eu-research-projects-soils", + "title": "Database of EU Research projects in soils", + "url": "https://esdac.jrc.ec.europa.eu/content/database-eu-research-projects-soils", + "request_needed": false, + "status": "SKIPPED", + "notes": "Dataset contains only metadata about funded research projects. No soil property, spatial, or measurement data is included.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Collection of EU Research projects in soils during the last forty years (n = 1101 projects) funded by the successive European Commission Framework Programs (FP) for research and innovation (from FP1 to H2020)" + }, + { + "name": "desertification-risk-greece", + "title": "Desertification risk in Greece", + "url": "https://esdac.jrc.ec.europa.eu/content/desertification-risk-greece", + "request_needed": false, + "status": "SKIPPED", + "notes": "No relevant soil data; One country", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "Qualitative assessment on desertification risk in Greece based on Environmentally Sensitive Areas (ESAs) data of MEDALUS methodology" + }, + { + "name": "dna-metagenomes-soil-biodiversity", + "title": "DNA metagenomes - Soil biodiversity", + "url": "https://esdac.jrc.ec.europa.eu/content/dna-metagenomes-soil-biodiversity", + "request_needed": true, + "status": "SKIPPED", + "notes": "LUCAS-based; Skipped because 1) too few samples; 2) difficult for fusion", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "The dataset includes raw DNA sequences for 630 samples collected as part of the LUCAS 2018 Soil survey for the biodiversity module. The dataset includes DNA reads annotated as antibiotic resistance and biosynthesis genes (ARG and ABG, respectively) revealed in the metagenomes from 630 soil samples collected during the LUCAS 2018 Soil survey" + }, + { + "name": "ecalp-ecopedological-map-alps", + "title": "ECALP: Ecopedological Map of the Alps", + "url": "https://esdac.jrc.ec.europa.eu/content/ecalp-ecopedological-map-alps", + "request_needed": false, + "status": "SKIPPED", + "notes": "No soil data", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": null + }, + { + "name": "erosion-potential-method-epm", + "title": "Erosion potential method (EPM)", + "url": "https://esdac.jrc.ec.europa.eu/content/erosion-potential-method-epm", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Global gross soil erosion rates according to the Erosion Potential Model (EPM) and its modified version (mEPM) and the estimated Z factor (Classification of soil erosion classes) according to the Copernicus Land Cover data source classification." + }, + { + "name": "estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union", + "title": "Net erosion and sediment transport using WaTEM/SEDEM (for EU)", + "url": "https://esdac.jrc.ec.europa.eu/content/estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Dataset (GIS map) (218) that shows the net soil erosion and deposition in European Union as a result of an application WaTEM/SEDEM model; resolution 100m; EU28. Data are also available at 25m resolution and at catchment scale." + }, + { + "name": "european-food-safety-authority-efsa-data-persam-software-tool", + "title": "European Food Safety Authority (EFSA) Data & PERSAM software tool", + "url": "https://esdac.jrc.ec.europa.eu/content/european-food-safety-authority-efsa-data-persam-software-tool", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Data and software made public as the result of a collaboration between JRC and EFSA. It contains soil, weather and crop data collected by JRC and organised by EFSA for its purpose. The site also hosts the software tool PERSAM for Predicting Environmental Concentrations in soil in support of the: \"EFSA Guidance Document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil\" which EFSA was asked by the European Commission to prepare. The data/maps are provided as rasters (ASCII grid). The soil data are: Topsoil Organic Matter content, Topsoil pH water, Topsoil Bulk Density, Topsoil Texture Class, Topsoil Water Content at Field Capacity." + }, + { + "name": "european-hydropedological-data-inventory-eu-hydi-database-0", + "title": "European Hydropedological Data Inventory (EU-HYDI) database", + "url": "https://esdac.jrc.ec.europa.eu/content/european-hydropedological-data-inventory-eu-hydi-database-0", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "The European Hydropedological Data Inventory (EU-HYDI) contains soil properties with a special focus on hydrological properties. It also includes various other measured soil characteristics associated to the same samples. It can hence serve multiple purposes, including scientific research, modelling and application of models at different geographical scales. The dataset consists of 10 interlinked CSV files." + }, + { + "name": "european-landslide-susceptibility-map-elsus-v2", + "title": "European Landslide Susceptibility Map version 2 (ELSUS v2)", + "url": "https://esdac.jrc.ec.europa.eu/content/european-landslide-susceptibility-map-elsus-v2", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "The spatial dataset (GIS map) shows landslide susceptibility levels at European scale, derived from heuristic-statistical modelling of main landslide conditioning factors using also landslide location data. It covers all EU member states except Malta, in addition to Albania, Andorra, Bosnia and Herzegovina, Croatia, FYR Macedonia, Iceland, Kosovo, Liechtenstein, Montenegro, Norway, San Marino, Serbia, and Switzerland. This dataset is available together with ancillary spatial datasets" + }, + { + "name": "european-map-soil-suitability-provide-platform-most-human-activities-eu28", + "title": "European map of soil suitability to provide a platform for most human activities (EU28)", + "url": "https://esdac.jrc.ec.europa.eu/content/european-map-soil-suitability-provide-platform-most-human-activities-eu28", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset (map)(2016) presents the suitability of soil as a platform for most human activities in the EU. Calculation of suitability was done using vaious properties of the European Soil database (soil type, soil water regime, limitation to agricultural use, depth to rock, land use) and slope of the terrain." + }, + { + "name": "european-reference-grids", + "title": "European Reference Grids", + "url": "https://esdac.jrc.ec.europa.eu/content/european-reference-grids", + "request_needed": false, + "status": "SKIPPED", + "notes": "No data", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": null + }, + { + "name": "european-soil-database-derived-data", + "title": "European Soil Database Derived data", + "url": "https://esdac.jrc.ec.europa.eu/content/european-soil-database-derived-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A number of layers for soil properties have been created based on data from the European Soil Database in combination with data from the Harmonized World Soil Database (HWSD) and Soil-Terrain Database (SOTER)." + }, + { + "name": "european-soil-database-v2-raster-library-10kmx10km", + "title": "European Soil Database v2 Raster Library 10kmx10km", + "url": "https://esdac.jrc.ec.europa.eu/content/european-soil-database-v2-raster-library-10kmx10km", + "request_needed": false, + "status": "SKIPPED", + "notes": "Vector format available", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": null + }, + { + "name": "european-soil-database-v2-raster-library-1kmx1km", + "title": "European Soil Database v2 Raster Library 1kmx1km", + "url": "https://esdac.jrc.ec.europa.eu/content/european-soil-database-v2-raster-library-1kmx1km", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This database (2024) is a new set of raster data (GeoTIFF) that have been derived from the European soil Database v2, for most attributes. The values for the attributes are categorized (non-continuous). These rasters are an interpretation of the data that are contained in the ESDB v2.0." + }, + { + "name": "european-soil-database-v20-vector-and-attribute-data", + "title": "European Soil Database v2.0 (vector and attribute data)", + "url": "https://esdac.jrc.ec.europa.eu/content/european-soil-database-v20-vector-and-attribute-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This database (2004) is the only harmonized soil database for Europe, extending also to Eurasia. It contains a soil geographical database SGDBE (polygons) to which a number of essential soil attributes are attached, and an associate database PTRDB, with attributes which values have been derived through pedotransfer rules. Also part of the database is the Soil Profile Analytical Database, that contains measured and estimated soil profiles for Europe." + }, + { + "name": "EUSEDcollab", + "title": "EUropean SEDiments collaboration (EUSEDcollab) database", + "url": "https://esdac.jrc.ec.europa.eu/content/EUSEDcollab", + "request_needed": true, + "status": "SKIPPED", + "notes": "Country-level resolution", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "The EUropean SEDiments collaboration (EUSEDcollab) database contains a compilation of data contributions from monitored catchments from across multiple European institutions" + }, + { + "name": "g2-soil-erosion", + "title": "G2 soil erosion model data", + "url": "https://esdac.jrc.ec.europa.eu/content/g2-soil-erosion", + "request_needed": true, + "status": "SKIPPED", + "notes": "Small region", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "G2 generic model for soil erosion applied to 5 application areas (Crete island,Cyprus, Ishmi-Erzeni watershed, Korce, Strymonas/Struma); available layers: Soil erosion (Total & Monthly) plus Rainfall erosivity (Total & Monthly), vegetation retention (Total & Monthly), soil erodibility, topographic influence and slope intercept." + }, + { + "name": "global-applications-soil-erosion-modelling-tracker", + "title": "Global Applications of Soil Erosion Modelling Tracker", + "url": "https://esdac.jrc.ec.europa.eu/content/global-applications-soil-erosion-modelling-tracker", + "request_needed": false, + "status": "SKIPPED", + "notes": "A bibliographic meta-database only; it does not contain real soil or sediment data", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Global Applications of Soil Erosion Modelling Tracker (GASEMT) includes 3030 individual modelling records from 126 countries, encompassing all continents. GASEMT includes peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017." + }, + { + "name": "global-landform-classification", + "title": "Global Landform classification", + "url": "https://esdac.jrc.ec.europa.eu/content/global-landform-classification", + "request_needed": true, + "status": "SKIPPED", + "notes": "Although not soil property data, landform classification might support soil-related modeling and terrain analysis. Kuangdai: skipped due to tricky old format", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "This dataset (GIS maps) represent global lanform classification calculated by the JRC, according to 1) Meybeck et al., presenting relief classes, which are calculated based on the relief roughness, and 2) Iwahashi and Pike, presenting relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature." + }, + { + "name": "global-phosphorus-losses-due-soil-erosion", + "title": "Global phosphorus losses due to soil erosion", + "url": "https://esdac.jrc.ec.europa.eu/content/global-phosphorus-losses-due-soil-erosion", + "request_needed": true, + "status": "SKIPPED", + "notes": "Modelled phosphorus loss data at coarse scale, not suitable for detailed soil analysis, more for global erosion nutrient cycling studies.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Global average phosphorus (P) losses due to soil erosion in kg ha\u22121 yr\u22121. Thus we combine the most recent spatially distributed global soil erosion estimates with global P content of cropland soils (data also available for P content)" + }, + { + "name": "global-rainfall-erosivity", + "title": "Global Rainfall Erosivity", + "url": "https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity", + "request_needed": true, + "status": "PROCESSED", + "notes": "Although not a soil dataset, it provides essential R-factor input for soil erosion modelling; no direct soil properties.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk estimation; for the whole World; R-factor map at resolutions of 30 arc-sec ((~1 km at the Equator)." + }, + { + "name": "global-rainfall-erosivity-projections-2050-and-2070", + "title": "Global rainfall erosivity projections for 2050 and 2070", + "url": "https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity-projections-2050-and-2070", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "We make available both the aggregated data (2050 and 2070 Rainfall erosivity for 3 scenarios) and the 102 contributing simulations (3 scenarios x 19 models x 2 time-scale) in a GEOTIFF format with a 30 arc-seconds spatial resolution." + }, + { + "name": "global-soil-biodiversity-maps-0", + "title": "Global Soil Biodiversity Atlas Maps", + "url": "https://esdac.jrc.ec.europa.eu/content/global-soil-biodiversity-maps-0", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Dataset (2016) containing 2 GIS maps from the Global Soil Biodiversity Atlas: 1) the Soil Biodiversity map showing a simple index describing the potential level of diversity living in soils (with the use of two other datasets: distribution of microbial soil carbon used as a proxy for soil microbial diversity, and the distribution of the main groups of soil macrofauna used as a proxy for soil fauna diversity. 2) the Soil Biodiversity threats showing the potential rather than the actual level of threat to soil organisms. For the development of this map, a number of diverse threats and corresponding proxies were chosen." + }, + { + "name": "global-soil-erodibility", + "title": "Global Soil Erodibility", + "url": "https://esdac.jrc.ec.europa.eu/content/global-soil-erodibility", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset encompasses global soil erodibility (K) factor maps, with the K factor being estimated through the Wischmeier and Smith (1978) method. In addition, measured values of saturated hydraulic conductivity (Ksat) have been incorporated into the original method to formulate the Ksat-based soil erodibility (Kksat factor) map. A third k-factor dataset which was included in the GloSEM (Borrelli et al., 2017) is also included." + }, + { + "name": "global-soil-erosion", + "title": "Global Soil Erosion", + "url": "https://esdac.jrc.ec.europa.eu/content/global-soil-erosion", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Global Soil Erosion is a re-sampled dataset (25km) of the original Global Soil Erosion map. Both the 2012 and 2001 datasets are provided. The data package includes also the input layers (K, LS, R, C) at 25km resolution and a sample area (in Amazon rainforest) at the original resolution of 250m" + }, + { + "name": "global-soil-erosion-water-2070", + "title": "Global soil erosion by water in 2070", + "url": "https://esdac.jrc.ec.europa.eu/content/global-soil-erosion-water-2070", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Land use and climate change impacts on global soil erosion by water (2015-2070). This dataset includes the baseline scenario (2015) and the future projections (2070) of soil erosion based on land use changes and climate change effects." + }, + { + "name": "global-soil-organic-carbon-estimates", + "title": "Global Soil Organic Carbon Estimates", + "url": "https://esdac.jrc.ec.europa.eu/content/global-soil-organic-carbon-estimates", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Global estimates of soil organic carbon stocks have been produced in the past to support the calculation of potential emissions of CO2 from the soil under scenarios of change land use/cover and climatic conditions." + }, + { + "name": "global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due", + "title": "Global Spatial Layers for Estimating Soil GHG Emissions from Indirect Land Use Changes(ILUC) due to the Production of Biofuels", + "url": "https://esdac.jrc.ec.europa.eu/content/global-spatial-layers-estimating-soil-ghg-emissions-indirect-land-use-changesiluc-due", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "The data made available on these pages are referred to in the Guide for Calculation of Carbon Stock Changes in Soil and Above and Below Ground Vegetation due to Land Use Conversion, which was prepared in support to Commission Decision" + }, + { + "name": "gloreda", + "title": "GloREDa", + "url": "https://esdac.jrc.ec.europa.eu/content/gloreda", + "request_needed": true, + "status": "SKIPPED", + "notes": "Using GloRESatE instead, as it provides more recent, higher-resolution, and integrated rainfall erosivity data.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Global Rainfall Erosivity Database (GloREDa) includes point R-factor data from almost 3,939 stations in 65 countries worldwide b) monthly erosivity values and c) Monthly erosivity maps" + }, + { + "name": "gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates", + "title": "GloRESatE - Global Rainfall Erosivity from Reanalysis and Satellite Estimates", + "url": "https://esdac.jrc.ec.europa.eu/content/gloresate-global-rainfall-erosivity-reanalysis-and-satellite-estimates", + "request_needed": false, + "status": "PROCESSED", + "notes": "Although not soil property data, GloRESatE provides critical rainfall erosivity input essential for soil erosion modeling (e.g., RUSLE), supporting integration with soil datasets", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset contains merged global rainfall erosivity product at 0.1\u00b0 \u00d7 0.1\u00b0 spatial resolution. The dataset is an integrated product of satellite (CMORPH, IMERG Final Run) and reanalysis (ERA5-Land) estimates with gauge rainfall erosivity datasets 6,170 gauge stations across the globe. The dataset has been given in the name of \u201cGloRESatE\u201d." + }, + { + "name": "glosem", + "title": "High resolution cropland global soil erosion (GloSEM 1.3)", + "url": "https://esdac.jrc.ec.europa.eu/content/glosem", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Global Soil Erosion Modelling (GloSEM) platform containts 4 datasets on global soil erosion in arable lands (at 100m resolution): Baseline data (2019), soil loss in 2070 for three climatic scenarios RCP2.6, RCP4.5, RCP8.5" + }, + { + "name": "google-earth-files", + "title": "Google Earth Files", + "url": "https://esdac.jrc.ec.europa.eu/content/google-earth-files", + "request_needed": false, + "status": "SKIPPED", + "notes": "Only for visualization", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "Google Earth Files (with \".kmz\" extension) that correspond to 73 attribute maps derived from the European Soil Database v2 (ESDB v2) for EU27 countries." + }, + { + "name": "groundwater-resources-maps-europe-0", + "title": "Groundwater Resources maps of Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/groundwater-resources-maps-europe-0", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "GIS Maps related to Groundwater resources in Europe, covering 3 themes: Inventory of aquifers; Hydrogeology of aquifers; Groundwater abstraction; for 9 European countries (Belgium, Federal Republic of Germany, Denmark, France, Ireland, Italy, Luxembourg, Netherlands and United Kingdom)" + }, + { + "name": "gully-erosion-based-lucas", + "title": "Gully Erosion based on LUCAS", + "url": "https://esdac.jrc.ec.europa.eu/content/gully-erosion-based-lucas", + "request_needed": false, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "The LUCAS topsoil survey 2018 included a module for visual assessment of gully erosion. Gully erosion channels were detected in 1% of the visited LUCAS topsoil sites. Find the 211 points declared as gully erosion." + }, + { + "name": "heavy-metals-topsoils", + "title": "Heavy Metals in topsoils", + "url": "https://esdac.jrc.ec.europa.eu/content/heavy-metals-topsoils", + "request_needed": false, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "GIS Maps (2008) produced by mapping the concentrations of eight critical heavy metals (arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc) using the 1588 georeferenced topsoil samples from the FOREGS Geochemical database. The concentrations were interpolated using block regression-kriging over the 26 European countries that contributed to the database." + }, + { + "name": "historical-reconstruction-soil-erosion-europe", + "title": "Historical reconstruction of soil erosion in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/historical-reconstruction-soil-erosion-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "This is a modelled soil erosion dataset, not containing direct soil properties", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "The past soil erosion rates have been reconstructed by applying the CE-DYNAM model. We have estimated annual losses by water erosion from 1860-2018; here, we present the average loss per decade (16 Geotiff files). The annual and monthly soil erosion reconstruction data (1860-2018) can be useful inputs for Earth System Models." + }, + { + "name": "isoil-project-interactions-between-soil-related-sciences", + "title": "iSOIL project (Interactions between soil related sciences)", + "url": "https://esdac.jrc.ec.europa.eu/content/isoil-project-interactions-between-soil-related-sciences", + "request_needed": true, + "status": "SKIPPED", + "notes": "Nine fields only", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Data from the FP7 iSOIL research projectdealing with new technologies for assessing soil properties along depth accurately and with high resolution by combining existing sensors as well as exploring emerging technologies (mainly geophysical); data available for the test sites of iSoil, located in Germany, Austria, Czech Republic and Bulgaria." + }, + { + "name": "land-degradation-debt", + "title": "Land degradation debt", + "url": "https://esdac.jrc.ec.europa.eu/content/land-degradation-debt", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Global Land degradation as \u2018debts\u2019 for a) tree cover, b) soil erosion c) above ground carbon d) below ground carbon. Environmental Debt is the difference between natural potential condition and current condition." + }, + { + "name": "land-degradation-europe", + "title": "Land Degradation in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/land-degradation-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset includes the Land Multi-degradation Index (LMI) for arable and agricultural lands and data for 12 indicators (land degradation processes) which were compiled to develop the LMI. The dataset includes the agricultural (~2 million km2) and the arable (~1.1 million km2) lands of the EU and other European countries." + }, + { + "name": "land-degradation-global-arable-lands", + "title": "Land degradation in global arable lands", + "url": "https://esdac.jrc.ec.europa.eu/content/land-degradation-global-arable-lands", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Analysis of the spatial footprint of the multiple forms of land degradation in global arable lands. This includes 5 land degradation processes: aridity, soil erosion, vegetation decline, soil salinization and soil organic carbon decline. Data are at global scale." + }, + { + "name": "land-suitability-temperate-europe", + "title": "Land suitability in temperate Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/land-suitability-temperate-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This datasets includes land suitability maps for several crops and land uses (14 crops , 7 fruit trees, 3 land-use types) in the temperate continental climate of Europe. To model the land suitability we used geospatial data depicting seventeen eco-pedological indicators (e.g. soil texture, pH, porosity, temperature, precipitation, slope). To evaluate how the land is utilized, the suitability maps have been spatially cross-tabulated with a crop map." + }, + { + "name": "legend-files", + "title": "Legend Files", + "url": "https://esdac.jrc.ec.europa.eu/content/legend-files", + "request_needed": false, + "status": "SKIPPED", + "notes": "This is not a soil dataset itself, but only legend and symbology files (LYR, AVL, KMZ) for visualizing existing European Soil Database layers in GIS. Download just when you need standardized GIS symbology :)", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": null + }, + { + "name": "ls-factor-slope-length-and-steepness-factor-eu", + "title": "LS-factor (Slope Length and Steepness factor) for the EU", + "url": "https://esdac.jrc.ec.europa.eu/content/ls-factor-slope-length-and-steepness-factor-eu", + "request_needed": true, + "status": "PROCESSED", + "notes": "Not direct soil data, but critical soil erosion parameter.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset (GIS maps) (2015) represents the \"Slope Length and Steepness factor\" (LS-factor), one of the six input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28; maps at resolutions of 25m (per country) and 100m (Europe-wide)." + }, + { + "name": "lucas-2009-topsoil-data", + "title": "LUCAS 2009 TOPSOIL data", + "url": "https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Data from the 2009 LUCAS campaign soil component containing soil properties data (clay, silt and sand content, coarse fragments, pH, organic carbon content, CaCO3, nitrogen, phosphorous, potassium, cation exchane capacity) and multispectral absorbance data." + }, + { + "name": "lucas-2015-topsoil-data-switzerland", + "title": "LUCAS 2015 Topsoil data of Switzerland", + "url": "https://esdac.jrc.ec.europa.eu/content/lucas-2015-topsoil-data-switzerland", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "The dataset contains the data of physical and chemical properties analysed in samples taken in Switzerland within the context of LUCAS 2015 survey. These data have been used in the study \u201cComparison of sampling with a spade and gouge auger for topsoil monitoring at the continental scale\u201d, published in the European Journal of Soil Science (https://onlinelibrary.wiley.com/doi/abs/10.1111/ejss.12862). The dataset format is an Excel file." + }, + { + "name": "lucas-2018-topsoil-data", + "title": "LUCAS 2018 TOPSOIL data", + "url": "https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Data from the 2018 LUCAS campaign soil component containing soil properties data for 18,984 samples: pH (CaCl2 and H2O), organic carbon content, CaCO3, nitrogen, phosphorous, potassium, EC (Electrical conductivity), Oxalate extractable Fe and Al ." + }, + { + "name": "lucas-2022-topsoil-data", + "title": "LUCAS 2022 TOPSOIL data", + "url": "https://esdac.jrc.ec.europa.eu/content/lucas-2022-topsoil-data", + "request_needed": false, + "status": "SKIPPED", + "notes": "Unavailable yet", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "Data from the 2022 LUCAS campaign soil component containing soil properties data for approximately 33,00 locations (38,000 samples): In addition to the standard core analysis (pH (CaCl2 and H2O), carbonate organic carbon, nitrogen, phosphorous, potassium, Electrical conductivity), data will be presented on eDNA, the residues of pesticide residues and a broader assessment of metals and micronutrients." + }, + { + "name": "lucas2015-topsoil-data", + "title": "LUCAS 2015 TOPSOIL data", + "url": "https://esdac.jrc.ec.europa.eu/content/lucas2015-topsoil-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Data from the 2015 LUCAS campaign soil component containing soil properties data (clay, silt and sand content, coarse fragments, pH (CaCl2 and H2O), organic carbon content, CaCO3, nitrogen, phosphorous, potassium, EC (Electrical conductivity) and multispectral reflectance data for 21,859 samples. These primary data are supplemented by reference ancillary data describing a range of environmental conditions for the LUCAS Soil locations." + }, + { + "name": "manure-and-soil-biodiversity", + "title": "Manure and soil biodiversity", + "url": "https://esdac.jrc.ec.europa.eu/content/manure-and-soil-biodiversity", + "request_needed": false, + "status": "SKIPPED", + "notes": "Not direct soil biodiversity data. It provides manure production maps (shapefile) and a literature database only. Useful for management context but no spatial or point soil biodiversity indicators included", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "This dataset includes the spatial distribution of manure in EU and UK (per country, animal type) plus the database of the literature used to review interactions between Manure and Soil biodiversity." + }, + { + "name": "map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil", + "title": "Maps indicating the availability of Raw Material from soils in the European Union", + "url": "https://esdac.jrc.ec.europa.eu/content/map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil", + "request_needed": true, + "status": "PROCESSED", + "notes": "shows general availability of soil materials (peat, sand, gravel) for extraction, not actual soil properties or measurements I think.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset (maps)(2016) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union." + }, + { + "name": "maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0", + "title": "Maps of heavy metals in the soils of the EU, based on LUCAS 2009 HM data", + "url": "https://esdac.jrc.ec.europa.eu/content/maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Detailed maps of heavy metals in the EU27 (EU-28 except Croatia), based on topsoil HM data from LUCAS 2009" + }, + { + "name": "maps-indicators-soil-hydraulic-properties-europe", + "title": "Maps of indicators of soil hydraulic properties for Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/maps-indicators-soil-hydraulic-properties-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated." + }, + { + "name": "maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0", + "title": "Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU", + "url": "https://esdac.jrc.ec.europa.eu/content/maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati)." + }, + { + "name": "maps-storing-and-filtering-capacity-soils-europe", + "title": "Maps of the Storing and Filtering Capacity of Soils in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/maps-storing-and-filtering-capacity-soils-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Dataset provides derived functional capacity indices, not soil property measurements.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used." + }, + { + "name": "mercury-content-european-union-topsoil", + "title": "Mercury content in the European Union topsoil", + "url": "https://esdac.jrc.ec.europa.eu/content/mercury-content-european-union-topsoil", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Topsoil Hg concentrations (\u03bcg kg\u22121) across 26 EU countries estimated by deep neural network \u2013 regression kriging. We also provide Mercury stocks and mercury fluxes to main riverbasins and sea outlets. The assessment is based on 21591 LUCAS samples (0-20cm) from 26 European Union countries." + }, + { + "name": "multiple-co-occurring-erosion-processes-global-cropland", + "title": "Multiple co-occurring erosion processes on global cropland", + "url": "https://esdac.jrc.ec.europa.eu/content/multiple-co-occurring-erosion-processes-global-cropland", + "request_needed": true, + "status": "SKIPPED", + "notes": "Higher resolution available in EU", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Novel assessment of the spatial distribution of individual and combined (additive) threat to soil due to five erosion processes across the world's areas suitable for farming. Susceptibility classes were predicted for water erosion, wind erosion, Soil Loss by Crop Harvesting (SLCH), Tillage erosion and gully erosion." + }, + { + "name": "multiple-concurrent-soil-erosion-processes", + "title": "Multiple concurrent soil erosion processes", + "url": "https://esdac.jrc.ec.europa.eu/content/multiple-concurrent-soil-erosion-processes", + "request_needed": true, + "status": "PROCESSED", + "notes": "Modeled erosion susceptibility", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A first-ever assessment at European scale combines the threat of water, wind, tillage and harvesting to reveal the cumulative impact on arable land. We present the datasets for each of the erosion process (water, wind, tillage, harvesting root crops) and their cumulative effect at 100m resolution for EU arable lands (110 Million ha)." + }, + { + "name": "n2o-emissions-agricultural-soils-europe", + "title": "N2O emissions from agricultural soils in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/n2o-emissions-agricultural-soils-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution." + }, + { + "name": "natural-susceptibility-soil-compaction-europe", + "title": "Natural susceptibility to soil compaction in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/natural-susceptibility-soil-compaction-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Map (2008) showing the natural susceptibility of agricultural soils to compaction if they were to be exposed to compaction, based on the creation of logical connections between relevant parameters (using pedotransfer rules), taking as input parameters attributes of the European soil database (soil type, texture, etc.). For EU-27." + }, + { + "name": "observedtypical-soc-index", + "title": "Observed/typical SOC index", + "url": "https://esdac.jrc.ec.europa.eu/content/observedtypical-soc-index", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Hugo", + "requested_downloaded_by": "Hugo", + "processed_by": "Kuangdai", + "abstract": "Observed/typical Soil Organic Carbon (SOC) index classes and associated pedo-climate zones across the EU and UK are presented in 2 GeoTiff files at 100 m resolution." + }, + { + "name": "octop-topsoil-organic-carbon-content-europe", + "title": "OCTOP: Topsoil Organic Carbon Content for Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/octop-topsoil-organic-carbon-content-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A 2004 GIS map of Soil Organic Carbon (SOC) content (%) in the surface horizon of soils in Europe, associated to a JRC internal report." + }, + { + "name": "pan-european-soc-stock-agricultural-soils", + "title": "Pan-European SOC stock of agricultural soils", + "url": "https://esdac.jrc.ec.europa.eu/content/pan-european-soc-stock-agricultural-soils", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Data (2014) related to Pan-European SOC stock of agricultural soils, containing GIS maps for a) Pan-European SOC stock of agricultural soils (shapefile), b) Potential carbon sequestration by modelling a comprehensive set of management practices (shapefile), c) Average Eroded SOC in agricultural soils (raster)." + }, + { + "name": "pan-european-soil-erosion-risk-assessment-pesera", + "title": "Pan European Soil Erosion Risk Assessment - PESERA", + "url": "https://esdac.jrc.ec.europa.eu/content/pan-european-soil-erosion-risk-assessment-pesera", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A 2003 GIS map of Soil erosion estimates (t/ha/yr) by applying the PESERA GRID (physical) model at 1km, using the European Soil Database, CORINE land cover, climate data from the MARS Project and a Digital Elevation Model. The resulting estimates of sediment loss are from erosion by water." + }, + { + "name": "pesticides-residues-european-agricultural-soils", + "title": "Pesticides residues in European agricultural soils", + "url": "https://esdac.jrc.ec.europa.eu/content/pesticides-residues-european-agricultural-soils", + "request_needed": true, + "status": "SKIPPED", + "notes": "LUCAS-based; Skipped because data only contain NUT average", + "screened_by": "Jiayao, Kuangdai", + "requested_downloaded_by": "Jiayao", + "processed_by": null, + "abstract": "This dataset contains the results of a study targeting residues of active ingredients of pesticides used as crop protection products in soil samples collected from the 2018 LUCAS survey. This database presents the average concentrations values in (mg/kg dry weight) obtained for all the 118 substances above their specific Limit of Quantification(LOQ), measured at 3473 sites. Data is reported with a geographical resolution at NUTS2 level due to data protection requirements of the EU General Data Protection Regulation (GDPR)." + }, + { + "name": "phosphorus-budget-and-p-stocks", + "title": "Phosphorus budget and P stocks", + "url": "https://esdac.jrc.ec.europa.eu/content/phosphorus-budget-and-p-stocks", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Phosphorus (P) budget in the European agricultural soils. We provide shape files with main inputs (fertilizers, manure, chemical weathering, atmospheric deposition) and the losses by water erosion. In addition, we model the Total P and the relevant P stocks (available/Olsen, Total). We also make available 21,681 LUCAS point data with measured Total P." + }, + { + "name": "phosphorus-cycle-european-agricultural-soils", + "title": "Phosphorus cycle in European agricultural soils", + "url": "https://esdac.jrc.ec.europa.eu/content/phosphorus-cycle-european-agricultural-soils", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Process-based biogeochemical models are valuable instruments to monitor the P cycle and predict the effect of agricultural management policies. We upscale the calibrated DayCent model at European level using data-derived soil properties, advanced input data sets, and representative management practices. Available datasets include Current P Budget and Soil Pools, Projected P Budget and Soil Pools for the EU and UK as well the datasets corresponding to the figures of the related manuscript." + }, + { + "name": "phosphorus-plant-removal", + "title": "Phosphorus plant removal", + "url": "https://esdac.jrc.ec.europa.eu/content/phosphorus-plant-removal", + "request_needed": true, + "status": "SKIPPED", + "notes": "NUTS2 level", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Phosphorus (P) plant removal is the amount of P removed by crop harvesting and plant residues. We provide shape files with assessments at EU, National and regional scale." + }, + { + "name": "post-fire-soil-erosion-europe", + "title": "Post-fire soil erosion in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/post-fire-soil-erosion-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset contains the soil erosion estimations resulting of a study that mapped post-fire soil erosion in EU and UK following the 2017\u2019s wildfires during 5 post-fire years. Five maps (GeoTIFF) and their corresponding dataframe (Rdata) are provided at 25m resolution, from year 1 (2017) to year 5 (2021)." + }, + { + "name": "post-fire-soil-erosion-map-portugal", + "title": "Post-fire soil erosion map for Portugal", + "url": "https://esdac.jrc.ec.europa.eu/content/post-fire-soil-erosion-map-portugal", + "request_needed": false, + "status": "SKIPPED", + "notes": "Exceptional condition; Cannot fuse", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "This dataset contains the results of a study that mapped post-fire soil erosion in Portugal and identified the areas with higher post-fire erosion risk for past and future climate extremes. Two maps are provided: one at 100m resolution and another at 25m resolution. The maps express \u2018predicted soil losses\u2019 (Mg ha-1 y-1)." + }, + { + "name": "potential-threats-soil-biodiversity-europe", + "title": "Potential threats to soil biodiversity in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/potential-threats-soil-biodiversity-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Dataset that contains 3 GIS maps showing Potential threats to soil biodiversity in Europe (for soil microorganisms, for fauna,forbiological functions), along with 13 input layers (habitat fragmentation, climate change, soil erosion, etc.); resolution 500m." + }, + { + "name": "predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1", + "title": "Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project", + "url": "https://esdac.jrc.ec.europa.eu/content/predicted-distribution-soc-content-europe-based-lucas-biosoil-and-czo-context-eu-funded-1", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe)." + }, + { + "name": "priming-effects-soils-across-europe", + "title": "Glucose-induced priming effects in soils across Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/priming-effects-soils-across-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Quantification and comparison of glucose-induced priming effects in soils with contrasting land uses and under different crop types" + }, + { + "name": "progress-management-contaminated-sites-europe-0", + "title": "Progress in the management of contaminated sites in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/progress-management-contaminated-sites-europe-0", + "request_needed": false, + "status": "SKIPPED", + "notes": "Country-level resolution", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "These are the underlying data for the report \"Progress in management of contaminated sites (CSI 015)\", based on the 2011 EIONET NRC Soil data collection concerning contaminated sites, and stored in an MS Excel file. Some data/information that arrived at JRC after the data collection deadline (for Czech Republic, Greece, Italy, Latvia) are bundled in an additional file." + }, + { + "name": "projected-land-conservation-impacts-global-soil-erosion-and-pollination-sufficiency", + "title": "Projected Land Conservation Impacts on Global Soil Erosion and Pollination Sufficiency", + "url": "https://esdac.jrc.ec.europa.eu/content/projected-land-conservation-impacts-global-soil-erosion-and-pollination-sufficiency", + "request_needed": true, + "status": "SKIPPED", + "notes": "Long term prediction.", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "We combine the open-source land-system model MAgPIE (Model of Agricultural Production and its Impact on the Environment) with the Spatial Economic Allocation Landscape Simulator (SEALS) and the Global Soil Erosion Modelling (GloSEM) platform to assess how different land conservation measures focused on climate and biodiversity protection could drive changes in soil loss by water erosion, pollination and landscape heterogeneity." + }, + { + "name": "rainfall-erosivity-european-union-and-switzerland", + "title": "Rainfall Erosivity in the EU and Switzerland (R-factor)", + "url": "https://esdac.jrc.ec.europa.eu/content/rainfall-erosivity-european-union-and-switzerland", + "request_needed": true, + "status": "PROCESSED", + "notes": "It is not direct soil property, it is rainfalla erosivity factor, key factor to estimate soil erosion", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Dataset (GIS map) (2015) and associated products for the \"Rainfall erosivity\" (R-factor), one of the input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28+Switzerland; R-factor map at resolutions of 500m. Users can downloads Raw data, Baseline map (2010), Monthly erosivity, Future projections (2050), Past erosivity (1961-2000)." + }, + { + "name": "saline-and-sodic-soils-european-union", + "title": "Saline and Sodic Soils in European Union", + "url": "https://esdac.jrc.ec.europa.eu/content/saline-and-sodic-soils-european-union", + "request_needed": true, + "status": "SKIPPED", + "notes": "Too small dataset", + "screened_by": "Tina, Kuangdai", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Saline and Sodic Soils Map for EU-27 (2008) showing the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation of salt affected areas with a limited level of reliability (e.g. < 50 or > 50% of the area); therefore the results represented in the map should only be used for orientating purposes." + }, + { + "name": "sediments-removal-costs", + "title": "Sediments removal costs", + "url": "https://esdac.jrc.ec.europa.eu/content/sediments-removal-costs", + "request_needed": false, + "status": "SKIPPED", + "notes": "This dataset is the costs of sediments removal", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "For the entire EU and UK, we provide estimates of sediments removal costs based on three quantification methods: a) Flat rate assignment from modelled sediment delivery b) Regional assignment from modelled sediment delivery c) Sediment remediation costs from extrapolated potential reservoir capacity losses" + }, + { + "name": "sinfo-esdb-data-adapted-mars-crop-yield-forecasting-system", + "title": "SINFO: ESDB Data adapted for the MARS Crop Yield Forecasting System", + "url": "https://esdac.jrc.ec.europa.eu/content/sinfo-esdb-data-adapted-mars-crop-yield-forecasting-system", + "request_needed": true, + "status": "SKIPPED", + "notes": "Downloaded. Analysis skipped due to unstructured file formats, unclear naming conventions, legacy Fortran code, and insufficient documentation linking rules to usable data.", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": null, + "abstract": "A database (2007) that is the result of adapting the European Soil Database for the provision of new and specific soil information for the CGMS (Crop Growth Monitoring System) for use in the MARS Crop Yield Forecast Sing System." + }, + { + "name": "SOC-changes-2009-18", + "title": "Changes in Soil Organic Carbon in Croplands and Grasslands between 2009 and 2018", + "url": "https://esdac.jrc.ec.europa.eu/content/SOC-changes-2009-18", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "This dataset (map) illustrates the variations in soil organic carbon (measured in g C kg y-1) within the 0-20 cm depth range for croplands and grasslands across the EU and UK between 2009 and 2018." + }, + { + "name": "soil-biodiversity-conservation-literature-and-legally-binding-instruments", + "title": "Soil Biodiversity Conservation literature and legally binding instruments", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-biodiversity-conservation-literature-and-legally-binding-instruments", + "request_needed": false, + "status": "SKIPPED", + "notes": "Not soil data \u2014 literature/policy summary", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "We present the review of 54 articles in the literature and Member States policy documents which address Soil Biodiversity Conservation" + }, + { + "name": "soil-biodiversity-dna-bacteria-and-fungi", + "title": "Soil biodiversity - DNA Bacteria and Fungi", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-biodiversity-dna-bacteria-and-fungi", + "request_needed": true, + "status": "SKIPPED", + "notes": "LUCAS-based; Skipped for now because files are too large (50 GB)", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "The dataset includes bacterial 16S data and fungal ITS raw DNA sequences for 885 samples collected as part of LUCAS 2018 Soil survey (Biodiversity module)." + }, + { + "name": "soil-biodiversity-dna-eukaryotes", + "title": "Soil biodiversity dna eukaryotes", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-biodiversity-dna-eukaryotes", + "request_needed": true, + "status": "SKIPPED", + "notes": "LUCAS-based; Skipped for now because files are too large (50 GB)", + "screened_by": "Jiayao", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "The dataset includes bacterial 18S raw DNA sequences for 885 samples collected as part of LUCAS 2018 Soil survey (Biodiversity module)." + }, + { + "name": "soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european", + "title": "Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27)", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27)" + }, + { + "name": "soil-bulk-density-europe", + "title": "Soil Bulk Density in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-bulk-density-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This dataset includes bulk density for Europe in 0-20cm, 0-10cm, 10-20cm layers generated with advanced Cubist rule-based regression model. We also provide the Packing density estimation based on bulk density and clay content. In addition, we make available the point data measurements of bulk density" + }, + { + "name": "soil-carbon-risk-index", + "title": "Soil Carbon Risk Index", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-carbon-risk-index", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Hugo", + "requested_downloaded_by": "Hugo", + "processed_by": "Kuangdai", + "abstract": "This dataset includes the a) Soil organic carbon risk index b) SOC fractions predicted by visible near-infrared c) the mineral-associated organic carbon (MAOC) saturation.This repository accompanies the publication \"Revisiting the soil carbon saturation concept to inform a risk index in European agricultural soils\" and can be used to access the results and code." + }, + { + "name": "soil-degradation-indicators-eu", + "title": "Soil degradation indicators in EU", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-degradation-indicators-eu", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Pan-EU assessment of soil degradation based on the latest state-of-the-art indicators of soil degradation as demonstrated in the EU Soil Health Dashboard. This dataset includes the 19 Soil degradation indicators plus the sum of all processes." + }, + { + "name": "soil-erodibility-k-factor-high-resolution-dataset-europe", + "title": "Soil Erodibility (K- Factor) High Resolution dataset for Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-erodibility-k-factor-high-resolution-dataset-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Map at 500m resolution (2014) providing a complete picture of the soil erodibility in the European Union member states. It is derived on the basis of the LUCAS 2009 soil survey exercise and the European Soil Database." + }, + { + "name": "soil-erosion-forestland-europe-using-rusle2015", + "title": "Soil erosion in forestland in Europe (using RUSLE2015)", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-erosion-forestland-europe-using-rusle2015", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Dataset (2 GIS-maps) (2016) related to soil erosion in Forestland in Europe. One map is the soil loss potential for EU28; the other map is the European Forest Cover Change for 36 European countries." + }, + { + "name": "soil-erosion-risk-assessment-europe-data-mesales-model-dataset", + "title": "Soil Erosion Risk Assessment in Europe data (MESALES model) - Dataset", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-erosion-risk-assessment-europe-data-mesales-model-dataset", + "request_needed": true, + "status": "SKIPPED", + "notes": " This dataset is fully superseded by newer, higher-quality sources.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "These Soil Erosion Risk Assessment in Europe data (2000) have been elaborated by INRA for JRC using the MESAES model and can be seen as an intermediate step towards a \"state-of-the-art erosion modelling at the European scale\", prior to the initiation of the PESERA project." + }, + { + "name": "soil-erosion-water-rusle2015", + "title": "Soil erosion by water (RUSLE2015)", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-erosion-water-rusle2015", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Dataset (GIS map) (2015) that shows the Soil Loss by Water Erosion in Europe and is the result of applying a modified version of the Revised Universal Soil Loss Equation (RUSLE) model, RUSLE 2015; resolution 100m. EU28. Two data points are available: 2010 and 2016" + }, + { + "name": "soil-function-data-measured-lucas-2018-sites-across-eu", + "title": "Soil function data measured in LUCAS 2018 sites across the EU", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-function-data-measured-lucas-2018-sites-across-eu", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "In the LUCAS 2018 survey, soil functions have been measured in the 881 sampling sites selected for the soil biodiversity assessment across Europe. These soil functions include * soil aggregates (i.e., mean width diameter and water-stable aggregates), * enzyme activities, * microbial respiration and biomass, and * measurement of ester-linked fatty acid methyl esters (FAMEs)." + }, + { + "name": "soil-ghg-fluxes-using-lucas-soil-daycent", + "title": "Soil GHG fluxes using LUCAS soil-DayCent (for EU)", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-ghg-fluxes-using-lucas-soil-daycent", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Soil fluxes (CO2 and N2O ) under mitigation scenarios using the LUCAS soil-DayCent model integration framework" + }, + { + "name": "soil-health-related-citizen-science-projects", + "title": "Soil health related citizen-science projects", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-health-related-citizen-science-projects", + "request_needed": false, + "status": "SKIPPED", + "notes": "Not data \u2014 metadata on citizen science projects. No soil properties or mappable content.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Soil-related citizen science projects have gained significant interest driven by the prominence of soil within public policy agendas. EUSO in collaboration with ECHO makes a review on previous citizen science projects, initiatives and activities that have engaged citizens to monitor soil. The database includes all those initiatives." + }, + { + "name": "soil-loss-due-crop-harvesting-european-union", + "title": "Soil loss due to crop harvesting in the European Union", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-loss-due-crop-harvesting-european-union", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Soil loss due to crop harvesting in the European Union. The regional estimates of total Soil Loss by Crop Harvesting (SLCH) are presented at country and regional level." + }, + { + "name": "soil-map-soil-atlas-africa", + "title": "Soil Atlas of Africa", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-map-soil-atlas-africa", + "request_needed": true, + "status": "SKIPPED", + "notes": " Africa, not Europe", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": null, + "abstract": "Africa has a diverse range of soil types, with over 60% representing hot, arid, or immature soil assemblages. A further 20% are tropical or subtropical soils. The continent also has a considerable number of soil types associated with local soil-forming factors. Unlike other continents, Africa does not have large expanses of prairie or steppe-type soils." + }, + { + "name": "soil-microbial-biomass-and-respiration", + "title": "Soil microbial biomass and respiration", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-microbial-biomass-and-respiration", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Maps of potential soil microbial basal respiration (bas), microbial biomass (Cmic), and respiratory quotient (qO2) predicted across Europe. Monthly maps for bas and Cmic are also available" + }, + { + "name": "soil-organic-carbon-losses-lateral-transfer-oceans", + "title": "Soil organic carbon losses with lateral transfer to the oceans", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-organic-carbon-losses-lateral-transfer-oceans", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A spatially distributed model coupling erosion, transport, and deposition to the carbon cycle was developed. Then, it was used to simulate the impact of cover crops on both erosion and carbon, to show that cover crops can simultaneously increase organic carbon storage and reduce particulate organic carbon export to the oceans." + }, + { + "name": "soil-organic-carbon-saturation-capacity", + "title": "Soil Organic Carbon - Saturation Capacity in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-organic-carbon-saturation-capacity", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset (map) (2016) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great potential of soil to store more carbon." + }, + { + "name": "soil-organic-carbon-soc-projections-europe", + "title": "Soil Organic Carbon (SOC) Projections for Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-organic-carbon-soc-projections-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication \"Assessment of soil organic carbon stocks under future climate and land cover changes in Europe\" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs)." + }, + { + "name": "soil-organic-matter-som-fractions", + "title": "Soil Organic Matter (SOM) fractions", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-organic-matter-som-fractions", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "This dataset contains the original measured Soil Organic Matter (SOM) fractions of a subset of the LUCAS 2009 topsoil dataset. This dataset includes 352 samples for all land uses and 186 samples only for grassland and fores used to derived maps on Particulate Organic Matter (POM) and Mineral-Associated Organic Matter (MAOM)" + }, + { + "name": "soil-ph-europe", + "title": "Soil pH in Europe", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-ph-europe", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "A quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based on Regression-Kriging. Fifty-four (54) auxiliary variables in the form of raster maps at 5km resolution were used to explain the differences in the distribution of soil pH (CaCl2) and the kriged map of the residuals from the regression model was added." + }, + { + "name": "soil-profile-analytical-database-2", + "title": "Soil Profile Analytical Database 2", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Soil Profile Analytical Database for Europe (2009) (separate from the European Soil Database SPADE-1) aiming to provide sufficient soil property data to support higher tier modeling of pesticide fate at the European level. Joint venture between JRC, ECPA and the ESBN." + }, + { + "name": "soil-sealing-food-security-loss-potential-agricultural-production-capability", + "title": "Soil Sealing & food security (Loss of Potential Agricultural Production Capability)", + "url": "https://esdac.jrc.ec.europa.eu/content/soil-sealing-food-security-loss-potential-agricultural-production-capability", + "request_needed": false, + "status": "SKIPPED", + "notes": "Country-level resolution", + "screened_by": "Kuangdai", + "requested_downloaded_by": null, + "processed_by": null, + "abstract": "This dataset (2015), an Excel file, contains the data associated to the peer-reveiwed paper: Gardi, C., Panagos, P., Van Liedekerke, M., Bosco, C., de Brogniez, D. 2015. Land take and food security: assessment of land take on the agricultural production in Europe. Journal of Environmental Planning and Management, 58 (5) , pp. 898-912." + }, + { + "name": "Soil_erosion_by_wind", + "title": "Soil erosion by wind", + "url": "https://esdac.jrc.ec.europa.eu/content/Soil_erosion_by_wind", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This dataset consists of various elements related to soil erosion by wind: 1) Soil loss by wind erosion in European agricultural soils (2016); 1km resolution, 2) Land susceptibility to wind erosion (2014), 500m resolution, 3) Wind erosion susceptibility of European soils (2014); 500m resolutiomn, and 4) Agriculture Field Parameters data (containing averaged Field Size, Field Orientation, Field Length, Average Number of Images, Percentage of Large Fields and Length to Width Ratios) for the EU 27 Member states and Switzerland, aggregated to NUTS region." + }, + { + "name": "spade-14", + "title": "Soil profile analytical database 14 (SPADE 14)", + "url": "https://esdac.jrc.ec.europa.eu/content/spade-14", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "Soil Profile Analytical Database 14 (SPADE 14) is based on the concept used in previous versions of SPADE (SPADE 1 and 8). It includes 1078 soil profile data from 28 countries." + }, + { + "name": "spadem", + "title": "SPADE/M", + "url": "https://esdac.jrc.ec.europa.eu/content/spadem", + "request_needed": false, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "Database (2004) developed from the measured profiles; present in the Soil Profile Analytical Database of Europe of the European Soil Database v2. It counts 560 profiles within the EU." + }, + { + "name": "support-practices-factor-p-factor-eu", + "title": "Support Practices factor (P-factor) for the EU", + "url": "https://esdac.jrc.ec.europa.eu/content/support-practices-factor-p-factor-eu", + "request_needed": true, + "status": "PROCESSED", + "notes": "P-factor (1km) for EU \u2014 critical RUSLE input layer accounting for support practices.", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "This GIS map (2015) represents the \"Support Practices factor\" (P-factor) for the EU. At European level, the effect of support practices (compulsory for farmers to receive incentives under the CAP-GAEC) on soil loss were assessed by P-factor estimation taking into account a) contour farming b) maintenance of stone walls and c) grass margins. Resolution 1km." + }, + { + "name": "support-renewable-energy-directive", + "title": "Support to Renewable Energy Directive", + "url": "https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive", + "request_needed": false, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": null + }, + { + "name": "topsoil-physical-properties-europe-based-lucas-topsoil-data", + "title": "Topsoil physical properties for Europe (based on LUCAS topsoil data)", + "url": "https://esdac.jrc.ec.europa.eu/content/topsoil-physical-properties-europe-based-lucas-topsoil-data", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This dataset (GIS maps)(2016) contains 7 soil property maps that have been derived using soil point data from the LUCAS 2009 soil survey (around 20,000 points) for EU-25, using hybrid approaches like regression kriging. Properties: clay, silt and sand content; coarse fragments; bulk density; USDA soil textural class; available water capacity. Resolution 500m." + }, + { + "name": "topsoil-soil-organic-carbon-lucas-eu25", + "title": "Topsoil Soil Organic Carbon (LUCAS) for EU25", + "url": "https://esdac.jrc.ec.europa.eu/content/topsoil-soil-organic-carbon-lucas-eu25", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Kuangdai", + "requested_downloaded_by": "Kuangdai", + "processed_by": "Kuangdai", + "abstract": "This dataset (2015) provides maps for Topsoil Soil Organic Carbon in EU-25 that are based on LUCAS 2009 soil poibnt data through a generalized additive model. Map of predicted topsoil organic carbon content (g C kg-1) : The map of predicted topsoil organic carbon content (g C kg-1) was produced by fitting a generalised additive model between organic carbon measurements from the LUCAS survey (dependent variable) and a set of selected environmental covariates; namely slope, land cover, annual accumulated temperature, net primary productivity, latitude and longitude. It also includes a Map of standard error of the OC model predictions (g C kg-1)." + }, + { + "name": "un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale", + "title": "UN Sustainable Development Goal 15.3.1: Assessment of the land degradation indicator at EU scale", + "url": "https://esdac.jrc.ec.europa.eu/content/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "This dataset covers 9 maps associated to the assessment of the land degradation indicator at EU scale in the context of the UN Sustainable Development Goal 15.3.1, and corresponding to the output reported in a peer-reviewed publication. The main map is the one that expresses land degradation on the basis of soil organic carbon and soil erosion values." + }, + { + "name": "water-erosion-europe-2050", + "title": "Water erosion in Europe by 2050", + "url": "https://esdac.jrc.ec.europa.eu/content/water-erosion-europe-2050", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "The dataset includes the soil loss by water erosion projections by 2050. The data include 3 raster files for the corresponding three different greenhouse gas concentration scenarios (RCP2.6, RCP4.5, RCP8.5) , the erosivity projections and the C-factor 2050." + }, + { + "name": "wrb-data-soil-atlas-northern-circumpolar-region", + "title": "WRB Data for the Soil Atlas of the Northern Circumpolar Region", + "url": "https://esdac.jrc.ec.europa.eu/content/wrb-data-soil-atlas-northern-circumpolar-region", + "request_needed": true, + "status": "PROCESSED", + "notes": "Completed", + "screened_by": "Tina", + "requested_downloaded_by": "Tina", + "processed_by": "Kuangdai", + "abstract": "These data are the WRB soil data for the whole area covered by the Soil Atlas of the Northern Circumpolar Region. It comes as a single shapefile." + }, + { + "name": "zn-concentrations-eu-topsoils", + "title": "Zinc concentrations in EU topsoils", + "url": "https://esdac.jrc.ec.europa.eu/content/zn-concentrations-eu-topsoils", + "request_needed": true, + "status": "PROCESSED", + "notes": "LUCAS-based; Prioritised", + "screened_by": "Jiayao", + "requested_downloaded_by": "Jiayao", + "processed_by": "Kuangdai", + "abstract": "Zinc (Zn) is essential to sustain crop production and human health, while it can be toxic when present in excess. The Zn concentration (mg kg-1) in European topsoils as predicted by the quantile regression forest using the 21,682 samples of LUCAS. The resolution of this map is 250 m by 250 m." + } +] \ No newline at end of file diff --git a/src/esdac/status.xlsx b/src/esdac/status.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..7db61a0658de0fdd48ee81cc3be81e4320387005 Binary files /dev/null and b/src/esdac/status.xlsx differ diff --git a/src/esdac/support-practices-factor-p-factor-eu/fuse_schema.json b/src/esdac/support-practices-factor-p-factor-eu/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..8a6d69b6f060fd760349123603bc30fc4fa6f713 --- /dev/null +++ b/src/esdac/support-practices-factor-p-factor-eu/fuse_schema.json @@ -0,0 +1,7 @@ +{ + "PFactor@tif": { + "erosion": { + "p_factor_support_practices": "EU_PFactor_V2" + } + } +} diff --git a/src/esdac/support-practices-factor-p-factor-eu/process.py b/src/esdac/support-practices-factor-p-factor-eu/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/support-practices-factor-p-factor-eu/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/support-renewable-energy-directive/fuse_skip.txt b/src/esdac/support-renewable-energy-directive/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..88c616936b8aea12380941cf5b48f075bddd09ad --- /dev/null +++ b/src/esdac/support-renewable-energy-directive/fuse_skip.txt @@ -0,0 +1 @@ +Low res \ No newline at end of file diff --git a/src/esdac/support-renewable-energy-directive/process.py b/src/esdac/support-renewable-energy-directive/process.py new file mode 100644 index 0000000000000000000000000000000000000000..34d5328b7ca03cb1b4839c893e82ac37a68e487a --- /dev/null +++ b/src/esdac/support-renewable-energy-directive/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_rdc_rst + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.RDC")): + move_list = process_rdc_rst(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/topsoil-physical-properties-europe-based-lucas-topsoil-data/fuse_skip.txt b/src/esdac/topsoil-physical-properties-europe-based-lucas-topsoil-data/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..26c46530aa5fdd8edc8365b5e28e46858676ea7b --- /dev/null +++ b/src/esdac/topsoil-physical-properties-europe-based-lucas-topsoil-data/fuse_skip.txt @@ -0,0 +1 @@ +Used in LUCAS \ No newline at end of file diff --git a/src/esdac/topsoil-physical-properties-europe-based-lucas-topsoil-data/process.py b/src/esdac/topsoil-physical-properties-europe-based-lucas-topsoil-data/process.py new file mode 100644 index 0000000000000000000000000000000000000000..fe088b4190f4c11d71ccba490767c8f14f3598ed --- /dev/null +++ b/src/esdac/topsoil-physical-properties-europe-based-lucas-topsoil-data/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/topsoil-soil-organic-carbon-lucas-eu25/fuse_schema.json b/src/esdac/topsoil-soil-organic-carbon-lucas-eu25/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..00da7986e1a2eb31b7d46d21f6e7b0cc871110da --- /dev/null +++ b/src/esdac/topsoil-soil-organic-carbon-lucas-eu25/fuse_schema.json @@ -0,0 +1,8 @@ +{ + "LUCASSOC@tif": { + "carbon": { + "topsoil_OC_prediction_mean (g/kg)": "ocCont_snap", + "topsoil_OC_prediction_std (g/kg)": "sterror_snap" + } + } +} diff --git a/src/esdac/topsoil-soil-organic-carbon-lucas-eu25/process.py b/src/esdac/topsoil-soil-organic-carbon-lucas-eu25/process.py new file mode 100644 index 0000000000000000000000000000000000000000..fe088b4190f4c11d71ccba490767c8f14f3598ed --- /dev/null +++ b/src/esdac/topsoil-soil-organic-carbon-lucas-eu25/process.py @@ -0,0 +1,20 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif files (excluding auxiliary .xml variants) === +move_list = [] +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and remove local copy === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale/fuse_schema.json b/src/esdac/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0da912d65f4527231b8a3ae1eb696d2e0b73171d --- /dev/null +++ b/src/esdac/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale/fuse_schema.json @@ -0,0 +1,59 @@ +{ + "RUSLE_SOC_land_degradation@tif": { + "land_degradation": { + "soil_erosion_exceeding_10Mg_ha_yr (t ha⁻¹ yr⁻¹)": "EROSION_10tha", + "SDG_land_degradation_based_on_ESA_SG": [ + "SDG_MOD_ESA_SG", + { + "0": "No degradation", + "1": "Degraded (one sub-indicator)", + "2": "Degraded (both sub-indicators)", + "-1": null + } + ], + "SDG_water_use_efficiency_based_on_COR_LC": [ + "SDG_MOD_WUE_COR_LC", + { + "0": "No degradation", + "1": "Degraded (one sub-indicator)", + "2": "Degraded (both sub-indicators)", + "-1": null + } + ], + "SDG_water_use_efficiency_based_on_ESA_SG": [ + "SDG_MOD_WUE_ESA_SG", + { + "0": "No degradation", + "1": "Degraded (one sub-indicator)", + "2": "Degraded (both sub-indicators)", + "-1": null + } + ], + "SDG_water_availability_dynamics_based_on_COR_LC": [ + "SDG_WAD_COR_LC", + { + "0": "No degradation", + "1": "Degraded (one sub-indicator)", + "2": "Degraded (both sub-indicators)", + "-1": null + } + ], + "SDG_water_availability_dynamics_based_on_ESA_SG": [ + "SDG_WAD_ESA_SG", + { + "0": "No degradation", + "1": "Degraded (one sub-indicator)", + "2": "Degraded (both sub-indicators)", + "-1": null + } + ], + "soc_less_than_1_percent": [ + "SOC_less1%", + { + "0": false, + "1": true + } + ] + } + } +} diff --git a/src/esdac/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale/process.py b/src/esdac/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale/process.py new file mode 100644 index 0000000000000000000000000000000000000000..3f5de58718ef6676d038a657e3e5fa5e9bfbc13a --- /dev/null +++ b/src/esdac/un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale/process.py @@ -0,0 +1,22 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_excel + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif and .xlsx files === +move_list = [] + +# Process each .tif file: generate a standardized .tif and preview .png +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/water-erosion-europe-2050/fuse_skip.txt b/src/esdac/water-erosion-europe-2050/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..09130966c1e943a1d151ce760b5b4f9d8cb0b7d0 --- /dev/null +++ b/src/esdac/water-erosion-europe-2050/fuse_skip.txt @@ -0,0 +1 @@ +Prediction \ No newline at end of file diff --git a/src/esdac/water-erosion-europe-2050/process.py b/src/esdac/water-erosion-europe-2050/process.py new file mode 100644 index 0000000000000000000000000000000000000000..940cc4d9e81671c2e9dfee9622b4e9714afabe41 --- /dev/null +++ b/src/esdac/water-erosion-europe-2050/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.tif")): + move_list = process_tif(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/wrb-data-soil-atlas-northern-circumpolar-region/fuse_skip.txt b/src/esdac/wrb-data-soil-atlas-northern-circumpolar-region/fuse_skip.txt new file mode 100644 index 0000000000000000000000000000000000000000..f3e366c8de88e199efcf9466dd18edb5b05a3768 --- /dev/null +++ b/src/esdac/wrb-data-soil-atlas-northern-circumpolar-region/fuse_skip.txt @@ -0,0 +1 @@ +No data \ No newline at end of file diff --git a/src/esdac/wrb-data-soil-atlas-northern-circumpolar-region/process.py b/src/esdac/wrb-data-soil-atlas-northern-circumpolar-region/process.py new file mode 100644 index 0000000000000000000000000000000000000000..91216b895fa810453eba5639820b2aa4a9746d98 --- /dev/null +++ b/src/esdac/wrb-data-soil-atlas-northern-circumpolar-region/process.py @@ -0,0 +1,19 @@ +from pathlib import Path +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_shp + +# === Step 1: Set up local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Unzip all ZIP files from drive/download to local/processed === +unzip(local_processed_dir, drive_download_dir, zip_files=None) + +# === Step 3: Process all TIFF files under local/processed === +move_list = [] +for tif_path in sorted(local_processed_dir.rglob("*.shp")): + move_list = process_shp(tif_path, move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/esdac/zn-concentrations-eu-topsoils/fuse_schema.json b/src/esdac/zn-concentrations-eu-topsoils/fuse_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..69095897da58a2a1e086f4d2ae1bfb185d36082b --- /dev/null +++ b/src/esdac/zn-concentrations-eu-topsoils/fuse_schema.json @@ -0,0 +1,9 @@ +{ + ".@tif": { + "trace_elements": { + "Zn_concentration (mg/kg)": "Zn/Figure 7", + "Zn_concentration_5th_percentile (mg/kg)": "5-95 Quantiles/Figure S5 a", + "Zn_concentration_95th_percentile (mg/kg)": "5-95 Quantiles/Figure S5 b" + } + } +} diff --git a/src/esdac/zn-concentrations-eu-topsoils/process.py b/src/esdac/zn-concentrations-eu-topsoils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..3f5de58718ef6676d038a657e3e5fa5e9bfbc13a --- /dev/null +++ b/src/esdac/zn-concentrations-eu-topsoils/process.py @@ -0,0 +1,22 @@ +from pathlib import Path + +from src.process.io import prepare_dirs, unzip, upload +from src.process.processor import process_tif, process_excel + +# === Step 1: Set up paths for local and drive directories === +local_processed_dir, drive_download_dir, drive_processed_dir = ( + prepare_dirs("esdac", Path(__file__).parent.stem) +) + +# === Step 2: Extract all ZIP files from drive/download into local/processed === +unzip(local_processed_dir, drive_download_dir) + +# === Step 3: Process all .tif and .xlsx files === +move_list = [] + +# Process each .tif file: generate a standardized .tif and preview .png +for tif in local_processed_dir.rglob("*.tif"): + move_list = process_tif(tif, move_list=move_list) + +# === Step 4: Upload processed files to drive/processed and clean up local === +upload(local_processed_dir, drive_processed_dir, move_list) diff --git a/src/fuse_esdac/codebooks/corine_lc.json b/src/fuse_esdac/codebooks/corine_lc.json new file mode 100644 index 0000000000000000000000000000000000000000..6d0733261aa6f8f669fa1527ba53b1f88f989dc5 --- /dev/null +++ b/src/fuse_esdac/codebooks/corine_lc.json @@ -0,0 +1,159 @@ +{ + "1": { + "value": "Artificial surfaces", + "children": { + "11": { + "value": "Urban fabric", + "children": { + "111": "Continuous urban fabric", + "112": "Discontinuous urban fabric" + } + }, + "12": { + "value": "Industrial, commercial and transport units", + "children": { + "121": "Industrial or commercial units", + "122": "Road and rail networks and associated land", + "123": "Port areas", + "124": "Airports" + } + }, + "13": { + "value": "Mine, dump and construction sites", + "children": { + "131": "Mineral extraction sites", + "132": "Dump sites", + "133": "Construction sites" + } + }, + "14": { + "value": "Artificial, non-agricultural vegetated areas", + "children": { + "141": "Green urban areas", + "142": "Sport and leisure facilities" + } + } + } + }, + "2": { + "value": "Agricultural areas", + "children": { + "21": { + "value": "Arable land", + "children": { + "211": "Non-irrigated arable land", + "212": "Permanently irrigated land", + "213": "Rice fields" + } + }, + "22": { + "value": "Permanent crops", + "children": { + "221": "Vineyards", + "222": "Fruit trees and berry plantations", + "223": "Olive groves" + } + }, + "23": { + "value": "Pastures", + "children": { + "231": "Pastures" + } + }, + "24": { + "value": "Heterogeneous agricultural areas", + "children": { + "241": "Annual crops associated with permanent crops", + "242": "Complex cultivation patterns", + "243": "Land principally occupied by agriculture, with significant areas of natural vegetation", + "244": "Agro-forestry areas" + } + } + } + }, + "3": { + "value": "Forest and semi natural areas", + "children": { + "31": { + "value": "Forests", + "children": { + "311": "Broad-leaved forest", + "312": "Coniferous forest", + "313": "Mixed forest" + } + }, + "32": { + "value": "Scrub and/or herbaceous vegetation associations", + "children": { + "321": "Natural grasslands", + "322": "Moors and heathland", + "323": "Sclerophyllous vegetation", + "324": "Transitional woodland-shrub" + } + }, + "33": { + "value": "Open spaces with little or no vegetation", + "children": { + "331": "Beaches, dunes, sands", + "332": "Bare rocks", + "333": "Sparsely vegetated areas", + "334": "Burnt areas", + "335": "Glaciers and perpetual snow" + } + } + } + }, + "4": { + "value": "Wetlands", + "children": { + "41": { + "value": "Inland wetlands", + "children": { + "411": "Inland marshes", + "412": "Peat bogs" + } + }, + "42": { + "value": "Maritime wetlands", + "children": { + "421": "Salt marshes", + "422": "Salines", + "423": "Intertidal flats" + } + } + } + }, + "5": { + "value": "Water bodies", + "children": { + "51": { + "value": "Inland waters", + "children": { + "511": "Water courses", + "512": "Water bodies" + } + }, + "52": { + "value": "Marine waters", + "children": { + "521": "Coastal lagoons", + "522": "Estuaries", + "523": "Sea and ocean" + } + } + } + }, + "9": { + "value": "NODATA", + "children": { + "99": { + "value": "NODATA", + "children": { + "999": "NODATA", + "990": "UNCLASSIFIED LAND SURFACE", + "995": "UNCLASSIFIED WATER BODIES" + } + } + } + } +} diff --git a/src/fuse_esdac/codebooks/lucas_lc.json b/src/fuse_esdac/codebooks/lucas_lc.json new file mode 100644 index 0000000000000000000000000000000000000000..5277e96d41e6cef783486fab3cfc23c140c48164 --- /dev/null +++ b/src/fuse_esdac/codebooks/lucas_lc.json @@ -0,0 +1,191 @@ +{ + "A": { + "value": "Artificial land", + "children": { + "A1": { + "value": "Roofed built-up areas", + "children": { + "A11": "Buildings with one to three floors", + "A12": "Buildings with more than three floors", + "A13": "Greenhouses" + } + }, + "A2": { + "value": "Artificial non-built up areas", + "children": { + "A21": "Non built-up area features", + "A22": "Non built-up linear features" + } + }, + "A3": "Other artificial areas" + } + }, + "B": { + "value": "Cropland", + "children": { + "B1": { + "value": "Cereals", + "children": { + "B11": "Common wheat", + "B12": "Durum wheat", + "B13": "Barley", + "B14": "Rye", + "B15": "Oats", + "B16": "Maize", + "B17": "Rice", + "B18": "Triticale", + "B19": "Other cereals" + } + }, + "B2": { + "value": "Root crops", + "children": { + "B21": "Potatoes", + "B22": "Sugar beet", + "B23": "Other root crops" + } + }, + "B3": { + "value": "Non-permanent industrial crops", + "children": { + "B31": "Sunflower", + "B32": "Rape and turnip rape", + "B33": "Soya", + "B34": "Cotton", + "B35": "Other fibre and oleaginous crops", + "B36": "Tobacco", + "B37": "Other non-permanent industrial crops" + } + }, + "B4": { + "value": "Dry pulses, vegetables and flowers", + "children": { + "B41": "Dry pulses", + "B42": "Tomatoes", + "B43": "Other fresh vegetables", + "B44": "Floriculture and ornamental plants", + "B45": "Strawberries" + } + }, + "B5": { + "value": "Fodder crops", + "children": { + "B51": "Clovers", + "B52": "Lucerne", + "B53": "Other leguminous and mixtures for fodder", + "B54": "Mixed cereals for fodder", + "B55": "Temporary grasslands" + } + }, + "B7": { + "value": "Permanent crops: fruit trees", + "children": { + "B71": "Apple fruit", + "B72": "Pear fruit", + "B73": "Cherry fruit", + "B74": "Nuts trees", + "B75": "Other fruit trees and berries", + "B76": "Oranges", + "B77": "Other citrus fruit" + } + }, + "B8": { + "value": "Other permanent crops", + "children": { + "B81": "Olive groves", + "B82": "Vineyards", + "B83": "Nurseries", + "B84": "Permanent industrial crops" + } + } + } + }, + "C": { + "value": "Woodland", + "children": { + "C1": "Broadleaved woodland", + "C2": { + "value": "Coniferous woodland", + "children": { + "C21": "Spruce dominated coniferous woodland", + "C22": "Pine dominated coniferous woodland", + "C23": "Other coniferous woodland" + } + }, + "C3": { + "value": "Mixed woodland", + "children": { + "C31": "Spruce dominated mixed woodland", + "C32": "Pine dominated mixed woodland", + "C33": "Other mixed woodland" + } + } + } + }, + "D": { + "value": "Shrubland", + "children": { + "D1": "Shrubland with sparse tree cover", + "D2": "Shrubland without tree cover" + } + }, + "E": { + "value": "Grassland", + "children": { + "E1": "Grassland with sparse tree/shrub cover", + "E2": "Grassland without tree/shrub cover", + "E3": "Spontaneously re-vegetated surfaces" + } + }, + "F": { + "value": "Bare land and lichens/moss", + "children": { + "F1": "Rocks and stones", + "F2": "Sand", + "F3": "Lichens and moss", + "F4": "Other bare soil" + } + }, + "G": { + "value": "Water areas", + "children": { + "G1": { + "value": "Inland water bodies", + "children": { + "G11": "Inland fresh water bodies", + "G12": "Inland salty water bodies" + } + }, + "G2": { + "value": "Inland running water", + "children": { + "G21": "Inland fresh running water", + "G22": "Inland salty running water" + } + }, + "G3": "Transitional water bodies", + "G4": "Sea and ocean", + "G5": "Glaciers, permanent snow" + } + }, + "H": { + "value": "Wetlands", + "children": { + "H1": { + "value": "Inland wetlands", + "children": { + "H11": "Inland marshes", + "H12": "Peatbogs" + } + }, + "H2": { + "value": "Coastal wetlands", + "children": { + "H21": "Salt marshes", + "H22": "Salines and other chemical deposits", + "H23": "Intertidal flats" + } + } + } + } +} diff --git a/src/fuse_esdac/codebooks/lucas_lu.json b/src/fuse_esdac/codebooks/lucas_lu.json new file mode 100644 index 0000000000000000000000000000000000000000..f5b0592b124831b91bc34aab064d5c2518aa4226 --- /dev/null +++ b/src/fuse_esdac/codebooks/lucas_lu.json @@ -0,0 +1,96 @@ +{ + "1": { + "value": "Primary sector", + "children": { + "11": { + "value": "Agriculture", + "children": { + "111": "Agriculture (excluding fallow land and kitchen gardens)", + "112": "Fallow land" + } + }, + "12": "Forestry", + "13": "Aquaculture and fishing", + "14": "Mining and quarrying", + "15": "Other primary production" + } + }, + "2": { + "value": "Secondary sector", + "children": { + "21": "Energy production", + "22": { + "value": "Industry and manufacturing", + "children": { + "221": "Manufacturing of food, beverages and tobacco products", + "222": "Manufacturing of textile products", + "223": "Coal, oil and metal processing", + "224": "Production of non-metal mineral goods", + "225": "Chemical and allied industries and manufacturing", + "226": "Machinery and equipment", + "227": "Wood based products", + "228": "Printing and reproduction" + } + } + } + }, + "3": { + "value": "Tertiary sector, transport, utilities & residential", + "children": { + "31": { + "value": "Transport, communication networks, storage, protection works", + "children": { + "311": "Railway transport", + "312": "Road transport", + "313": "Water transport", + "314": "Air transport", + "315": "Transport via pipelines", + "316": "Telecommunication", + "317": "Logistics and storage", + "318": "Protection infrastructures", + "319": "Electricity, gas and thermal power distribution" + } + }, + "32": { + "value": "Water and waste treatment", + "children": { + "321": "Water supply and treatment", + "322": "Waste treatment" + } + }, + "33": "Construction", + "34": { + "value": "Commerce, financial, professional and information services", + "children": { + "341": "Commerce", + "342": "Financial, professional and information services" + } + }, + "35": "Community services", + "36": { + "value": "Recreation, leisure, sport", + "children": { + "361": "Amenities, museums, leisure", + "362": "Sport" + } + }, + "37": "Residential" + } + }, + "4": { + "value": "Unused and abandoned areas", + "children": { + "41": { + "value": "Abandoned areas", + "children": { + "411": "Abandoned industrial areas", + "412": "Abandoned commercial areas", + "413": "Abandoned transport areas", + "414": "Abandoned residential areas", + "415": "Other abandoned areas" + } + }, + "42": "Semi-natural and natural areas not in use" + } + } +} diff --git a/src/fuse_esdac/codebooks/lucas_mix.json b/src/fuse_esdac/codebooks/lucas_mix.json new file mode 100644 index 0000000000000000000000000000000000000000..2d92bf77f9f963820ef91b6c9d56f62360b4f99c --- /dev/null +++ b/src/fuse_esdac/codebooks/lucas_mix.json @@ -0,0 +1,69 @@ +{ + "plot_area_size (ha)": { + "1": "< 0.5", + "2": "0.5 ~ 1", + "3": "1 ~ 10", + "4": "> 10", + "8": "Not Relevant" + }, + "tree_height_maturity (m)": { + "1": "< 5", + "2": "> 5", + "8": "Not Relevant" + }, + "sign_of_grazing": { + "1": "Yes", + "2": "No", + "8": "Not Relevant" + }, + "sign_of_ploughing": { + "1": "Yes", + "2": "No", + "8": "Not Relevant" + }, + "cover_of_residual_crop (%)": { + "1": "< 10", + "2": "10 ~ 25", + "3": "25 ~ 50", + "4": "> 50", + "8": "Not Relevant" + }, + "cover_of_stones (%)": { + "1": "< 10", + "2": "10 ~ 25", + "3": "25 ~ 50", + "4": "> 50", + "8": "Not Relevant" + }, + "water_management_measure": { + "1": "Irrigation", + "2": "Potential irrigation", + "3": "Drainage", + "4": "Irrigation and drainage", + "5": "No visible water management", + "8": "Not relevant" + }, + "water_management_irrigation_source": { + "1": "Well", + "2": "Pond/Lake/Reservoir", + "3": "Stream/Canal/Ditch", + "4": "Lagoon/Wastewater", + "5": "Other / not identifiable", + "8": "Not relevant" + }, + "water_management_irrigation_type": { + "1": "Gravity", + "2": "Pressure: sprinkler irrigation", + "3": "Pressure: micro-irrigation", + "4": "Gravity/Pressure", + "5": "Other / not identifiable", + "8": "Not relevant" + }, + "water_management_delivery": { + "1": "Canal", + "2": "Ditch", + "3": "Pipeline", + "5": "Other / not identifiable", + "8": "Not relevant" + } +} diff --git a/src/fuse_esdac/codebooks/spade.json b/src/fuse_esdac/codebooks/spade.json new file mode 100644 index 0000000000000000000000000000000000000000..7334b6f69f33ccc77bd5876d2652be117233165d --- /dev/null +++ b/src/fuse_esdac/codebooks/spade.json @@ -0,0 +1,334 @@ +{ + "gravel_type": { + "0": "No stone or gravel", + "1": "Very few, <5% by volume", + "2": "Few, 5–15% by volume", + "3": "Frequent or many, 15–40% by volume", + "4": "Very frequent or very many, 40–80% by volume", + "5": "Dominant or skeletal, >80% by volume" + }, + "structure_type": { + "1": "Platy", + "2": "Prismatic", + "3": "Columnar", + "4": "Angular blocky", + "5": "Subangular blocky", + "6": "Granular", + "7": "Crumb", + "8": "Massive", + "9": "Single grain", + "10": "Wedge shaped" + }, + "WRB_soil_group": { + "A": "Acrisol", + "Af": "Ferric Acrisol", + "Ag": "Gleyic Acrisol", + "Ah": "Humic Acrisol", + "Ao": "Orthic Acrisol", + "Ap": "Plinthic Acrisol", + "B": "Cambisol", + "Ba": "Calcaric Cambisol", + "Bc": "Chromic Cambisol", + "Bcc": "Calcaro-Chromic Cambisol", + "Bch": "Humo-Chromic Cambisol", + "Bck": "Calci-Chromic Cambisol", + "Bd": "Dystric Cambisol", + "Bda": "Ando-Dystric Cambisol", + "Bdg": "Gleyo-Dystric Cambisol", + "Bds": "Spodo-Dystric Cambisol", + "Be": "Eutric Cambisol", + "Bea": "Ando-Eutric Cambisol", + "Bec": "Calcaro-Eutric Cambisol", + "Bef": "Fluvi-Eutric Cambisol", + "Beg": "Gleyo-Eutric Cambisol", + "Bev": "Verti-Eutric Cambisol", + "Bg": "Gleyic Cambisol", + "Bgc": "Calcaro-Gleyic Cambisol", + "Bge": "Eutri-Gleyic Cambisol", + "Bgg": "Stagno-Gleyic Cambisol", + "Bgs": "Spodo-Gleyic Cambisol", + "Bh": "Humic Cambisol", + "Bhc": "Calcaro-Humic Cambisol", + "Bk": "Calcic Cambisol", + "Bkf": "Fluvi-Calcic Cambisol", + "Bkh": "Humo-Calcic Cambisol", + "Bkv": "Verti-Calcic Cambisol", + "Bv": "Vertic Cambisol", + "Bvc": "Calcaro-Vertic Cambisol", + "Bvg": "Gleyo-Vertic Cambisol", + "Bvk": "Calci-Vertic Cambisol", + "Bx": "Gelic Cambisol", + "Bxs": "Spodo-Gelic Cambisol", + "C": "Chernozem", + "Ch": "Haplic Chernozem", + "Chp": "Pachi-Haplic Chernozem", + "Chv": "Verti-Haplic Chernozem", + "Ck": "Calcic Chernozem", + "Ckb": "Vermi-Calcic Chernozem", + "Ckc": "Calcaro-Calcic Chernozem", + "Ckcb": "Vermi-Calcaro-Calcic Chernozem", + "Ckp": "Pachi-Calcic Chernozem", + "Cl": "Luvic Chernozem", + "D": "Podzoluvisol", + "Dd": "Dystric Podzoluvisol", + "De": "Eutric Podzoluvisol", + "Dg": "Gleyic Podzoluvisol", + "Dgd": "Dystric Gleyic Podzoluvisol", + "Dge": "Eutric Gleyic Podzoluvisol", + "Dgs": "Stagno-Gleyic Podzoluvisol", + "E": "Rendzina", + "Ec": "Cambic Rendzina", + "Eh": "Histic Rendzina", + "Eo": "Orthic Rendzina", + "F": "Ferralsol", + "Fo": "Orthic Ferralsol", + "G": "Gleysol", + "Gc": "Calcaric Gleysol", + "Gcf": "Fluvi-Calcaric Gleysol", + "Gcs": "Stagno-Calcaric Gleysol", + "Gd": "Dystric Gleysol", + "Gdf": "Fluvi-Dystric Gleysol", + "Gds": "Stagno-Dystric Gleysol", + "Ge": "Eutric Gleysol", + "Gef": "Fluvi-Eutric Gleysol", + "Ges": "Stagno-Eutric Gleysol", + "Gev": "Verti-Eutric Gleysol", + "Gf": "Fluvic Gleysol", + "Gfm": "Molli-Fluvic Gleysol", + "Gh": "Humic Gleysol", + "Ghf": "Fluvi-Humic Gleysol", + "Ghh": "Histo-Humic Gleysol", + "Ght": "Thioni-Humic Gleysol", + "Gi": "Histic Gleysol", + "Gih": "Humo-Histic Gleysol", + "Gl": "Luvic Gleysol", + "Gls": "Stagno-Luvic Gleysol", + "Gm": "Mollic Gleysol", + "Gmc": "Calcaro-Mollic Gleysol", + "Gmf": "Fluvi-Mollic Gleysol", + "Gmv": "Verti-Mollic Gleysol", + "Gs": "Stagnic Gleysol", + "Gt": "Thionic Gleysol", + "H": "Phaeozem", + "Hc": "Calcaric Phaeozem", + "Hcf": "Fluvi-Calcaric Phaeozem", + "Hcn": "Alkalino-Calcaric Phaeozem", + "Hcs": "Saline-Calcaric Phaeozem", + "Hg": "Gleyic Phaeozem", + "Hgc": "Calcaro-Gleyic Phaeozem", + "Hgf": "Fluvi-Gleyic Phaeozem", + "Hgs": "Stagno-Gleyic Phaeozem", + "Hgv": "Verti-Gleyic Phaeozem", + "Hh": "Haplic Phaeozem", + "Hhv": "Verti-Haplic Phaeozem", + "Hl": "Luvic Phaeozem", + "Hlv": "Verti-Luvic Phaeozem", + "Ho": "Orthic Phaeozem", + "I": "Lithosol", + "Ic": "Calcaric Lithosol", + "Ich": "Humo-Calcaric Lithosol", + "Id": "Dystric Lithosol", + "Ie": "Eutric Lithosol", + "J": "Fluvisol", + "Jc": "Calcaric Fluvisol", + "Jcf": "Fluvi-Calcaric Fluvisol", + "Jcg": "Gleyo-Calcaric Fluvisol", + "Jd": "Dystric Fluvisol", + "Jdf": "Fluvi-Dystric Fluvisol", + "Jdg": "Gleyo-Dystric Fluvisol", + "Je": "Eutric Fluvisol", + "Jef": "Fluvi-Eutric Fluvisol", + "Jeg": "Gleyo-Eutric Fluvisol", + "Jm": "Mollic Fluvisol", + "Jmg": "Gleyo-Mollic Fluvisol", + "Jmv": "Verti-Mollic Fluvisol", + "Jt": "Thionic Fluvisol", + "K": "Kastanozem", + "Kh": "Haplic Kastanozem", + "Khb": "Vermi-Haplic Kastanozem", + "Kk": "Calcic Kastanozem", + "Kkb": "Vermi-Calcic Kastanozem", + "Kkv": "Verti-Calcic Kastanozem", + "Kl": "Luvic Kastanozem", + "Ko": "Orthic Kastanozem", + "L": "Luvisol", + "La": "Albic Luvisol", + "Lap": "Plano-Albic Luvisol", + "Lc": "Chromic Luvisol", + "Lcp": "Plano-Chromic Luvisol", + "Lcr": "Rhodo-Chromic Luvisol", + "Lcv": "Verti-Chromic Luvisol", + "Ld": "Dystric Luvisol", + "Ldg": "Gleyo-Dystric Luvisol", + "Lf": "Ferric Luvisol", + "Lg": "Gleyic Luvisol", + "Lga": "Albo-Gleyic Luvisol", + "Lgp": "Plano-Gleyic Luvisol", + "Lgs": "Stagno-Gleyic Luvisol", + "Lh": "Humic Luvisol", + "Lk": "Calcic Luvisol", + "Lkc": "Chromo-Calcic Luvisol", + "Lkcr": "Rhodo-Chromo-Calcic Luvisol", + "Lkv": "Verti-Calcic Luvisol", + "Lo": "Orthic Luvisol", + "Lop": "Plano-Orthic Luvisol", + "Lp": "Plinthic Luvisol", + "Ls": "Spodic Luvisol", + "Lv": "Vertic Luvisol", + "Lvc": "Chromo-Vertic Luvisol", + "Lvcr": "Rhodo-Chromo-Vertic Luvisol", + "Lvk": "Calci-Vertic Luvisol", + "M": "Greyzem", + "Mo": "Orthic Greyzem", + "O": "Histosol", + "Od": "Dystric Histosol", + "Odp": "Placi-Dystric Histosol", + "Oe": "Eutric Histosol", + "Ox": "Gelic Histosol", + "P": "Podzol", + "Pf": "Ferric Podzol", + "Pg": "Gleyic Podzol", + "Pgh": "Histo-Gleyic Podzol", + "Pgs": "Stagno-Gleyic Podzol", + "Ph": "Humic Podzol", + "Phf": "Ferro-Humic Podzol", + "Pl": "Leptic Podzol", + "Plh": "Humo-Leptic Podzol", + "Po": "Orthic Podzol", + "Pof": "Ferro-Orthic Podzol", + "Poh": "Humo-Orthic Podzol", + "Pol": "Lepto-Orthic Podzol", + "Pp": "Placic Podzol", + "Pph": "Humo-Placic Podzol", + "Q": "Arenosol", + "Qa": "Albic Arenosol", + "Qc": "Cambic Arenosol", + "Qcc": "Calcaro-Cambic Arenosol", + "Qcd": "Dystri-Cambic Arenosol", + "Qcg": "Gleyo-Cambic Arenosol", + "Qcs": "Spodo-Cambic Arenosol", + "Ql": "Luvic Arenosol", + "Qld": "Dystri-Luvic Arenosol", + "Qlg": "Gleyo-Luvic Arenosol", + "R": "Regosol", + "Rc": "Calcaric Regosol", + "Rd": "Dystric Regosol", + "Re": "Eutric Regosol", + "S": "Solonetz", + "Sg": "Gleyic Solonetz", + "Sm": "Mollic Solonetz", + "So": "Orthic Solonetz", + "Sof": "Fluvi-Orthic Solonetz", + "T": "Andosol", + "Th": "Humic Andosol", + "Tm": "Mollic Andosol", + "To": "Ochric Andosol", + "Tv": "Vitric Andosol", + "U": "Ranker", + "Ud": "Dystric Ranker", + "Ul": "Luvic Ranker", + "V": "Vertisol", + "Vc": "Chromic Vertisol", + "Vcc": "Calcaro-Chromic Vertisol", + "Vg": "Gleyic Vertisol", + "Vp": "Pellic Vertisol", + "Vpc": "Calcaro-Pellic Vertisol", + "Vpg": "Gleyo-Pellic Vertisol", + "Vpn": "Sodi-Pellic Vertisol", + "W": "Planosol", + "Wd": "Dystric Planosol", + "Wdv": "Verti-Dystric Planosol", + "We": "Eutric Planosol", + "Wev": "Verti-Eutric Planosol", + "Wm": "Mollic Planosol", + "X": "Xerosol", + "Xk": "Calcic Xerosol", + "Xl": "Luvic Xerosol", + "Xy": "Gypsic Xerosol", + "Z": "Solonchak", + "Zg": "Gleyic Solonchak", + "Zgf": "Fluvi-Gleyic Solonchak", + "Zo": "Orthic Solonchak", + "Zt": "Takyric Solonchak", + "g": "Glacier", + "p": "Plaggensol", + "r": "Rock Outcrop", + "zzz": "undefined", + "LVc": "Calcaric Luvisol" + }, + "groundwater_table_level": { + "1": "Groundwater table between 0-50 cm", + "2": "Groundwater table between 50-100 cm", + "3": "Groundwater table between 100-150 cm", + "4": "Groundwater table between 150-200 cm", + "5": "Groundwater table below 200 cm" + }, + "lu": { + "1": "Artificial surfaces", + "2": "Agricultural areas", + "3": "Forest and semi-natural areas", + "4": "Wetland", + "5": "Water bodies", + "11": "Urban fabric", + "12": "Industrial, commercial and transport units", + "13": "Mines, dumps and construction sites", + "14": "Artificial non-agricultural vegetated areas", + "21": "Arable land", + "22": "Permanent crops", + "23": "Pastures", + "24": "Heterogeneous agricultural areas", + "31": "Forests", + "32": "Scrub and/or herbaceous vegetation associations", + "33": "Open spaces with little or no vegetation", + "41": "Inland wetlands", + "42": "Coastal wetlands", + "51": "Continental waters", + "52": "Marine waters", + "111": "Continuous urban fabric", + "112": "Discontinuous urban fabric", + "121": "Industrial or commercial units", + "122": "Road and rail networks and associated land", + "123": "Sea ports", + "124": "Airport", + "131": "Mineral extraction site", + "132": "Dump", + "133": "Construction site", + "141": "Green urban areas", + "142": "Sport and leisure facilities", + "211": "Non-irrigated arable land", + "212": "Permanently irrigated land", + "213": "Rice fields", + "221": "Vineyards", + "222": "Fruit trees and berries plantations", + "223": "Olive groves", + "231": "Pastures", + "241": "Annual crops associated with permanent crops", + "242": "Complex cultivation patterns", + "243": "Land principally occupied by agriculture with significant natural vegetation", + "244": "Agro-forestry areas", + "311": "Broad leaved forest", + "312": "Coniferous forest", + "313": "Mixed forest", + "321": "Natural grassland", + "322": "Moors and heathland", + "323": "Sclerophylous vegetation", + "324": "Transitional woodland-scrub", + "331": "Beaches, dunes, sand", + "332": "Bare rocks", + "333": "Sparsely vegetated areas", + "334": "Burnt areas", + "335": "Glacier and permanent snow fields", + "412": "Peat bogs", + "421": "Salt marshes", + "422": "Salines", + "423": "Intertidal flats", + "441": "Inland marshes", + "490": "Missing data", + "500": "Sea mask", + "511": "Stream courses", + "512": "Water bodies", + "521": "Coastal lagoons", + "522": "Estuaries", + "523": "Sea and Ocean" + } +} diff --git a/src/fuse_esdac/codebooks/unsd_eu.csv b/src/fuse_esdac/codebooks/unsd_eu.csv new file mode 100644 index 0000000000000000000000000000000000000000..11243834086c0808ab5d50fcc45405cc09927294 --- /dev/null +++ b/src/fuse_esdac/codebooks/unsd_eu.csv @@ -0,0 +1,54 @@ +ο»Ώ001;World;150;Europe;151;Eastern Europe;Eastern Continental;EACN;Belarus;112;BY;BLR;;; +001;World;150;Europe;151;Eastern Europe;Eastern Continental;EACN;Ukraine;804;UA;UKR;;; +001;World;150;Europe;151;Eastern Europe;Russian;RUSS;Russian Federation;643;RU;RUS;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Bulgaria;100;BG;BGR;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Republic of Moldova;498;MD;MDA;;; +001;World;150;Europe;153;Central Europe;Central European Group;CENT;Czechia;203;CZ;CZE;;; +001;World;150;Europe;153;Central Europe;Central European Group;CENT;Hungary;348;HU;HUN;;; +001;World;150;Europe;153;Central Europe;Central European Group;CENT;Poland;616;PL;POL;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Romania;642;RO;ROU;;; +001;World;150;Europe;153;Central Europe;Central European Group;CENT;Slovakia;703;SK;SVK;;; +001;World;150;Europe;154;Northern Europe;Nordic Countries;NORD;Γ…land Islands;248;AX;ALA;;; +001;World;150;Europe;154;Northern Europe;Scandinavia;SCAN;Denmark;208;DK;DNK;;; +001;World;150;Europe;154;Northern Europe;Baltic States;BALT;Estonia;233;EE;EST;;; +001;World;150;Europe;154;Northern Europe;Nordic Countries;NORD;Faroe Islands;234;FO;FRO;;; +001;World;150;Europe;154;Northern Europe;Nordic Countries;NORD;Finland;246;FI;FIN;;; +001;World;150;Europe;154;Northern Europe;British Isles;BRIT;Guernsey;831;GG;GGY;;; +001;World;150;Europe;154;Northern Europe;Nordic Countries;NORD;Iceland;352;IS;ISL;;; +001;World;150;Europe;154;Northern Europe;British Isles;BRIT;Ireland;372;IE;IRL;;; +001;World;150;Europe;154;Northern Europe;British Isles;BRIT;Isle of Man;833;IM;IMN;;; +001;World;150;Europe;154;Northern Europe;British Isles;BRIT;Jersey;832;JE;JEY;;; +001;World;150;Europe;154;Northern Europe;Baltic States;BALT;Latvia;428;LV;LVA;;; +001;World;150;Europe;154;Northern Europe;Baltic States;BALT;Lithuania;440;LT;LTU;;; +001;World;150;Europe;154;Northern Europe;Scandinavia;SCAN;Norway;578;NO;NOR;;; +001;World;150;Europe;154;Northern Europe;Nordic Countries;NORD;Svalbard and Jan Mayen Islands;744;SJ;SJM;;; +001;World;150;Europe;154;Northern Europe;Scandinavia;SCAN;Sweden;752;SE;SWE;;; +001;World;150;Europe;154;Northern Europe;British Isles;BRIT;United Kingdom of Great Britain and Northern Ireland;826;GB;GBR;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Albania;008;AL;ALB;;; +001;World;150;Europe;039;Southern Europe;Iberian Peninsula;IBER;Andorra;020;AD;AND;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Bosnia and Herzegovina;070;BA;BIH;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Croatia;191;HR;HRV;;; +001;World;150;Europe;039;Southern Europe;Mediterranean Islands;MEDI;Gibraltar;292;GI;GIB;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Greece;300;GR;GRC;;; +001;World;150;Europe;039;Southern Europe;Apennine Peninsula;APEN;Holy See;336;VA;VAT;;; +001;World;150;Europe;039;Southern Europe;Apennine Peninsula;APEN;Italy;380;IT;ITA;;; +001;World;150;Europe;039;Southern Europe;Mediterranean Islands;MEDI;Malta;470;MT;MLT;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Montenegro;499;ME;MNE;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;North Macedonia;807;MK;MKD;;x; +001;World;150;Europe;039;Southern Europe;Iberian Peninsula;IBER;Portugal;620;PT;PRT;;; +001;World;150;Europe;039;Southern Europe;Apennine Peninsula;APEN;San Marino;674;SM;SMR;;; +001;World;150;Europe;039;Southern Europe;Balkan Peninsula;BALK;Serbia;688;RS;SRB;;; +001;World;150;Europe;153;Central Europe;Central European Group;CENT;Slovenia;705;SI;SVN;;; +001;World;150;Europe;039;Southern Europe;Iberian Peninsula;IBER;Spain;724;ES;ESP;;; +001;World;150;Europe;155;Western Europe;DACH Region;DACH;Austria;040;AT;AUT;;; +001;World;150;Europe;155;Western Europe;Benelux;BENE;Belgium;056;BE;BEL;;; +001;World;150;Europe;155;Western Europe;France and Monaco;FRMO;France;250;FR;FRA;;; +001;World;150;Europe;155;Western Europe;DACH Region;DACH;Germany;276;DE;DEU;;; +001;World;150;Europe;155;Western Europe;DACH Region;DACH;Liechtenstein;438;LI;LIE;;; +001;World;150;Europe;155;Western Europe;Benelux;BENE;Luxembourg;442;LU;LUX;;; +001;World;150;Europe;155;Western Europe;France and Monaco;FRMO;Monaco;492;MC;MCO;;; +001;World;150;Europe;155;Western Europe;Benelux;BENE;Netherlands (Kingdom of the);528;NL;NLD;;; +001;World;150;Europe;155;Western Europe;DACH Region;DACH;Switzerland;756;CH;CHE;;; +001;World;150;Europe;039;Southern Europe;Mediterranean Islands;MEDI;Cyprus;196;CY;CYP;;; +001;World;150;Europe;039;Southern Europe;Mediterranean Islands;MEDI;Akrotiri and Dhekelia;998;XA;XAD;;; +001;World;150;Europe;039;Southern Europe;Mediterranean Islands;MEDI;Northern Cyprus;999;ZN;ZNC;;; diff --git a/src/fuse_esdac/codebooks/unsd_eu_alias.json b/src/fuse_esdac/codebooks/unsd_eu_alias.json new file mode 100644 index 0000000000000000000000000000000000000000..a40310a26c9c2c5d5a387dc77b69b8b7a0b8c513 --- /dev/null +++ b/src/fuse_esdac/codebooks/unsd_eu_alias.json @@ -0,0 +1,124 @@ +{ + "EU": [ + "Europe", + "European Continent", + "European Region", + "Continent of Europe" + ], + "N_EU": [ + "Northern Europe", + "North Europe", + "Northern European Region", + "North European Region", + "Region of Northern Europe" + ], + "S_EU": [ + "Southern Europe", + "South Europe", + "Southern European Region", + "South European Region", + "Region of Southern Europe" + ], + "W_EU": [ + "Western Europe", + "West Europe", + "Western European Region", + "West European Region", + "Region of Western Europe" + ], + "E_EU": [ + "Eastern Europe", + "East Europe", + "Eastern European Region", + "East European Region", + "Region of Eastern Europe" + ], + "C_EU": [ + "Central Europe", + "Central European Region", + "Region of Central Europe" + ], + "EACN": [ + "Eastern Continental", + "Eastern Continental Europe", + "East Continental", + "East Continental Europe", + "Eastern Continental Region" + ], + "RUSS": [ + "Russian", + "Russian Region", + "Russian Area", + "Russian Subregion" + ], + "BALK": [ + "Balkan Peninsula", + "Balkans", + "Balkan Region", + "Balkan Area" + ], + "CENT": [ + "Central European Group", + "Central Europe Group", + "Central European Region" + ], + "NORD": [ + "Nordic Countries", + "Nordic Region", + "Nordic Europe", + "Nordic Area" + ], + "SCAN": [ + "Scandinavia", + "Scandinavian Region", + "Scandinavian Europe", + "Scandinavian Area" + ], + "BALT": [ + "Baltic States", + "Baltic Region", + "Baltic Europe", + "Baltic Area" + ], + "BRIT": [ + "British Isles", + "Britain and Ireland", + "British Region", + "British Isles Region" + ], + "IBER": [ + "Iberian Peninsula", + "Iberia", + "Iberian Region", + "Iberian Area" + ], + "MEDI": [ + "Mediterranean Islands", + "Mediterranean Island Region", + "Mediterranean Europe Islands", + "European Mediterranean Islands" + ], + "APEN": [ + "Apennine Peninsula", + "Apennine Region", + "Apennine Europe" + ], + "DACH": [ + "DACH Region", + "DACH", + "German-speaking Europe", + "DACH Countries" + ], + "BENE": [ + "Benelux", + "Benelux Region", + "Benelux Countries", + "Benelux Area" + ], + "FRMO": [ + "France and Monaco", + "France & Monaco", + "French-Monaco Region", + "France–Monaco Region" + ] +} diff --git a/src/fuse_esdac/codebooks/unsd_original.csv b/src/fuse_esdac/codebooks/unsd_original.csv new file mode 100644 index 0000000000000000000000000000000000000000..ffbf083ea8e1cb50869f045321b105a07aea5089 --- /dev/null +++ b/src/fuse_esdac/codebooks/unsd_original.csv @@ -0,0 +1,249 @@ +ο»ΏGlobal Code;Global Name;Region Code;Region Name;Sub-region Code;Sub-region Name;Intermediate Region Code;Intermediate Region Name;Country or Area;M49 Code;ISO-alpha2 Code;ISO-alpha3 Code;Least Developed Countries (LDC);Land Locked Developing Countries (LLDC);Small Island Developing States (SIDS) +001;World;002;Africa;015;Northern Africa;;;Algeria;012;DZ;DZA;;; +001;World;002;Africa;015;Northern Africa;;;Egypt;818;EG;EGY;;; 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+001;World;009;Oceania;061;Polynesia;;;Pitcairn;612;PN;PCN;;; +001;World;009;Oceania;061;Polynesia;;;Samoa;882;WS;WSM;;;x +001;World;009;Oceania;061;Polynesia;;;Tokelau;772;TK;TKL;;; +001;World;009;Oceania;061;Polynesia;;;Tonga;776;TO;TON;;;x +001;World;009;Oceania;061;Polynesia;;;Tuvalu;798;TV;TUV;x;;x +001;World;009;Oceania;061;Polynesia;;;Wallis and Futuna Islands;876;WF;WLF;;; \ No newline at end of file diff --git a/src/fuse_esdac/configs/01_sample.json b/src/fuse_esdac/configs/01_sample.json new file mode 100644 index 0000000000000000000000000000000000000000..09dd1fb117cba88d908b833e5a1c671da92cf9c3 --- /dev/null +++ b/src/fuse_esdac/configs/01_sample.json @@ -0,0 +1,11 @@ +{ + "input": null, + "datasets": [ + "lucas-2009-topsoil-data", + "lucas-2015-topsoil-data-switzerland", + "lucas2015-topsoil-data", + "lucas-2018-topsoil-data", + "european-hydropedological-data-inventory-eu-hydi-database-0", + "spadem" + ] +} diff --git a/src/fuse_esdac/configs/02_extend.json b/src/fuse_esdac/configs/02_extend.json new file mode 100644 index 0000000000000000000000000000000000000000..5976b44859fc35aac9c37dc7a5007b63552a3fd6 --- /dev/null +++ b/src/fuse_esdac/configs/02_extend.json @@ -0,0 +1,67 @@ +{ + "input": "01_sample", + "datasets": [ + "soil-carbon-risk-index", + "3d-soil-hydraulic-database-europe-1-km-and-250-m-resolution", + "arsenic-european-topsoils", + "bacterial-fungal-biomass-fatty-acid-methyl-esters", + "biodiversity-factor-soil-erosion", + "cadmium-topsoils-european-union", + "caesium-137-and-plutonium-239240-european-topsoils", + "carbon-budget-eu-agricultural-soils", + "clay-mineral-inventory-soils-europe-based-lucas-2015-survey-soil-samples-2", + "copper-distribution-topsoils", + "cover-crops-accross-europe", + "cover-management-factor-c-factor-eu", + "estimate-net-erosion-and-sediment-transport-using-watemsedem-european-union", + "european-food-safety-authority-efsa-data-persam-software-tool", + "european-map-soil-suitability-provide-platform-most-human-activities-eu28", + "european-soil-database-derived-data", + "european-soil-database-v2-raster-library-1kmx1km", + "global-rainfall-erosivity", + "global-soil-erodibility", + "global-soil-organic-carbon-estimates", + "glosem", + "gully-erosion-based-lucas", + "heavy-metals-topsoils", + "land-degradation-europe", + "land-suitability-temperate-europe", + "ls-factor-slope-length-and-steepness-factor-eu", + "map-indicating-availability-raw-material-soils-european-union-organic-soil-material-b-soil", + "maps-heavy-metals-soils-eu-based-lucas-2009-hm-data-0", + "maps-indicators-soil-hydraulic-properties-europe", + "maps-related-predicting-preservation-cultural-artefacts-and-buried-materials-soils-eu-0", + "maps-storing-and-filtering-capacity-soils-europe", + "mercury-content-european-union-topsoil", + "multiple-concurrent-soil-erosion-processes", + "n2o-emissions-agricultural-soils-europe", + "natural-susceptibility-soil-compaction-europe", + "observedtypical-soc-index", + "octop-topsoil-organic-carbon-content-europe", + "pan-european-soc-stock-agricultural-soils", + "pan-european-soil-erosion-risk-assessment-pesera", + "phosphorus-budget-and-p-stocks", + "phosphorus-cycle-european-agricultural-soils", + "potential-threats-soil-biodiversity-europe", + "priming-effects-soils-across-europe", + "rainfall-erosivity-european-union-and-switzerland", + "SOC-changes-2009-18", + "soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european", + "soil-bulk-density-europe", + "soil-degradation-indicators-eu", + "soil-erodibility-k-factor-high-resolution-dataset-europe", + "soil-erosion-forestland-europe-using-rusle2015", + "soil-erosion-water-rusle2015", + "soil-function-data-measured-lucas-2018-sites-across-eu", + "soil-ghg-fluxes-using-lucas-soil-daycent", + "soil-loss-due-crop-harvesting-european-union", + "soil-microbial-biomass-and-respiration", + "soil-organic-carbon-saturation-capacity", + "soil-organic-matter-som-fractions", + "Soil_erosion_by_wind", + "support-practices-factor-p-factor-eu", + "topsoil-soil-organic-carbon-lucas-eu25", + "un-sustainable-development-goal-1531-assessment-land-degradation-indicator-eu-scale", + "zn-concentrations-eu-topsoils" + ] +} diff --git a/src/fuse_esdac/large_inputs/existing_descritpions.json b/src/fuse_esdac/large_inputs/existing_descritpions.json new file mode 100644 index 0000000000000000000000000000000000000000..1b5d55f14021538cf00bc81709e86838f3db1791 --- /dev/null +++ b/src/fuse_esdac/large_inputs/existing_descritpions.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d7cc552633be102c3eb86d6d4e7730c65f930142f417ec1aa8561b78f44a0ce +size 433092 diff --git a/src/fuse_esdac/large_inputs/gadm_tree.pkl b/src/fuse_esdac/large_inputs/gadm_tree.pkl new file mode 100644 index 0000000000000000000000000000000000000000..bd01c44c4cad7dec6137c1e38ced902a2de5488a --- /dev/null +++ b/src/fuse_esdac/large_inputs/gadm_tree.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9fa1d1b1a9b964506c74f0d3f24e274b61ef4d8bb9a7603d40681289b9e80cea +size 4714422866 diff --git a/src/fuse_esdac/large_inputs/lucas_photos_by_point.json b/src/fuse_esdac/large_inputs/lucas_photos_by_point.json new file mode 100644 index 0000000000000000000000000000000000000000..2df2b0551d04e29eba50ffe97fc51403e2f375b5 --- /dev/null +++ b/src/fuse_esdac/large_inputs/lucas_photos_by_point.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58f04b3db9c4a8c2eb24ba13dfae5d520c64044f577f4993ca1137074ebd1525 +size 33462439 diff --git a/src/fuse_esdac/locate_lucas_photos.py b/src/fuse_esdac/locate_lucas_photos.py new file mode 100644 index 0000000000000000000000000000000000000000..702712641b76abaeda863134e950a48d3f13dbb7 --- /dev/null +++ b/src/fuse_esdac/locate_lucas_photos.py @@ -0,0 +1,133 @@ +import argparse +import json +import re +import shutil +from pathlib import Path + +import requests +from bs4 import BeautifulSoup +from tqdm import tqdm + +shutil.copy("src/fuse_esdac/large_inputs/lucas_photos_by_point.json", "src/fuse_esdac/outputs/03_photo_gadm") + +# ============================================================ +# Config +# ============================================================ +BASE_URL = "https://gisco-services.ec.europa.eu/lucas/photos/" +OUT_DIR = Path("src/fuse_esdac/outputs") +OUT_DIR.mkdir(exist_ok=True) + +SAMPLES_PATH = OUT_DIR / "01_sample/samples.json" +OUTPUT_PATH = OUT_DIR / "03_photo_gadm/lucas_photos_by_point.json" + + +# ============================================================ +# Apache directory parser +# ============================================================ +def extract_dir_listing(url): + """Return all file URLs under an Apache-style directory.""" + try: + r = requests.get(url, timeout=15) + if r.status_code != 200: + return [] + soup = BeautifulSoup(r.text, "html.parser") + return [ + url + a.get("href") + for a in soup.find_all("a") + if a.get("href") + and not a.get("href").startswith("?") + and not a.get("href").startswith("../") + and not a.get("href").endswith("/") + ] + except Exception: + return [] + + +def safe_save_json(data, path): + tmp = path.with_suffix(".tmp") + with open(tmp, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + tmp.replace(path) + + +# ============================================================ +# CLI setup +# ============================================================ +parser = argparse.ArgumentParser() +parser.add_argument("--redo-empty", action="store_true", + help="Redo samples that were processed but have empty results") +parser.add_argument("--save-every", type=int, default=200, + help="Save progress every N samples (default: 200)") +args = parser.parse_args() + +# ============================================================ +# Load samples and progress +# ============================================================ +with open(SAMPLES_PATH, "r", encoding="utf-8") as f: + samples = json.load(f) +target_keys = {k for k in samples if k.startswith("lucas")} +print(f"βœ… Number of target samples: {len(target_keys)}") + +processed = json.load(open(OUTPUT_PATH, "r", encoding="utf-8")) if OUTPUT_PATH.exists() else {} +processed_keys = ( + {k for k, v in processed.items() if len(v) > 0} + if args.redo_empty else set(processed.keys()) +) +todo_keys = sorted(target_keys - processed_keys) +print(f"βœ… Redo-empty: {args.redo_empty}") +print(f"βœ… Already processed: {len(processed_keys)}") +print(f"βœ… Remaining to process: {len(todo_keys)}") + +# ============================================================ +# Main loop +# ============================================================ +success, fail, counter_since_save = 0, 0, 0 +pbar = tqdm(total=len(todo_keys), ncols=100) + +for key in todo_keys: + sample = samples[key] + + # --- Extract base info --- + try: + year = key[5:9] + pointid = re.search(r"_(\d+)_", key).group(1) + country_code = sample.get("COUNTRY_CODE", "").strip().upper() + if not country_code: + raise ValueError("Missing COUNTRY_CODE") + except Exception: + processed[key] = [] + fail += 1 + pbar.update(1) + pbar.set_description(f"Total:{len(todo_keys)} βœ…{success} ❌{fail}") + continue + + prefix1, prefix2 = pointid[:3], pointid[3:6] + + # --- Directly query directory listing --- + dir_url = f"{BASE_URL}{year}/{country_code}/{prefix1}/{prefix2}/" + files = extract_dir_listing(dir_url) + matched = [f for f in files if re.search(pointid, Path(f).stem)] + + # --- Record result --- + processed[key] = matched + if matched: + success += 1 + else: + fail += 1 + + counter_since_save += 1 + pbar.update(1) + pbar.set_description(f"Total:{len(todo_keys)} βœ…{success} ❌{fail}") + + if counter_since_save >= args.save_every: + safe_save_json(processed, OUTPUT_PATH) + counter_since_save = 0 + pbar.set_postfix_str("πŸ’Ύ saved progress") + +# --- Final save --- +safe_save_json(processed, OUTPUT_PATH) +pbar.close() + +print(f"\nβœ… Finished {len(processed)} / {len(target_keys)}") +print(f"βœ… Success: {success}, ❌ Empty: {fail}") +print(f"βœ… Output β†’ {OUTPUT_PATH}") diff --git a/src/fuse_esdac/prompts/desc.txt b/src/fuse_esdac/prompts/desc.txt new file mode 100644 index 0000000000000000000000000000000000000000..941d8ef962b4382117b6c93005df165448abb346 --- /dev/null +++ b/src/fuse_esdac/prompts/desc.txt @@ -0,0 +1,21 @@ +The following dict shows the metadata of a soil-related variable. The data come from ESDAC. I want you to write a precise and concise description of this variable. + +Professional level requirement: +- Avoid oversimplified layman wording; it does not need to be understandable by the general public. +- Avoid overly technical phrasing that only soil scientists can understand. +- Aim for a middle level suitable for researchers with general scientific background and for modern LLMs to interpret. + +If the variable name is ambiguous or unclear, first consult ESDAC (https://esdac.jrc.ec.europa.eu/) by searching the items listed in "source_datasets" to infer the intended meaning. Use only information that can be confidently derived from those sources. If, after checking, you are still unsure, return an empty string. + +In your answer, only give the plain-text description straightforwardly. + +You must avoid: +1. Copying the dict and putting the description inside it. +2. Giving redundant reasoning before the answer (e.g., "Based on your input, the description is ..."). +3. Adding any message after the description (e.g., "If you want me to do another variable..."). +4. Citations, links, or metadataβ€”**only the description text**. + +Input dict: +{input_dict} + +Your answer here: diff --git a/src/fuse_esdac/utils.py b/src/fuse_esdac/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..24c736723f0eed4ee470241554ccbc69a8433440 --- /dev/null +++ b/src/fuse_esdac/utils.py @@ -0,0 +1,608 @@ +import builtins +import json +import keyword +import math +import re +from datetime import datetime # noqa +from pathlib import Path +from textwrap import indent +from typing import List +from typing import Optional, Callable, Tuple +from typing import Union, Dict, Any + +import numpy as np +import pandas as pd +import rasterio +from pyproj import Geod +from pyproj import Transformer +from rasterio.transform import Affine +from tqdm import tqdm + + +def _nan_to_none(obj): + if isinstance(obj, dict): + return {k: _nan_to_none(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [_nan_to_none(v) for v in obj] + elif isinstance(obj, float) and math.isnan(obj): + return None + else: + return obj + + +def resolve_code(code: Any, + codebook: Union[str, Dict[str, Any]], + top_key: str = None, + ignore_error_codes=None) -> Optional[str]: + """ + Resolve a hierarchical code into its labels by prefix-walking the code. + + - If code is in ignore_error_codes or empty -> return None silently. + - If code not found in codebook and not ignored -> raise KeyError. + - top_key missing is a hard error (still raises). + """ + # normalize ignore list + ignore_error_codes = [] if ignore_error_codes is None else list(ignore_error_codes) + + # accept non-str inputs; only None/empty-string -> None + if code is None or (isinstance(code, str) and not code.strip()): + return None + + # normalize code to string; "3.0" -> "3" + code_str = str(code).strip() + try: + f = float(code_str) + if f.is_integer(): + code_str = str(int(f)) + except Exception: + # keep original string if not a number + pass + + # fast path: ignored codes + if code_str in ignore_error_codes: + return None + + # Load codebook + if isinstance(codebook, str): + try: + with open("src/fuse_esdac/codebooks/" + codebook + ".json", "r", encoding="utf-8") as fs: + codebook_dict = json.load(fs) + except FileNotFoundError as e: + raise FileNotFoundError(f"Codebook file not found: {codebook}") from e + except json.JSONDecodeError as e: + raise ValueError(f"Invalid JSON in codebook file: {codebook}") from e + elif isinstance(codebook, dict): + codebook_dict = codebook + else: + raise TypeError(f"codebook must be a file path (str) or dict, got {type(codebook).__name__}") + + # Pick source dict (top_key remains strict) + src = codebook_dict if top_key is None else codebook_dict.get(top_key) + if src is None: + raise KeyError(f"top_key '{top_key}' not found in codebook") + if not isinstance(src, dict): + raise TypeError(f"Top-level codebook section must be a dict, got {type(src).__name__}") + + labels: List[str] = [] + level_dict = src + + # progressive prefixes + prefixes = [code_str[:i] for i in range(1, len(code_str) + 1)] + + for pref in prefixes: + if not isinstance(level_dict, dict): + raise ValueError(f"Invalid structure: expected dict at prefix '{pref}', got {type(level_dict).__name__}") + + if pref not in level_dict: + break + + node = level_dict[pref] + + if isinstance(node, dict): + if "value" not in node: + raise KeyError(f"Missing 'value' key in node for prefix '{pref}'") + if "children" not in node: + raise KeyError(f"Missing 'children' key in node for prefix '{pref}'") + if not isinstance(node["value"], str): + raise TypeError(f"'value' for prefix '{pref}' must be a string, got {type(node['value']).__name__}") + if not isinstance(node["children"], dict): + raise TypeError(f"'children' for prefix '{pref}' must be a dict, got {type(node['children']).__name__}") + + labels.append(node["value"]) + level_dict = node["children"] + + elif isinstance(node, str): + if not node.strip(): + raise ValueError(f"Empty string label for prefix '{pref}'") + labels.append(node) + break # leaf node, stop walking + + else: + raise TypeError(f"Unexpected node type for prefix '{pref}': {type(node).__name__}") + + # post-check: no match -> ignore or raise + if len(labels) == 0: + if code_str in ignore_error_codes: + return None + raise KeyError(f"Code '{code_str}' not found in codebook") + + return code_str + ": " + " => ".join(labels) + + +def as_float(number: Any, keep_str=True, ignore_substr="-999", factor=1.0) -> Optional[float]: + """Convert to float; empty CSV values -> None; real errors -> raise.""" + if number is None or (isinstance(number, str) and number.strip() == ""): + return None + if ignore_substr is not None and ignore_substr in number: + return None + try: + return float(number) * factor + except (ValueError, TypeError) as e: + if keep_str: + return number.strip() + raise ValueError(f"as_float: cannot convert {number!r}") from e + + +def as_int_id(number: Any, allow_letters: bool = False) -> Optional[str]: + """ + Convert to integer-like string. + If allow_letters is True, allow prefix/suffix letters around digits (e.g., 'AA024322BB'), + but reject strings with interleaved letters (e.g., 'A2323B232C'). + """ + if number is None: + raise ValueError("as_int_id: input is None") + + s = str(number).strip() + + if not allow_letters: + try: + return str(int(float(s))) + except (ValueError, TypeError) as e: + raise ValueError(f"as_int_id: cannot convert {number!r}") from e + else: + # Allow only prefix/suffix letters, core must be digits + m = re.fullmatch(r"[A-Za-z]*([0-9]+)[A-Za-z]*", s) + if not m: + raise ValueError(f"as_int_id: invalid alphanumeric format {number!r}") + return m.group(1) + + +def _sanitize_identifier(name: str, used: set) -> str: + """Convert arbitrary string to a unique, valid Python identifier.""" + # Replace non-identifier chars with underscores and strip leading/trailing underscores + ident = re.sub(r'\W+', '_', str(name)).strip('_') + + # Empty or starts with digit -> prefix with underscore + if not ident or ident[0].isdigit(): + ident = f"_{ident}" if ident else "_arg" + + # Avoid keywords and builtins + if keyword.iskeyword(ident) or ident in dir(builtins): + ident += "_" + + # Ensure uniqueness + base = ident + i = 1 + while ident in used: + ident = f"{base}_{i}" + i += 1 + + used.add(ident) + return ident + + +def make_callable(expr: str, returns_str: bool = False) -> Union[Callable, Tuple[Callable, str]]: + """ + Compile an expression string with placeholders into a callable. + + Placeholders of the form 【name】 are replaced by valid Python parameter names, + preserving order and uniqueness. A mapping from safe names to original placeholders + is attached to the returned function as `_arg_map` and `_arg_order`. + + Args: + expr: Expression string containing placeholders. + returns_str: If True, return (callable, function_definition_str); otherwise return callable. + + Returns: + Callable or (Callable, str): The compiled function, and optionally the source code. + """ + # find placeholders + orig_vars = re.findall(r'【(.*?)】', expr) + orig_vars = list(dict.fromkeys(orig_vars)) # dedupe preserving order + + # build safe arg names + mapping + used = set() + safe_args = [] + arg_map = {} + for v in orig_vars: + s = _sanitize_identifier(v, used) + safe_args.append(s) + arg_map[s] = v + + # replace placeholders in expression with safe arg names + body_expr = expr + for s in safe_args: + orig = arg_map[s] + body_expr = body_expr.replace(f"【{orig}】", s) + + # define function text + args_str = ", ".join(safe_args) + func_str = f"def func({args_str}):\n" + func_str += indent(f"return {body_expr}", " ") + + # exec in isolated globals that still see current globals + exec_globals = globals().copy() + local_vars = {} + exec(func_str, exec_globals, local_vars) + fn = local_vars["func"] + + # attach mapping for callers + setattr(fn, "_arg_map", arg_map) # safe -> original + setattr(fn, "_arg_order", tuple(safe_args)) # ordered safe args + + if returns_str: + return fn, func_str + return fn + + +def _eval_expr(expr: str, row: Dict[str, Any]) -> Any: + """ + Evaluate a schema expression against a CSV row. + - If any referenced CSV value is empty/None -> return None (no noise). + - Otherwise: print detailed error and return None. + """ + if expr is None: + return None + try: + fn, func_src = make_callable(expr, returns_str=True) + kwargs = {} + had_empty_input = False + for safe in getattr(fn, "_arg_order", []): + orig = fn._arg_map[safe] + raw = row.get(orig, None) + # normalize to str or None; treat "" as empty + if raw is None: + had_empty_input = True + kwargs[safe] = None + else: + s = str(raw) + if s == "": + had_empty_input = True + kwargs[safe] = None + else: + kwargs[safe] = s + return fn(**kwargs) + except Exception as e: + if 'had_empty_input' in locals() and had_empty_input: + return None + print("=== EVAL ERROR ===") + print("expr:", expr) + try: + print("compiled:\n", func_src) + except Exception: + pass + try: + print("kwargs:", kwargs) + except Exception: + pass + print("error:", repr(e)) + print("==================") + return None + + +def _labels_to_list(s: Optional[str]) -> list: + if not isinstance(s, str): + return [] + parts = [p.strip() for p in s.split(",")] + return [p for p in parts if p] + + +def fuse_csv( + source_file: Union[str, Path], + schema: Dict[str, Any], + source_name: str, + current_dict: Dict[str, Any], +) -> Dict[str, Any]: + """ + Fuse CSV rows into hierarchical dict. + + New convention: + - Thematic sections map var_name -> expression directly. + - Units parsed from "(...)" in var_name. + - SHARED merged into every variable. + - ENTRY_KEY handling: + * If ENTRY_KEY starts with '^', treat as regex to match existing keys (never create new). + * Otherwise, treat as literal key (create new if not present). + """ + df = pd.read_csv(source_file, dtype=str, keep_default_na=False, na_values=[]) + out_dict = dict(current_dict) if current_dict else {} + + CONTROL_KEYS = {"ENTRY_KEY", "LAT_LONG", "COUNTRY_CODE", "SAMPLE_DATE", "SAMPLE_DEPTH_RANGE_CM", "SHARED"} + thematic_keys = [k for k in schema.keys() if k not in CONTROL_KEYS] + + filename_for_bar = Path(source_file).name if isinstance(source_file, (str, Path)) else str(source_file) + for _, row in tqdm(df.iterrows(), total=len(df), desc=filename_for_bar): + row_dict = row.to_dict() + entry_key_expr = schema.get("ENTRY_KEY") + entry_key_val = _eval_expr(entry_key_expr, row_dict) + if entry_key_val in (None, ""): + continue + + lat_long = _eval_expr(schema.get("LAT_LONG"), row_dict) + country_code = _eval_expr(schema.get("COUNTRY_CODE"), row_dict) + sample_date = _eval_expr(schema.get("SAMPLE_DATE"), row_dict) + sample_depth_range_cm = _eval_expr(schema.get("SAMPLE_DEPTH_RANGE_CM"), row_dict) + + # Shared metadata + shared_evaluated = {sk: _eval_expr(sv, row_dict) + for sk, sv in (schema.get("SHARED", {}) or {}).items()} + shared_evaluated["source_dataset"] = source_name + if "distance_to_grid (m)" not in shared_evaluated: + shared_evaluated["distance_to_grid (m)"] = 0.0 + + # Step 1. Resolve target entries + if str(entry_key_val).startswith("^"): # regex matching + pattern = re.compile(str(entry_key_val)) + targets = [k for k in out_dict.keys() if pattern.search(k)] + is_new_sample = False + # never create new + else: # exact match + if lat_long is None: + continue + if lat_long[0] is None or lat_long[1] is None: + continue + if entry_key_val not in out_dict: + out_dict[entry_key_val] = {} + targets = [entry_key_val] + is_new_sample = True + + # Step 2. Apply update + for key in targets: + sample = out_dict[key] + if is_new_sample: + assert lat_long is not None + sample["LAT_LONG"] = lat_long + assert country_code is not None + sample["COUNTRY_CODE"] = country_code + assert sample_date is not None + sample["SAMPLE_DATE"] = sample_date + assert sample_depth_range_cm is not None + sample["SAMPLE_DEPTH_RANGE_CM"] = sample_depth_range_cm + assert source_name is not None + sample["SAMPLE_SOURCE_DATASET"] = source_name + + for sec_name in thematic_keys: + sec_schema = schema.get(sec_name, {}) or {} + section_data = {} + + for var_name, expr in sec_schema.items(): + value = _eval_expr(expr, row_dict) + + m = re.match(r"^([^\s()]+)\s*\((.+)\)\s*$", var_name) + if m: + clean_name = m.group(1).strip() + unit = m.group(2).strip() + else: + clean_name, unit = var_name.strip(), None + + leaf = {"value": value} + if unit: + leaf["unit"] = unit + leaf.update(shared_evaluated) + + section_data[clean_name] = leaf + + existing_section = sample.get(sec_name, {}) + merged_section = dict(existing_section) + for var_name, leaf in section_data.items(): + if var_name not in merged_section: + merged_section[var_name] = leaf # only add if missing + sample[sec_name] = merged_section + + return out_dict + + +def fuse_tif( + source_file: Union[str, Path], + schema: Dict[str, Any], + source_name: str, + current_dict: Dict[str, Any], +) -> Dict[str, Any]: + out_dict = dict(current_dict) if current_dict else {} + + CONTROL_KEYS = {"CRS_STRING"} + thematic_keys = [k for k in schema.keys() if k not in CONTROL_KEYS] + + keys, lats, lons = [], [], [] + for k, s in out_dict.items(): + if "LAT_LONG" in s and s["LAT_LONG"] not in (None, "", "null"): + lat, lon = s["LAT_LONG"] + keys.append(k) + lats.append(lat) + lons.append(lon) + + if not keys: + return out_dict + + keys = np.array(keys) + lats = np.array(lats, dtype=float) + lons = np.array(lons, dtype=float) + + tif_dir = Path(source_file) + geod = Geod(ellps="WGS84") + + def _sample_one_entry(tif_entry): + value_map = None + scale_factor = 1.0 + + if isinstance(tif_entry, list): + if len(tif_entry) == 2: + if isinstance(tif_entry[1], dict): + tif_key, value_map = tif_entry + elif isinstance(tif_entry[1], (int, float)): + tif_key, scale_factor = tif_entry + else: + raise ValueError( + f"Error parsing entry {tif_entry}: second item must be a dict (codebook) or a number (scale factor)" + ) + elif len(tif_entry) == 1 and isinstance(tif_entry[0], str): + # simple single-item list, same as plain string + tif_key = tif_entry[0] + else: + # multi-entry list β†’ likely a profile list, handle outside + raise ValueError( + f"Error parsing entry {tif_entry}: unexpected list form (did you mean a profile list?)" + ) + else: + tif_key = tif_entry + + tif_path = tif_dir / f"{tif_key}.standardized.tif" + with rasterio.open(tif_path) as ds: + arr = ds.read(1) + nodata = ds.nodata + inv_affine: Affine = ~ds.transform + if "CRS_STRING" in schema: + crs = rasterio.crs.CRS.from_string(schema["CRS_STRING"]) + else: + crs = ds.crs + if crs is None or "LOCAL_CS" in crs.to_wkt(): + raise ValueError(f"{tif_key}.standardized.tif: CRS not available. Please provide CRS_STRING") + transformer = Transformer.from_crs("EPSG:4326", crs, always_xy=True) + + xs, ys = transformer.transform(lons, lats) + cols, rows = inv_affine * (xs, ys) + rows = np.round(rows).astype(int) + cols = np.round(cols).astype(int) + + in_bounds = (rows >= 0) & (rows < ds.height) & (cols >= 0) & (cols < ds.width) + + values = np.empty(len(keys), dtype=object) + values[:] = np.nan + dists = np.full(len(keys), np.nan, dtype=float) + + if np.any(in_bounds): + r = rows[in_bounds] + c = cols[in_bounds] + + raw_vals = arr[r, c] + + x_centers, y_centers = ds.transform * (c, r) + lats_geo = lats[in_bounds] + lons_geo = lons[in_bounds] + + if crs.is_geographic: + _, _, d_valid = geod.inv(lons_geo, lats_geo, x_centers, y_centers) + else: + xs_valid = xs[in_bounds] + ys_valid = ys[in_bounds] + d_valid = np.sqrt((xs_valid - x_centers) ** 2 + (ys_valid - y_centers) ** 2) + + if nodata is not None: + nd_mask = np.isclose(raw_vals, nodata, equal_nan=True) + else: + nd_mask = np.zeros_like(raw_vals, dtype=bool) + + out_vals = [] + for rv, is_nd in zip(raw_vals, nd_mask): + if is_nd: + out_vals.append(np.nan) + else: + if value_map is not None: + try: + code_str = str(int(round(float(rv)))) + except Exception: + code_str = str(int(round(rv))) + out_vals.append(code_str + ": " + str(value_map.get(code_str, "Unknown"))) + else: + out_vals.append(float(rv) * scale_factor) + + values[in_bounds] = out_vals + d_valid = np.array(d_valid, dtype=float) + d_valid[nd_mask] = np.nan + dists[in_bounds] = d_valid + + return values, dists, in_bounds + + all_vars = [] + for sec_name in thematic_keys: + for var_name, tif_entry in schema[sec_name].items(): + all_vars.append((sec_name, var_name, tif_entry)) + + for sec_name, var_name, tif_entry in tqdm(all_vars, desc=f"Processing variables in {tif_dir.name}"): + m = re.match(r"^(.*)\s+\(([^()]*)\)$", var_name) + if m: + clean_name, unit = m.group(1).strip(), m.group(2).strip() + else: + clean_name, unit = var_name.strip(), None + + is_profile = ( + isinstance(tif_entry, list) + and not ( + len(tif_entry) == 2 + and isinstance(tif_entry[1], (dict, int, float)) + ) + ) + + if is_profile: + per_entry_values = [] + per_entry_dists = [] + union_in_bounds = np.zeros(len(keys), dtype=bool) + + for entry in tif_entry: + vals, dists, in_bounds = _sample_one_entry(entry) + per_entry_values.append(vals) + per_entry_dists.append(dists) + union_in_bounds |= in_bounds + + for idx, key in enumerate(keys): + if not union_in_bounds[idx]: + continue + + sample = out_dict[key] + section = sample.setdefault(sec_name, {}) + if clean_name in section: + continue + + value_list = [vals[idx] for vals in per_entry_values] + + dist_candidates = [] + for d in per_entry_dists: + di = d[idx] + if not (isinstance(di, float) and np.isnan(di)): + dist_candidates.append(float(di)) + dist_reduced = (max(dist_candidates) if dist_candidates else None) + + leaf = { + "value": value_list, + **({"unit": unit} if unit else {}), + "distance_to_grid (m)": dist_reduced, + "source_dataset": source_name + } + leaf = _nan_to_none(leaf) + section[clean_name] = leaf + + else: + vals, dists, in_bounds = _sample_one_entry(tif_entry) + + ib_idx = np.where(in_bounds)[0] + for ii in ib_idx: + key = keys[ii] + sample = out_dict[key] + section = sample.setdefault(sec_name, {}) + if clean_name in section: + continue + + raw_val = vals[ii] + val = None if (isinstance(raw_val, float) and np.isnan(raw_val)) else raw_val + dist_val = float(dists[ii]) if not np.isnan(dists[ii]) else np.nan + + leaf = { + "value": val, + **({"unit": unit} if unit else {}), + "distance_to_grid (m)": dist_val, + "source_dataset": source_name + } + leaf = _nan_to_none(leaf) + section[clean_name] = leaf + + return out_dict diff --git a/src/fuse_esdac/x_00_gen_datasets_csv.py b/src/fuse_esdac/x_00_gen_datasets_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..7026412f4cba2abea6c7e8536f408fe2bc4a2e04 --- /dev/null +++ b/src/fuse_esdac/x_00_gen_datasets_csv.py @@ -0,0 +1,73 @@ +import json +import pandas as pd +from pathlib import Path + +# Paths +base_dir = Path("src/esdac") +output_dir = Path("src/fuse_esdac/outputs") +output_dir.mkdir(parents=True, exist_ok=True) + +# Read JSON file +with open(base_dir / "status.json", "r", encoding="utf-8") as f: + data = json.load(f) + +# Keep only items with status == "PROCESSED" +processed_items = [item for item in data if item.get("status") == "PROCESSED"] + +# Convert to DataFrame +df = pd.DataFrame(processed_items) + +# Rename url β†’ dataset_url +df.rename(columns={"url": "dataset_url"}, inplace=True) + +# Drop unwanted columns +cols_to_drop = [ + "request_needed", "status", "notes", + "screened_by", "requested_downloaded_by", "processed_by" +] +df.drop(columns=cols_to_drop, inplace=True, errors="ignore") + +# Determine fuse status and reason +fuse_statuses = [] +reasons = [] + +for _, row in df.iterrows(): + dataset_dir = base_dir / row["name"] + fuse_schema = dataset_dir / "fuse_schema.json" + fuse_skip = dataset_dir / "fuse_skip.txt" + + if fuse_schema.exists(): + fuse_statuses.append("FUSED") + reasons.append("") + elif fuse_skip.exists(): + fuse_statuses.append("SKIPPED") + try: + text = fuse_skip.read_text(encoding="utf-8").strip() + except Exception as e: + text = f"[Error reading fuse_skip.txt: {e}]" + reasons.append(text) + else: + fuse_statuses.append("AWAIT") + reasons.append("") + +df["fuse_status"] = fuse_statuses +df["reason"] = reasons + +# Save to CSV +output_path = output_dir / "processed_datasets.csv" +df.to_csv(output_path, index=False, encoding="utf-8") + +print(f"βœ… Saved {len(df)} processed datasets to {output_path}") + +# ---- existing code above stays unchanged ---- + +# Save to CSV (all processed) +output_path = output_dir / "processed_datasets.csv" +df.to_csv(output_path, index=False, encoding="utf-8") +print(f"βœ… Saved {len(df)} processed datasets to {output_path}") + +# ---- new feature: save only fused datasets ---- +df_fused = df[df["fuse_status"] == "FUSED"].drop(columns=["fuse_status", "reason"]) +fused_output_path = output_dir / "fused_datasets.csv" +df_fused.to_csv(fused_output_path, index=False, encoding="utf-8") +print(f"βœ… Saved {len(df_fused)} fused datasets to {fused_output_path}") diff --git a/src/fuse_esdac/x_01_fuse.py b/src/fuse_esdac/x_01_fuse.py new file mode 100644 index 0000000000000000000000000000000000000000..e02b155ccb7c47dadd5708305f50b7339cf521bb --- /dev/null +++ b/src/fuse_esdac/x_01_fuse.py @@ -0,0 +1,157 @@ +import argparse +import json +from pathlib import Path + +import pandas as pd + +from utils import fuse_csv, fuse_tif + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint", type=str, required=True, + help="Checkpoint config") + parser.add_argument("--last-only", action="store_true", + help="Only do the last dataset (for debugging)") + parser.add_argument("--recover_unfinished", action="store_true", + help="Recover from samples_unfinished.json and skip finished datasets") + args = parser.parse_args() + + # ------------------------- + # Load checkpoint config + # ------------------------- + with open(f"./src/fuse_esdac/configs/{args.checkpoint}.json", "r") as f: + cfg = json.load(f) + + input_json = Path(f"./src/fuse_esdac/outputs/{cfg.get('input')}/samples.json") + datasets = cfg.get("datasets", []) + if args.last_only and datasets: + datasets = datasets[-1:] + + # ------------------------- + # Prepare output dir (needed for unfinished save/recovery) + # ------------------------- + out_dir = Path(f"./src/fuse_esdac/outputs/{args.checkpoint}") + out_dir.mkdir(parents=True, exist_ok=True) + + out_json = out_dir / f"samples.json" + out_var_csv = out_dir / f"stat_variable.csv" + unfinished_json = out_dir / "samples_unfinished.json" + finished_datasets_json = out_dir / "datasets_finished.json" + + # ------------------------- + # Load previous fused dict (normal) OR recover unfinished + # ------------------------- + if args.recover_unfinished: + if not unfinished_json.exists(): + raise FileNotFoundError(f"--recover_unfinished requires {unfinished_json} to exist") + with open(unfinished_json, "r") as f: + big_output_dict = json.load(f) + + if finished_datasets_json.exists(): + with open(finished_datasets_json, "r") as f: + finished_datasets = set(json.load(f)) + else: + finished_datasets = set() + + # Skip finished ones (preserve order) + datasets = [d for d in datasets if d not in finished_datasets] + else: + finished_datasets = set() + if input_json and Path(input_json).exists(): + with open(input_json, "r") as f: + big_output_dict = json.load(f) + else: + big_output_dict = {} + + # ------------------------- + # Fuse datasets + # ------------------------- + for dataset in datasets: + try: + data_dir = Path(f"./datasets/esdac/{dataset}/processed") + schema_path = Path(f"./src/esdac/{dataset}/fuse_schema.json") + with open(schema_path, "r") as f: + schema = json.load(f) + + for filename in schema.keys(): + if filename.endswith(".csv"): + csv_path = data_dir / filename + big_output_dict = fuse_csv( + source_file=csv_path, + schema=schema[filename], + source_name=dataset, + current_dict=big_output_dict, + ) + elif filename.endswith("@tif"): + tif_path = data_dir / filename[:-4] + big_output_dict = fuse_tif( + source_file=tif_path, + schema=schema[filename], + source_name=dataset, + current_dict=big_output_dict, + ) + + # Mark dataset finished (for recovery skipping) + finished_datasets.add(dataset) + with open(finished_datasets_json, "w") as f: + json.dump(sorted(finished_datasets), f, indent=2, ensure_ascii=False) + + except Exception: + # Save partial progress and re-raise + with open(unfinished_json, "w") as f: + json.dump(big_output_dict, f, indent=2, ensure_ascii=False) + raise + + # ------------------------- + # Save outputs + # ------------------------- + with open(out_json, "w") as f: + json.dump(big_output_dict, f, indent=2, ensure_ascii=False) + + # ------------------------- + # Variable-level table + # ------------------------- + table = {} + for sample_key, sample in big_output_dict.items(): + prefix = sample_key.split("_")[0] + if prefix not in table: + table[prefix] = {} + for domain, section in sample.items(): + if isinstance(section, dict): + for var, leaf in section.items(): + if isinstance(leaf, dict): + if leaf.get("value") not in (None, "", "null"): + key = f"{domain}:{var}" + table[prefix][key] = table[prefix].get(key, 0) + 1 + + # Count raw samples per prefix (independent of filled variables) + sample_count = {} + for sample_key in big_output_dict.keys(): + prefix = sample_key.split("_")[0] + sample_count[prefix] = sample_count.get(prefix, 0) + 1 + + df_var = ( + pd.DataFrame.from_dict(table, orient="index") + .fillna(0) + .infer_objects() + .astype(int) + ) + + # Ensure all prefixes are present even if they had no filled variables + all_prefixes = sorted(sample_count.keys()) + if not df_var.empty: + df_var = df_var.reindex(all_prefixes, fill_value=0) + else: + df_var = pd.DataFrame(index=all_prefixes) + + # Add true sample counts per row + df_var["TOTAL_SAMPLES"] = df_var.index.map(lambda p: sample_count.get(p, 0)).astype(int) + + # Add TOTAL row with correct sums and total sample count + totals_no_samples_col = df_var.drop(columns=["TOTAL_SAMPLES"]).sum(axis=0).astype(int) + df_var.loc["SUM"] = 0 # initialize the row + df_var.loc["SUM", totals_no_samples_col.index] = totals_no_samples_col + df_var.loc["SUM", "TOTAL_SAMPLES"] = int(sum(sample_count.values())) + df_var = df_var.astype(int) + + df_var.to_csv(out_var_csv) diff --git a/src/fuse_esdac/x_02_gadm_eu_gen.py b/src/fuse_esdac/x_02_gadm_eu_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..def4686985baac7e0dec131ac818025e7a33e7ec --- /dev/null +++ b/src/fuse_esdac/x_02_gadm_eu_gen.py @@ -0,0 +1,372 @@ +import csv +import json +import os +import pickle +from collections import defaultdict +from typing import Dict, Any + +import matplotlib.pyplot as plt +from shapely.ops import unary_union +from shapely.wkb import dumps as wkb_dumps +from shapely.wkb import loads as wkb_loads + +UNSD_EU_CSV = "src/fuse_esdac/codebooks/unsd_eu.csv" +REGION_ALIAS_JSON = "src/fuse_esdac/codebooks/unsd_eu_alias.json" +WORLD_TREE_PKL = "src/fuse_esdac/large_inputs/gadm_tree.pkl" +OUT_TREE_PKL = "src/fuse_esdac/outputs/03_photo_gadm/gadm_tree_europe.pkl" + + +def load_unsd_europe(csv_path: str): + """ + Load the UNSD-based Europe CSV we built earlier. + + Expected columns (0-based index): + 0: "001" + 1: "World" + 2: "150" + 3: "Europe" + 4: primary region code (039, 151, 154, 155, 153) + 5: primary region name ("Western Europe", etc.) + 6: secondary region name (e.g. "DACH Region") + 7: secondary region ID (e.g. "DACH") + 8: country name + 9: numeric country code + 10: alpha-2 code (e.g. "GB") + 11: alpha-3 code (e.g. "GBR") + 12+: may be empty + """ + macro_id_map = { + "Northern Europe": "N_EU", + "Western Europe": "W_EU", + "Eastern Europe": "E_EU", + "Southern Europe": "S_EU", + "Central Europe": "C_EU", + } + + countries = {} # alpha3 -> dict + macro_to_sub = defaultdict(set) # (macro_name, macro_id) -> {(sub_name, sub_id), ...} + sub_to_countries = defaultdict(set) # (macro_name, macro_id, sub_name, sub_id) -> {alpha3, ...} + + with open(csv_path, "r", encoding="utf-8") as f: + reader = csv.reader(f, delimiter=";") + for row in reader: + if not row: + continue + # skip obviously invalid rows + if len(row) < 12: + continue + # very simple heuristic: first field should be "001" for our data; if not, skip + if row[0] != "001": + continue + + macro_name = row[5].strip() + sub_name = row[6].strip() + sub_id = row[7].strip() + country_name = row[8].strip() + alpha3 = row[11].strip() + + if not alpha3: + continue + + if macro_name not in macro_id_map: + raise ValueError(f"Unknown macro region name in CSV: {macro_name}") + + macro_id = macro_id_map[macro_name] + + countries[alpha3] = { + "name": country_name, + "macro_name": macro_name, + "macro_id": macro_id, + "sub_name": sub_name, + "sub_id": sub_id, + } + + macro_to_sub[(macro_name, macro_id)].add((sub_name, sub_id)) + sub_to_countries[(macro_name, macro_id, sub_name, sub_id)].add(alpha3) + + return countries, macro_to_sub, sub_to_countries + + +def build_europe_subtree(tree_world, countries, macro_to_sub, sub_to_countries): + """ + Build a new tree that only contains Europe, with new levels: + + level 0: Europe (EU) + level 1: macro regions (N_EU, W_EU, ...) + level 2: subregions (BENE, DACH, BRIT, ...) + level 3+: original GADM hierarchy (countries and below) with level offset +3 + + We keep original geometry for GADM nodes and aggregate new levels from those. + """ + # 1) Copy only European countries and their entire subtrees + europe_country_gids = set(countries.keys()) # these are ISO alpha-3, matching GADM GID_0 + tree_eu: Dict[str, Dict[str, Any]] = {} + + def copy_subtree(root_gid: str): + stack = [root_gid] + while stack: + gid = stack.pop() + if gid in tree_eu: + continue + if gid not in tree_world: + raise KeyError(f"GID {gid} from UNSD CSV not found in GADM tree") + node = tree_world[gid] + tree_eu[gid] = { + "name": node["name"], + "level": node["level"], # temporary, will be shifted later + "parent": node["parent"], + "children": set(node["children"]), + "geometry": node["geometry"], # WKB bytes + } + stack.extend(node["children"]) + + for alpha3 in sorted(europe_country_gids): + copy_subtree(alpha3) + + # 2) Create Europe root + tree_eu["EU"] = { + "name": "Europe", + "level": 0, + "parent": None, + "children": set(), + "geometry": None, + } + + # 3) Create macro-region nodes (level 1) + macro_nodes = {} # macro_id -> gid (=macro_id) + for (macro_name, macro_id) in sorted({(m, mid) for (m, mid) in macro_to_sub.keys()}): + gid = macro_id # use macro_id as GID + macro_nodes[macro_id] = gid + tree_eu[gid] = { + "name": macro_name, + "level": 1, + "parent": "EU", + "children": set(), + "geometry": None, + } + tree_eu["EU"]["children"].add(gid) + + # 4) Create subregion nodes (level 2) + sub_nodes = {} # (macro_id, sub_id) -> gid (=sub_id) + for (macro_name, macro_id), subregions in macro_to_sub.items(): + macro_gid = macro_nodes[macro_id] + for (sub_name, sub_id) in sorted(subregions): + gid = sub_id # use sub_id as GID + sub_nodes[(macro_id, sub_id)] = gid + tree_eu[gid] = { + "name": sub_name, + "level": 2, + "parent": macro_gid, + "children": set(), + "geometry": None, + } + tree_eu[macro_gid]["children"].add(gid) + + # 5) Attach country nodes under subregions; they will be level 3 after shift + # First, identify which nodes are countries in the copied GADM tree: level == 0 + country_nodes_in_eu = {gid for gid, node in tree_eu.items() if node["level"] == 0 and gid != "EU"} + # By construction, these should be exactly europe_country_gids + missing = europe_country_gids - country_nodes_in_eu + if missing: + raise RuntimeError(f"Countries present in CSV but not in GADM subtree: {missing}") + + for alpha3 in sorted(europe_country_gids): + node = tree_eu[alpha3] + info = countries[alpha3] + macro_id = info["macro_id"] + sub_id = info["sub_id"] + sub_gid = sub_nodes[(macro_id, sub_id)] + + # Re-parent this country from None -> subregion + node["parent"] = sub_gid + tree_eu[sub_gid]["children"].add(alpha3) + + # 6) Shift levels of all original GADM nodes by +3 + reserved_gids = {"EU"} | set(macro_nodes.values()) | set(sub_nodes.values()) + for gid, node in tree_eu.items(): + if gid in reserved_gids: + continue + # This includes all GADM-derived nodes: countries and below + node["level"] = node["level"] + 3 + + # 7) Compute geometry for subregions, macro-regions, and Europe + # All existing GADM nodes already have WKB geometry + + # Subregions: union of country geometries + for (macro_id, sub_id), sub_gid in sub_nodes.items(): + country_alpha3s = sub_to_countries[ + (countries[next(iter(sub_to_countries[(macro_id, sub_id)].__iter__()))]["macro_name"], # ugly, ignore + macro_id, + tree_eu[sub_gid]["name"], + sub_id)] if False else None # placeholder to avoid confusion + # simpler: recompute country set for this (macro_id, sub_id) + # to avoid the above monstrosity: + country_alpha3s = { + a3 + for (m_name, m_id, s_name, s_id), a3s in sub_to_countries.items() + if m_id == macro_id and s_id == sub_id + for a3 in a3s + } + + geoms = [] + for alpha3 in country_alpha3s: + geom_wkb = tree_eu[alpha3]["geometry"] + geoms.append(wkb_loads(geom_wkb)) + if not geoms: + raise RuntimeError(f"No country geometries for subregion {sub_gid}") + merged = unary_union(geoms) + tree_eu[sub_gid]["geometry"] = wkb_dumps(merged, hex=False) + + # Macro-regions: union of subregion geometries + for macro_id, macro_gid in macro_nodes.items(): + geoms = [] + for sub_gid in tree_eu[macro_gid]["children"]: + geom_wkb = tree_eu[sub_gid]["geometry"] + if geom_wkb is None: + raise RuntimeError(f"Missing geometry for subregion {sub_gid}") + geoms.append(wkb_loads(geom_wkb)) + merged = unary_union(geoms) + tree_eu[macro_gid]["geometry"] = wkb_dumps(merged, hex=False) + + # Europe: union of macro-region geometries + geoms = [] + for macro_gid in tree_eu["EU"]["children"]: + geom_wkb = tree_eu[macro_gid]["geometry"] + if geom_wkb is None: + raise RuntimeError(f"Missing geometry for macro region {macro_gid}") + geoms.append(wkb_loads(geom_wkb)) + merged = unary_union(geoms) + tree_eu["EU"]["geometry"] = wkb_dumps(merged, hex=False) + + # 8) Final sanity: all nodes must have geometry + for gid, node in tree_eu.items(): + if node["geometry"] is None: + raise RuntimeError(f"No geometry for node {gid} ({node['name']})") + + # 9) Attach aliases to nodes (EU / macro / sub) + if not os.path.exists(REGION_ALIAS_JSON): + raise FileNotFoundError(f"Region alias file not found: {REGION_ALIAS_JSON}") + + with open(REGION_ALIAS_JSON, "r", encoding="utf-8") as f: + alias_map = json.load(f) + + # First: enforce that all level <= 2 nodes have aliases defined + for gid, node in tree_eu.items(): + if node["level"] <= 2: + if gid not in alias_map: + raise KeyError( + f"Missing aliases for region GID '{gid}' " + f"(level={node['level']}, name='{node['name']}')" + ) + + # Second: attach aliases to nodes + for gid, aliases in alias_map.items(): + if gid not in tree_eu: + raise KeyError(f"Alias GID '{gid}' not found in Europe tree") + + # normalize: store as sorted unique list + tree_eu[gid]["aliases"] = sorted(set(aliases)) + + # Third: ensure lower-level nodes have an aliases field (empty list) + for gid, node in tree_eu.items(): + if "aliases" not in node: + node["aliases"] = [] + + return tree_eu + + +def main(): + # Load world tree + with open(WORLD_TREE_PKL, "rb") as f: + tree_world = pickle.load(f) + + # Load UNSD Europe table + countries, macro_to_sub, sub_to_countries = load_unsd_europe(UNSD_EU_CSV) + + # Build Europe tree + tree_eu = build_europe_subtree(tree_world, countries, macro_to_sub, sub_to_countries) + + # Save to disk + os.makedirs(os.path.dirname(OUT_TREE_PKL), exist_ok=True) + with open(OUT_TREE_PKL, "wb") as f: + pickle.dump(tree_eu, f) + + print(f"βœ… Europe tree built and saved to: {OUT_TREE_PKL}") + print(f"Nodes: {len(tree_eu)}") + # Small debug output + print("Root:", "EU", tree_eu["EU"]["name"]) + print("Level-1 children:", tree_eu["EU"]["children"]) + + +plot_times = 0 + + +def check(): + IN_FILE = "src/fuse_esdac/outputs/03_photo_gadm/gadm_tree_europe.pkl" + + with open(IN_FILE, "rb") as f: + tree = pickle.load(f) + + # 1) print first 4 levels as tree: EU -> macro -> subregion -> country + def tree_print(gid, max_level=3, prefix=""): + node = tree[gid] + name = node["name"] or "?" + print(f"{prefix}{gid} ({name})") + if node["level"] >= max_level: + return + # sort children by gid + for child in sorted(node["children"]): + cnode = tree[child] + if cnode["level"] <= max_level: + tree_print(child, max_level, prefix + "β”œβ”€") + + print("=== Europe Hierarchy (levels 0-3) ===") + tree_print("EU", max_level=3) + + # 2) plot level<=2 boundaries + # Levelβ†’Color mapping (explicit because user asked) + LEVEL_COLORS = { + 0: "black", + 1: "red", + 2: "green", + 3: "blue", + 4: "magenta", + 5: "cyan", + 6: "orange" + } + + def get_color(time: int): + return LEVEL_COLORS[time % len(LEVEL_COLORS)] + + def plot_boundary_by_level(geom): + global plot_times + color = get_color(plot_times) + plot_times += 1 + + # Only outer rings + if geom.geom_type == "Polygon": + x, y = geom.exterior.xy + plt.plot(x, y, color=color, lw=0.5) + elif geom.geom_type == "MultiPolygon": + for poly in geom.geoms: + x, y = poly.exterior.xy + plt.plot(x, y, color=color, lw=0.5) + + plt.figure() + + # Draw all nodes at level <= 2 + for gid, node in tree.items(): + level = node["level"] + if level == 1: # Europe, macro, subregions + print(f"{gid} ({node['name']})") + geom = wkb_loads(node["geometry"]) + plot_boundary_by_level(geom) + + plt.title("Europe boundaries (level-based coloring)") + plt.gca().set_aspect("equal", adjustable="datalim") + plt.show() + + +if __name__ == "__main__": + main() + check() diff --git a/src/fuse_esdac/x_03_merge_photos_gadm.py b/src/fuse_esdac/x_03_merge_photos_gadm.py new file mode 100644 index 0000000000000000000000000000000000000000..9aea5c2ef82c774f0168c20cb89fb1e9cfa9cc02 --- /dev/null +++ b/src/fuse_esdac/x_03_merge_photos_gadm.py @@ -0,0 +1,156 @@ +import os +import json +import pickle +from collections import OrderedDict + +import pyproj +from shapely.geometry import Point +from shapely.ops import transform +from shapely.wkb import loads +from shapely.strtree import STRtree +from tqdm import tqdm + + +# ============================================================ +# LOAD DATA +# ============================================================ +print("πŸ“‚ Loading input files...") + +with open("./src/fuse_esdac/outputs/02_extend/samples.json", "r") as f: + samples = json.load(f) + +with open("./src/fuse_esdac/outputs/03_photo_gadm/lucas_photos_by_point.json", "r") as f: + photos = json.load(f) + +with open("./src/fuse_esdac/outputs/03_photo_gadm/gadm_tree_europe.pkl", "rb") as f: + gadm_tree = pickle.load(f) + +print(f"βœ… Loaded {len(samples):,} samples, {len(photos):,} photos, {len(gadm_tree):,} GADM nodes") + + +# ============================================================ +# PREPARE GEOMETRIES & SPATIAL INDEX +# ============================================================ +print("🧩 Decoding geometries...") +for node in tqdm(gadm_tree.values(), desc="Decoding"): + if isinstance(node["geometry"], (bytes, bytearray, memoryview)): + node["geometry"] = loads(node["geometry"]) + +# Collect leaf polygons +leaf_gids, leaf_geoms = zip( + *[(gid, node["geometry"]) for gid, node in gadm_tree.items() if not node["children"]] +) +print(f"πŸƒ Leaf polygons: {len(leaf_gids):,}") + +# Build STRtree for fast lookup +print("πŸ“Œ Building spatial index...") +leaf_index = STRtree(leaf_geoms) +geom_to_gid = dict(zip(leaf_geoms, leaf_gids)) + +# Group nodes by level (for hierarchy) +levels = {} +for gid, node in gadm_tree.items(): + levels.setdefault(node["level"], {})[gid] = node +print(f"πŸ“Š GADM levels: {sorted(levels.keys())}") + + +# ============================================================ +# UTILITIES +# ============================================================ +def _build_path_from_gid(gid): + """Return (ids, names) from root β†’ leaf.""" + ids, names = [gid], [gadm_tree[gid]["name"]] + while gadm_tree[gid]["parent"] is not None: + gid = gadm_tree[gid]["parent"] + ids.append(gid) + names.append(gadm_tree[gid]["name"]) + return ids[::-1], names[::-1] + + +# ============================================================ +# FALLBACK LOOKUP (NEAREST LEAF) +# ============================================================ +_project_to_m = pyproj.Transformer.from_crs("EPSG:4326", "EPSG:3857", always_xy=True).transform + +def find_gadm_codes_nearest(lat, lon, key=None): + """Find nearest leaf polygon and climb upward; print distance warning.""" + p = Point(lon, lat) + best_gid, min_d = None, float("inf") + + for gid, geom in zip(leaf_gids, leaf_geoms): + d = geom.distance(p) + if d < min_d: + best_gid, min_d = gid, d + + if best_gid is None: + print(f"❌ Nearest-leaf fallback failed for {key} ({lat}, {lon})") + return [], [] + + # Accurate distance (meters) + p_m = transform(_project_to_m, p) + geom_m = transform(_project_to_m, gadm_tree[best_gid]["geometry"]) + dist_m = p_m.distance(geom_m) + + ids, names = _build_path_from_gid(best_gid) + print( + f"⚠️ Fallback used for {key} ({lat:.6f}, {lon:.6f}) β†’ " + f"{' / '.join(names)} (distance {dist_m:.1f} m)" + ) + return ids, names + + +# ============================================================ +# MAIN GADM LOOKUP +# ============================================================ +def find_gadm_codes(lat, lon, key=None): + """Try fast leaf search, then fallback if needed.""" + p = Point(lon, lat) + for idx in leaf_index.query(p): + geom = leaf_geoms[idx] + if geom.contains(p): + return _build_path_from_gid(leaf_gids[idx]) + return find_gadm_codes_nearest(lat, lon, key) + + +# ============================================================ +# PROCESS SAMPLES +# ============================================================ +print("🧠 Merging photos + GADM info into samples...") + +for key, sample in tqdm(samples.items(), desc="Processing"): + # Add photos + if key in photos: + sample.setdefault("ASSETS", {}) + sample["ASSETS"]["photos"] = { + "value": photos[key], + "source_dataset": "lucas", + "distance_to_grid (m)": 0.0, + } + + # Add GADM hierarchy + lat, lon = sample["LAT_LONG"] + gadm_ids, gadm_names = find_gadm_codes(float(lat), float(lon), key) + + # Ensure GADM appears after LAT_LONG + new_sample = OrderedDict() + for k, v in sample.items(): + new_sample[k] = v + if k == "LAT_LONG": + new_sample["GADM_IDS"] = gadm_ids + new_sample["GADM_NAMES"] = gadm_names + samples[key] = new_sample + + +# ============================================================ +# SAVE OUTPUT +# ============================================================ +out_dir = "./src/fuse_esdac/outputs/03_photo_gadm" +os.makedirs(out_dir, exist_ok=True) + +with open(f"{out_dir}/samples_with_photo_gadm.pkl", "wb") as f: + pickle.dump(samples, f, protocol=pickle.HIGHEST_PROTOCOL) + +with open(f"{out_dir}/samples_with_photo_gadm.json", "w", encoding="utf-8") as f: + json.dump(samples, f, ensure_ascii=False, indent=2) + +print(f"\nβœ… All done! Saved to {out_dir}/") diff --git a/src/fuse_esdac/x_04_table.py b/src/fuse_esdac/x_04_table.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb1126df9546f11a3a4766064f89e98772e7abc --- /dev/null +++ b/src/fuse_esdac/x_04_table.py @@ -0,0 +1,345 @@ +import json +import os +import pickle +from collections import defaultdict, OrderedDict + +import numpy as np +import pandas as pd +from tqdm import tqdm + +# ----------------------------- CONFIG -------------------------------- # +INPUT_JSON = "./src/fuse_esdac/outputs/03_photo_gadm/samples_with_photo_gadm.pkl" +OUTPUT_CSV = "./src/fuse_esdac/outputs/04_table/final_table.csv" +OUTPUT_DICT_PKL = "./src/fuse_esdac/outputs/04_table/final_dict.pkl" +OUTPUT_DICT_JSON = "./src/fuse_esdac/outputs/04_table/final_dict.json" +OUTPUT_META = "./src/fuse_esdac/outputs/04_table/column_meta.json" +OUTPUT_META_NAMES = "./src/fuse_esdac/outputs/04_table/column_names.json" +# --------------------------------------------------------------------- # + +# Null normalization rules +NULL_STRINGS = { + "nan", "null", "none", "", "na", "n/a", "missing", "unknown", "unknown1" +} + + +def is_null_value(v): + # Real null + if v is None: + return True + # String forms + if isinstance(v, str): + s = v.strip().lower() + if s in NULL_STRINGS: + return True + return False + + +FILL_VALUES_TO_NULL = {-9000, -90000} +NEGATIVE_FEATURE_KEYWORDS = ("density", "percentage", "content", "stock") +NONE_POSITIVE_FEATURE_KEYWORDS = ("ph_",) + + +def numeric_or_none(v): + try: + return float(v) + except (TypeError, ValueError): + return None + + +def clean_numeric_scalar_value(header, v): + if is_null_value(v): + return None + + x = numeric_or_none(v) + if x is None: + return v + + if x in FILL_VALUES_TO_NULL: + return None + + h = header.lower() + if any(k in h for k in NEGATIVE_FEATURE_KEYWORDS) and x < -0.00001: + return None + if any(k in h for k in NONE_POSITIVE_FEATURE_KEYWORDS) and x < 0.00001: + return None + + return v + + +def clean_numeric_vector_value(header, v): + if is_null_value(v): + return None + + if not isinstance(v, list): + return v + + cleaned = [ + clean_numeric_scalar_value(header, x) + for x in v + ] + + if all(x is None for x in cleaned): + return None + + return cleaned + + +def main(): + with open(INPUT_JSON, "rb") as f: + data = pickle.load(f) + + # 1) Discover variables and enforce unit consistency on (theme:var) + var_to_unit = {} # "theme:var" -> unit_str ('' if missing) + for sid, sample in data.items(): + # force presence of required non-variable fields (surface schema issues early) + _ = sample["LAT_LONG"] + _ = sample["GADM_IDS"] + _ = sample["GADM_NAMES"] + _ = sample["COUNTRY_CODE"] + + for theme, theme_obj in sample.items(): + if theme in ("LAT_LONG", "GADM_IDS", "GADM_NAMES", "COUNTRY_CODE", + "SAMPLE_DATE", "SAMPLE_DEPTH_RANGE_CM", "SAMPLE_SOURCE_DATASET"): + continue + + # theme_obj is expected to be a dict of variables + for var, var_obj in theme_obj.items(): + # required fields per your spec (value, source_dataset, distance_to_grid; unit optional) + _ = var_obj["value"] + _ = var_obj["source_dataset"] + _ = var_obj["distance_to_grid (m)"] + unit = var_obj.get("unit", "") # empty string if missing + + key = f"{theme}:{var}" + if key not in var_to_unit: + var_to_unit[key] = unit + else: + if var_to_unit[key] != unit: + print(f"WARNING: inconsistent unit for '{key}': " + f"using '{var_to_unit[key]}' but encountered '{unit}'") + + # 2) Build a stable mapping to final column headers with unit parentheses (including empty ()) + def header_for(key): + unit = var_to_unit[key] + return f"{key} ({unit})" if unit else key + + base_cols = ["id", "LAT_LONG", "GADM_IDS", "GADM_NAMES", "COUNTRY_CODE", + "SAMPLE_DATE", "SAMPLE_DEPTH_RANGE_CM", "SAMPLE_SOURCE_DATASET"] + var_cols = [header_for(key) for key in var_to_unit.keys()] + all_cols = base_cols + var_cols + + # 3) Populate rows and collect meta + meta_sources = defaultdict(set) # header -> set of datasets + meta_distances = defaultdict(list) # header -> list of numeric distances (non-null only) + + table = [] + for sid, sample in tqdm(data.items(), desc="Building table"): + row = OrderedDict.fromkeys(all_cols, None) + + row["id"] = sid + row["LAT_LONG"] = sample["LAT_LONG"] + row["GADM_IDS"] = sample["GADM_IDS"] + row["GADM_NAMES"] = sample["GADM_NAMES"] + row["COUNTRY_CODE"] = sample["COUNTRY_CODE"] + row["SAMPLE_DATE"] = sample.get("SAMPLE_DATE") + row["SAMPLE_DEPTH_RANGE_CM"] = sample.get("SAMPLE_DEPTH_RANGE_CM") + row["SAMPLE_SOURCE_DATASET"] = sample.get("SAMPLE_SOURCE_DATASET") + + for theme, theme_obj in sample.items(): + if theme in ("LAT_LONG", "GADM_IDS", "GADM_NAMES", "COUNTRY_CODE", + "SAMPLE_DATE", "SAMPLE_DEPTH_RANGE_CM", "SAMPLE_SOURCE_DATASET"): + continue + + for var, var_obj in theme_obj.items(): + key = f"{theme}:{var}" + header = header_for(key) + + v = var_obj["value"] + if is_null_value(v): + v = None + row[header] = v # write to final header (with unit parentheses) + + # --- meta: collect sources regardless of v --- + src = var_obj["source_dataset"] + if isinstance(src, str) and src.strip(): + meta_sources[header].add(src) + + # --- meta: collect non-null distances only --- + dist = var_obj["distance_to_grid (m)"] + if not is_null_value(dist): + try: + meta_distances[header].append(float(dist)) + except (TypeError, ValueError): + # Surface bad distance type immediately + raise TypeError(f"distance_to_grid (m) not numeric for '{header}' in sample '{sid}': {dist!r}") + + table.append(row) + + df = pd.DataFrame(table, columns=all_cols) + + # 4) Warn for columns that are entirely null + for col in var_cols: + if df[col].isna().all(): + print(f"WARNING: column has all null values -> {col}") + + # 5) Build column_meta.json before cleaning + def py_type(x): + if isinstance(x, bool): + return "bool" + if isinstance(x, int): + return "int" + if isinstance(x, float): + return "float" + if isinstance(x, str): + return "string" + return None # ignore other types + + meta = {} + total_rows = len(df) + + for header in var_cols: + # ----- distance stats ----- + dists = np.array(meta_distances[header], dtype=float) if meta_distances[header] else np.array([]) + if dists.size > 0: + stats = { + "min": float(np.min(dists)), + "max": float(np.max(dists)), + "mean": float(np.mean(dists)), + "std": float(np.std(dists)), + "median": float(np.median(dists)), + } + else: + stats = { + "min": None, + "max": None, + "mean": None, + "std": None, + "median": None, + } + + # ----- detect array-valued or scalar ----- + col_vals = df[header].tolist() + first_non_null = next((v for v in col_vals if v is not None), None) + + if isinstance(first_non_null, list): + is_array = True + flat_vals = [] + for v in col_vals: + if isinstance(v, list): + flat_vals.extend(v) + type_counts = {} + for v in flat_vals: + t = py_type(v) + if t: + type_counts[t] = type_counts.get(t, 0) + 1 + else: + is_array = False + type_counts = {} + for v in col_vals: + t = py_type(v) + if t: + type_counts[t] = type_counts.get(t, 0) + 1 + + datatype = max(type_counts.items(), key=lambda x: x[1])[0] if type_counts else "unknown" + null_fraction = float(df[header].isna().sum()) / total_rows + + meta[header] = { + "source_datasets": sorted(meta_sources[header]), + "distance_to_grid_stats (m)": stats, + "null_fraction": null_fraction, + "datatype": datatype, + "is_array_valued": is_array, + "description": "", + } + + # 6) Clean numeric scalar columns only + cleaned_cells = {} + cleaned_columns = defaultdict(int) + + for header in var_cols: + if meta[header]["datatype"] not in ("int", "float"): + continue + if not meta[header]["is_array_valued"]: + for row_i, v in df[header].items(): + new_v = clean_numeric_scalar_value(header, v) + if not ( + pd.isna(new_v) and pd.isna(v) + ) and new_v != v: + df.at[row_i, header] = new_v + cleaned_cells[(row_i, header)] = new_v + cleaned_columns[header] += 1 + else: + for row_i, v in df[header].items(): + new_v = clean_numeric_vector_value(header, v) + if new_v != v: + df.at[row_i, header] = new_v + cleaned_cells[(row_i, header)] = new_v + cleaned_columns[header] += 1 + + if cleaned_columns: + print("Cleaned columns:") + for header, n in sorted(cleaned_columns.items()): + print(f" {header}: {n} cells") + else: + print("Cleaned columns: none") + + # 7) Write cleaned values back to dict + for row_i, row in df.iterrows(): + sid = row["id"] + sample = data[sid] + + for theme, theme_obj in sample.items(): + if theme in ("LAT_LONG", "GADM_IDS", "GADM_NAMES", "COUNTRY_CODE", + "SAMPLE_DATE", "SAMPLE_DEPTH_RANGE_CM", "SAMPLE_SOURCE_DATASET"): + continue + + for var, var_obj in theme_obj.items(): + key = f"{theme}:{var}" + header = header_for(key) + + if (row_i, header) in cleaned_cells: + var_obj["value"] = cleaned_cells[(row_i, header)] + + # 8) Save outputs + os.makedirs(os.path.dirname(OUTPUT_CSV), exist_ok=True) + + df_out = df.copy() + df_out = df_out.replace({np.nan: None}) + df_out.to_csv(OUTPUT_CSV, index=False, encoding="utf-8", na_rep="") + + # Write cleaned dict as pickle + with open(OUTPUT_DICT_PKL, "wb") as f: + pickle.dump(data, f) + + # Write cleaned dict as json + with open(OUTPUT_DICT_JSON, "w", encoding="utf-8") as f: + json.dump( + data, + f, + indent=2, + ensure_ascii=False + ) + + with open(OUTPUT_META, "w", encoding="utf-8") as f: + json.dump(meta, f, indent=2) + + with open(OUTPUT_META_NAMES, "w", encoding="utf-8") as f: + json.dump( + {"column_names": sorted(meta.keys())}, + f, + indent=2, + ensure_ascii=False, + ) + + print("Done.") + print(f"Rows: {len(df)} Columns: {len(df.columns)}") + print(f"Cleaned cells: {len(cleaned_cells)}") + print( + f"Saved: {OUTPUT_CSV}, " + f"{OUTPUT_DICT_PKL}, {OUTPUT_DICT_JSON}, " + f"{OUTPUT_META}, {OUTPUT_META_NAMES}" + ) + + +if __name__ == "__main__": + main() diff --git a/src/fuse_esdac/x_05_desc.py b/src/fuse_esdac/x_05_desc.py new file mode 100644 index 0000000000000000000000000000000000000000..d52e870fb40158c292f1763f5ff71ee0f291854c --- /dev/null +++ b/src/fuse_esdac/x_05_desc.py @@ -0,0 +1,84 @@ +import json +import os +import time + +from openai import OpenAI +from tqdm import tqdm + +PATH_META = "./src/fuse_esdac/outputs/05_LLM_desc/column_meta.json" +PATH_TEMPLATE = "./src/fuse_esdac/prompts/desc.txt" +PATH_APIKEY = "./src/fuse_esdac/outputs/05_LLM_desc/apikey.txt" +PATH_EXISTING = "./src/fuse_esdac/large_inputs/existing_descritpions.json" + +# === model selection === +MODEL = "gpt-5" # change to "gpt-4o" / "gpt-4o-mini" / "gpt-4.1" etc. + +# === load key + template === +client = OpenAI(api_key=open(PATH_APIKEY).read().strip()) +prompt_template = open(PATH_TEMPLATE).read() + +# === init meta file if missing === +if not os.path.exists(PATH_META): + os.system(f"cp ./src/fuse_esdac/outputs/04_table/column_meta.json {PATH_META}") + +# === load existing descriptions once (if available) === +if os.path.exists(PATH_EXISTING): + with open(PATH_EXISTING) as f: + existing_desc = json.load(f) +else: + existing_desc = {} + +new_generated = {} + +while True: + with open(PATH_META) as f: + col_meta = json.load(f) + + all_done = True + + for k, v in tqdm(col_meta.items()): + # 1) already has description in current file + if v.get("description"): + continue + + # 2) try to reuse description from existing descriptions file + if isinstance(existing_desc, dict) and existing_desc.get(k): + v["description"] = existing_desc[k] + with open(PATH_META, "w") as f: + json.dump(col_meta, f, indent=2) + continue + + # 3) need to call OpenAI to generate + all_done = False + input_dict = {k: v} + prompt = prompt_template.format(input_dict=input_dict) + + # --- safe call with retry --- + for _ in range(3): + try: + resp = client.chat.completions.create( + model=MODEL, + messages=[{"role": "user", "content": prompt}], + ) + answer = resp.choices[0].message.content.strip() + break + except Exception as e: + print("API error, retrying in 5 sec:", e) + time.sleep(5) + else: + answer = "" + + v["description"] = answer + new_generated[k] = answer # do NOT write back to existing_desc + + # save immediate + with open(PATH_META, "w") as f: + json.dump(col_meta, f, indent=2) + + time.sleep(0.2) # rate limit safety + + if all_done: + break + +print("\n=== Newly generated descriptions (NOT saved to existing_descritpions.json) ===") +print(json.dumps(new_generated, indent=2, ensure_ascii=False)) diff --git a/src/fuse_esdac/x_06_upload.sh b/src/fuse_esdac/x_06_upload.sh new file mode 100644 index 0000000000000000000000000000000000000000..9308ec1a897e061bc6cd8ddf070b3e9c950d1985 --- /dev/null +++ b/src/fuse_esdac/x_06_upload.sh @@ -0,0 +1,25 @@ +rsync -rP src/fuse_esdac/outputs/04_table/final_table.csv datasets/fusion/data_table.csv +rsync -rP src/fuse_esdac/outputs/04_table/final_dict.json datasets/fusion/data_dict.json +rsync -rP src/fuse_esdac/outputs/04_table/final_dict.pkl datasets/fusion/data_dict.pkl + +rsync -rP src/fuse_esdac/outputs/fused_datasets.csv datasets/fusion/meta_fused_datasets.csv +rsync -rP src/fuse_esdac/outputs/04_table/column_names.json datasets/fusion/meta_column_names.json +rsync -rP src/fuse_esdac/outputs/05_LLM_desc/column_meta.json datasets/fusion/meta_column_complete.json + +rsync -rP src/fuse_esdac/outputs/03_photo_gadm/gadm_tree_europe.pkl datasets/fusion/gadm_tree_europe.pkl + +rm -rf datasets/fusion/assets +mkdir -p datasets/fusion/assets + +rsync -rP datasets/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/processed/assets/cond.zip datasets/fusion/assets/ +rsync -rP datasets/esdac/european-hydropedological-data-inventory-eu-hydi-database-0/processed/assets/ret.zip datasets/fusion/assets/ + +rm -rf ./temp +mkdir -p ./temp + +unzip -o datasets/esdac/lucas-2009-topsoil-data/processed/assets/psd.zip -d ./temp +unzip -o datasets/esdac/lucas2015-topsoil-data/processed/assets/psd.zip -d ./temp + +find ./temp -type f | zip datasets/fusion/assets/psd.zip -@ + +rm -rf ./temp diff --git a/src/process/__init__.py b/src/process/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/process/io.py b/src/process/io.py new file mode 100644 index 0000000000000000000000000000000000000000..af088f36ece2135a60c1d250b96fd741877ed0de --- /dev/null +++ b/src/process/io.py @@ -0,0 +1,131 @@ +import shutil +import zipfile +from pathlib import Path + + +def prepare_dirs(host: str, name: str) -> tuple[Path, Path, Path]: + """ + Prepare directory structure for a dataset. + + Args: + host (str): The dataset host name (e.g., 'esdac'). + name (str): The dataset folder name. + + Returns: + Tuple of local_processed_dir, drive_download_dir, drive_processed_dir. + """ + script_dir = Path("src") / host / name + local_processed_dir = script_dir / "processed" + if local_processed_dir.exists(): + shutil.rmtree(local_processed_dir) + local_processed_dir.mkdir(parents=False, exist_ok=False) + + drive_base = Path("datasets") / host / name + drive_download_dir = drive_base / "download" + drive_processed_dir = drive_base / "processed" + drive_processed_dir.mkdir(parents=False, exist_ok=True) + + return local_processed_dir, drive_download_dir, drive_processed_dir + + +def unzip(local_processed_dir: Path, drive_download_dir: Path, zip_files: list[str] = None) -> None: + """ + Unzip specified ZIP files from drive_download_dir into subfolders of local_processed_dir. + + Args: + local_processed_dir (Path): Where to extract ZIP contents. + drive_download_dir (Path): Directory containing ZIP files. + zip_files (list[str], optional): List of ZIP filenames to extract. Extracts all if None. + """ + zip_paths = ( + [drive_download_dir / zf for zf in zip_files] + if zip_files is not None + else list(drive_download_dir.glob("*.zip")) + ) + + for zip_path in zip_paths: + target_dir = local_processed_dir / zip_path.stem + target_dir.mkdir(parents=True, exist_ok=True) + try: + with zipfile.ZipFile(zip_path, "r") as zip_ref: + for name in zip_ref.namelist(): + try: + zip_ref.extract(name, path=target_dir) + except zipfile.BadZipFile: + print(f"⚠️ Skipped corrupted file in {zip_path.name}: {name}") + print(f"βœ… Unzipped {zip_path.name} β†’ {target_dir}") + except Exception as e: + print(f"❌ Failed to unzip {zip_path.name}: {e}") + + +def unzip_nested(directory: Path): + """Recursively unzip all ZIP files under directory.""" + for zip_path in sorted(directory.rglob("*.zip")): + target = zip_path.with_suffix("") + target.mkdir(parents=True, exist_ok=True) + try: + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(target) + print(f"βœ… Unzipped nested: {zip_path.relative_to(directory)} β†’ {target.relative_to(directory)}") + except Exception as e: + print(f"❌ Failed to unzip nested {zip_path}: {e}") + + +def copy_to_local(local_processed_dir: Path, drive_download_dir: Path, files: list[str]) -> None: + """ + Copy specific files from drive_download_dir to local_processed_dir. + + Args: + local_processed_dir (Path): Destination directory. + drive_download_dir (Path): Source directory. + files (list[str]): List of filenames to copy. + """ + for file_name in files: + src_path = drive_download_dir / file_name + dst_path = local_processed_dir / Path(file_name).name + try: + shutil.copy2(src_path, dst_path) + print(f"βœ… Copied: {file_name}") + except Exception as e: + print(f"❌ Failed to copy {file_name}: {e}") + + +def upload( + local_processed_dir: Path, + drive_processed_dir: Path, + move_list: list[Path] = None, + clear_remote_before_upload: bool = True, + remove_local_after_upload: bool = True +) -> None: + """ + Upload processed files from local to drive, optionally clearing remote and cleaning up local. + + Args: + local_processed_dir (Path): Directory containing files to move. + drive_processed_dir (Path): Destination directory on Drive. + move_list (list[Path], optional): List of files to move. If None, moves all in local. + clear_remote_before_upload (bool): Whether to clear drive folder before uploading. + remove_local_after_upload (bool): Whether to remove local folder after upload. + """ + if move_list is None: + move_list = [p for p in local_processed_dir.rglob("*") if p.is_file()] + + if clear_remote_before_upload and drive_processed_dir.exists(): + shutil.rmtree(drive_processed_dir) + print(f"πŸ—‘οΈ Cleared drive processed dir: {drive_processed_dir}") + + print(f"πŸ“€ Uploading to {drive_processed_dir}") + for src_path in move_list: + if not src_path.is_file(): + continue + rel_path = src_path.relative_to(local_processed_dir) + dst_path = drive_processed_dir / rel_path + dst_path.parent.mkdir(parents=True, exist_ok=True) + if dst_path.exists(): + dst_path.unlink() + shutil.move(str(src_path), str(dst_path)) + print(f" βœ… Moved {rel_path}") + + if remove_local_after_upload and local_processed_dir.exists(): + shutil.rmtree(local_processed_dir) + print(f"πŸ—‘οΈ Removed local processed dir: {local_processed_dir}") diff --git a/src/process/processor.py b/src/process/processor.py new file mode 100644 index 0000000000000000000000000000000000000000..439f1f8c3602237118ab9d7c5ec7b100a5205005 --- /dev/null +++ b/src/process/processor.py @@ -0,0 +1,387 @@ +import json +from pathlib import Path + +import geopandas as gpd +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import rasterio +from dbfread import DBF +from matplotlib.colors import Normalize +from osgeo import gdal +from rasterio.features import rasterize +from rasterio.transform import from_bounds + + +def process_tif(input_path: Path, move_list: list[Path] = None, nodata: float = None) -> list[Path]: + """ + Process a single TIFF file by standardising and generating a PNG preview. + + Args: + input_path (Path): Path to the TIFF file. + move_list (list[Path], optional): List to append generated files to. + nodata (float, optional): Overwriting nodata value. + + Returns: + list[Path]: Updated list of processed files. + """ + if move_list is None: + move_list = [] + new_move_list = move_list[:] + + if input_path.suffix.lower() not in [".tif", ".tiff", ".asc"] or input_path.is_dir(): + print(f"⚠️ Skipped non-TIFF file: {input_path}") + return new_move_list + + try: + with rasterio.open(input_path) as src: + data = src.read(1) # preserve original dtype + if nodata is None: + nodata = src.nodata if src.nodata is not None else src.meta.get("nodata") + + # Save standardized compressed TIFF + standardized_path = input_path.with_name(input_path.stem + ".standardized.tif") + with rasterio.open(standardized_path, "w", **src.meta, compress="deflate") as dst: + dst.write(data, 1) + new_move_list.append(standardized_path) + + # Save PNG preview + step = max(1, int(max(data.shape) / 1000)) + data_vis = data[::step, ::step] + if nodata is not None: + mask = np.isclose(data_vis, nodata) + data_vis = np.where(mask, np.nan, data_vis) + + norm = Normalize( + vmin=np.nanpercentile(data_vis, 2), + vmax=np.nanpercentile(data_vis, 98) + ) + scaled = np.uint8(np.clip(norm(data_vis) * 255, 0, 255)) + rgb = np.stack([scaled] * 3, axis=-1) + rgb[np.isnan(data_vis)] = [255, 0, 255] + + png_path = input_path.with_suffix(".png") + plt.imsave(png_path, rgb) + new_move_list.append(png_path) + + print(f"βœ… Processed {input_path.name}") + + except Exception as e: + print(f"❌ Failed to process {input_path.name}: {e}") + + return new_move_list + + +def process_excel(input_path: Path, skip_rows=0, move_list: list[Path] = None) -> list[Path]: + """ + Convert Excel file to CSV, skipping header rows automatically if requested. + + Args: + input_path (Path): Path to the Excel file. + skip_rows (int or 'auto'): Number of rows to skip before header, or 'auto' to detect. + move_list (list[Path], optional): List to append output CSV to. + + Returns: + list[Path]: Updated list of processed files. + """ + if move_list is None: + move_list = [] + new_move_list = move_list[:] + + if input_path.suffix.lower() not in [".xls", ".xlsx"]: + print(f"⚠️ Skipped non-Excel file: {input_path.name}") + return new_move_list + + try: + # Auto-detect header if needed + if skip_rows == "auto": + peek = pd.read_excel(input_path, nrows=5, header=None) + skip_rows = 0 + for _, row in peek.iterrows(): + if all(isinstance(cell, str) for cell in row if pd.notna(cell)): + break + skip_rows += 1 + + # Convert Excel to CSV + df = pd.read_excel(input_path, skiprows=skip_rows) + df.columns = df.columns.str.replace(r'[\r\n]+', ' ', regex=True).str.strip() + csv_path = input_path.with_name(input_path.stem + ".csv") + df.to_csv(csv_path, index=False) + + new_move_list.append(csv_path) + print(f"βœ… Converted Excel to CSV: {csv_path.name}") + + except Exception as e: + print(f"❌ Failed to process {input_path.name}: {e}") + + return new_move_list + + +def process_dbf(input_path: Path, move_list: list[Path] = None) -> list[Path]: + """ + Convert DBF file to CSV. + + Args: + input_path (Path): Path to the DBF file. + move_list (list[Path], optional): List to append output CSV to. + + Returns: + list[Path]: Updated list of processed files. + """ + if move_list is None: + move_list = [] + new_move_list = move_list[:] + + if input_path.suffix.lower() != ".dbf": + print(f"⚠️ Skipped non-DBF file: {input_path.name}") + return new_move_list + + try: + # Read DBF file + df = pd.DataFrame(iter(DBF(input_path))) + + # Clean column names + df.columns = df.columns.str.replace(r'[\r\n]+', ' ', regex=True).str.strip() + + # Save as CSV + csv_path = input_path.with_name(input_path.stem + ".csv") + df.to_csv(csv_path, index=False) + + new_move_list.append(csv_path) + print(f"βœ… Converted DBF to CSV: {csv_path.name}") + + except Exception as e: + print(f"❌ Failed to process {input_path.name}: {e}") + + return new_move_list + + +def process_shp(input_path: Path, move_list: list[Path] = None, + drop_geometry=False) -> list[Path]: + """ + Process a single Shapefile by extracting the attribute table and saving a meta. + + Args: + input_path (Path): Path to the Shapefile (.shp). + move_list (list[Path], optional): List to append output files to. + drop_geometry (bool, optional): Whether to drop geometry columns. + + Returns: + list[Path]: Updated list of processed files. + """ + if move_list is None: + move_list = [] + new_move_list = move_list[:] + + if input_path.suffix.lower() != ".shp": + print(f"⚠️ Skipped non-Shapefile: {input_path.name}") + return new_move_list + + try: + # Read the shapefile + gdf = gpd.read_file(input_path) + + # Save attribute table (drop geometry) + df = gdf.drop(columns=gdf.geometry.name, errors='ignore') if drop_geometry else gdf + csv_path = input_path.with_name(input_path.stem + ".shp.csv") + df.to_csv(csv_path, index=False) + new_move_list.append(csv_path) + + # Save basic meta as JSON + meta = { + "n_features": len(gdf), + "crs": str(gdf.crs), + "bounds": gdf.total_bounds.tolist(), # [minx, miny, maxx, maxy] + "fields": list(df.columns) + } + json_path = input_path.with_name(input_path.stem + ".meta.json") + with open(json_path, "w") as f: + json.dump(meta, f, indent=2) + new_move_list.append(json_path) + + print(f"βœ… Processed Shapefile: {input_path.name}") + + except Exception as e: + print(f"❌ Failed to process {input_path.name}: {e}") + + return new_move_list + + +def process_rdc_rst(rdc_path: Path, move_list: list[Path] = None, nodata: float = None) -> list[Path]: + """ + Process an RDC/RST pair by standardising and generating a PNG preview. + + Args: + rdc_path (Path): Path to the RDC file. + move_list (list[Path], optional): List to append generated files to. + nodata (float, optional): Overwriting nodata value. + + Returns: + list[Path]: Updated list of processed files. + """ + + if move_list is None: + move_list = [] + new_move_list = move_list[:] + + if rdc_path.suffix.lower() != ".rdc": + print(f"⚠️ Skipped non-RDC file: {rdc_path}") + return new_move_list + + # Parse RDC metadata + try: + with open(rdc_path, "r") as f: + lines = f.readlines() + meta_dict = {} + for line in lines: + if ":" in line: + key, value = line.split(":", 1) + meta_dict[key.strip().lower()] = value.strip() + + cols = int(meta_dict["columns"]) + rows = int(meta_dict["rows"]) + min_x = float(meta_dict["min. x"]) + max_y = float(meta_dict["max. y"]) + if nodata is None: + nodata_str = meta_dict.get("flag value", "none") + nodata = None if nodata_str.lower() == "none" else float(nodata_str) + + # Resolution handling + try: + res = float(meta_dict["resolution"]) + except ValueError: + # Compute resolution from extents + res_x = (float(meta_dict["max. x"]) - float(meta_dict["min. x"])) / cols + res_y = (float(meta_dict["max. y"]) - float(meta_dict["min. y"])) / rows + if not np.isclose(res_x, res_y): + print(f"⚠️ WARNING: non-square pixel! res_x={res_x}, res_y={res_y}") + res = res_x # or res_y β€” assume square pixels + + # Define geotransform: (min_x, res, 0, max_y, 0, -res) + geotransform = (min_x, res, 0, max_y, 0, -res) + + except Exception as e: + print(f"❌ Failed to parse RDC: {rdc_path.name}: {e}") + return new_move_list + + # Read RST with GDAL + try: + rst_path = rdc_path.with_suffix(".rst") + ds = gdal.Open(str(rst_path)) + band = ds.GetRasterBand(1) + data = band.ReadAsArray() + + # Apply nodata mask + if nodata is not None: + data = np.ma.masked_equal(data, nodata) + + # Save standardized compressed TIFF + standardized_path = rdc_path.with_name(rdc_path.stem + ".standardized.tif") + driver = gdal.GetDriverByName("GTiff") + out_ds = driver.Create( + str(standardized_path), cols, rows, 1, gdal.GDT_Float32, options=["COMPRESS=DEFLATE"] + ) + out_ds.SetGeoTransform(geotransform) + out_ds.SetProjection("") # No CRS info in RDC, can leave empty or set if known + out_band = out_ds.GetRasterBand(1) + out_band.WriteArray(data) + if nodata is not None: + out_band.SetNoDataValue(nodata) + out_ds.FlushCache() + new_move_list.append(standardized_path) + + # Save PNG preview + step = max(1, int(max(data.shape) / 1000)) + data_vis = data[::step, ::step] + if nodata is not None: + mask = np.isclose(data_vis, nodata) + data_vis = np.where(mask, np.nan, data_vis) + + norm = Normalize( + vmin=np.nanpercentile(data_vis, 2), + vmax=np.nanpercentile(data_vis, 98) + ) + scaled = np.uint8(np.clip(norm(data_vis) * 255, 0, 255)) + rgb = np.stack([scaled] * 3, axis=-1) + rgb[np.isnan(data_vis)] = [255, 0, 255] + + png_path = rdc_path.with_suffix(".png") + plt.imsave(png_path, rgb) + new_move_list.append(png_path) + + print(f"βœ… Processed RDC/RST pair: {rdc_path.name}") + + except Exception as e: + print(f"❌ Failed to process RDC/RST pair: {rdc_path.name}: {e}") + + return new_move_list + + +def rasterize_shp(shp_path, columns, resolution_m=1000, out_dir=None, crs="EPSG:3035"): + """ + Rasterize specified columns from a shapefile into GeoTIFF rasters. + + Parameters + ---------- + shp_path : str or Path + Path to the shapefile (.shp). + columns : list of str + List of column names in the shapefile to rasterize. + resolution_m : float, optional + Pixel size in meters (default = 1000, i.e. 1 km resolution). + out_dir : str or Path, optional + Output directory for GeoTIFFs. If None, same directory as shapefile. + crs : str or int, optional + Target coordinate reference system (default = 'EPSG:3035'). + + Output + ------ + Saves GeoTIFFs named '__.tif' in the output directory. + """ + shp_path = Path(shp_path) + out_dir = Path(out_dir) if out_dir else shp_path.parent + out_dir.mkdir(parents=True, exist_ok=True) + + gdf = gpd.read_file(shp_path) + gdf = gdf.to_crs(crs) + + missing_cols = [c for c in columns if c not in gdf.columns] + if missing_cols: + raise ValueError(f"Missing columns in shapefile: {missing_cols}") + + minx, miny, maxx, maxy = gdf.total_bounds + width = int((maxx - minx) / resolution_m) + height = int((maxy - miny) / resolution_m) + transform = from_bounds(minx, miny, maxx, maxy, width, height) + + stem = shp_path.stem + + for col in columns: + print(f"Rasterizing '{col}' ...") + shapes = ((geom, val) for geom, val in zip(gdf.geometry, gdf[col])) + + out_path = out_dir / f"{stem}__{col}.tif" + with rasterio.open( + out_path, + "w", + driver="GTiff", + height=height, + width=width, + count=1, + dtype="float32", + crs=gdf.crs, + transform=transform, + nodata=-9999, + ) as dst: + raster = rasterize( + shapes=shapes, + out_shape=(height, width), + transform=transform, + fill=-9999, + dtype="float32", + ) + dst.write(raster, 1) + + print(f"βœ… Saved β†’ {out_path}") + + print("πŸŽ‰ Rasterization complete.") diff --git a/viewer.py b/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..d9a90bb3e6318cf5ed39cd04a512ea49c5266d75 --- /dev/null +++ b/viewer.py @@ -0,0 +1,309 @@ +import json +import os +import tkinter as tk +from tkinter import ttk + +import pandas as pd +import rasterio +from PIL import Image, ImageTk + + +class DatasetTreeViewer(tk.Tk): + def __init__(self, host_name): + super().__init__() + self.title("Dataset Viewer β€” Β© Earth Rover Program, 2025") + self.geometry("1200x700") + self.base_path = os.path.join("datasets", host_name) + self.datasets = self.get_datasets(host_name) + + # === Layout === + paned = tk.PanedWindow(self, orient=tk.HORIZONTAL, sashrelief=tk.RAISED, sashwidth=6) + paned.pack(fill=tk.BOTH, expand=True) + + left_frame = tk.Frame(paned) + right_frame = tk.Frame(paned) + paned.add(left_frame) + paned.paneconfigure(left_frame, minsize=300) + paned.add(right_frame) + + bottom_frame = tk.Frame(self) + bottom_frame.pack(fill=tk.X) + + # === Checkbox grid above Treeview (2x2 layout) + checkbox_outer_frame = tk.Frame(left_frame) + checkbox_outer_frame.pack(pady=4) + + self.status_filters = {} + default_checked = {"PROCESSED"} + status_options = ["UNEXAMINED", "SKIPPED", "REQUESTED", "DOWNLOADED", "PROCESSED"] + + for idx, status in enumerate(status_options): + row = idx // 2 + col = idx % 2 + var = tk.BooleanVar(value=status in default_checked) + cb = tk.Checkbutton( + checkbox_outer_frame, + text=status.capitalize(), + variable=var, + command=self.build_tree, + anchor="w", + width=15, # fixed width for alignment + padx=0 + ) + cb.grid(row=row, column=col, sticky="w") + self.status_filters[status] = var + + # === Treeview + self.tree = ttk.Treeview(left_frame) + self.tree.heading("#0", text=f"Datasets from {host_name.upper()}") + self.tree.pack(fill=tk.BOTH, expand=True) + self.tree.bind("<>", self.on_tree_select) + + # === Viewer frame + self.viewer_frame = tk.Frame(right_frame) + self.viewer_frame.pack(fill=tk.BOTH, expand=True) + + # === Bottom info + self.info_text = tk.Text(bottom_frame, height=10, wrap=tk.WORD) + self.info_text.pack(fill=tk.X) + + self.build_tree() + + def get_datasets(self, host_name): + status_path = os.path.join("src", host_name, "status.json") + datasets = {} + + try: + with open(status_path, "r") as f: + items = json.load(f) + + for item in items: + name = item["name"] + dataset_path = os.path.join(self.base_path, name) + processed_path = os.path.join(dataset_path, "processed") + + if not os.path.exists(processed_path): + continue + + file_list = [] + for root, _, files in os.walk(processed_path): + for f in files: + if f.endswith((".csv", ".png", ".tif", ".tiff", ".json", ".txt")): + full_path = os.path.join(root, f) + rel_path = os.path.relpath(full_path, processed_path) + file_list.append(rel_path) + + datasets[name] = { + "name": name, + "title": item.get("title", ""), + "abstract": item.get("abstract") or "", + "request_needed": item.get("request_needed", False), + "status": item.get("status"), + "notes": item.get("notes"), + "screened_by": item.get("screened_by"), + "requested_downloaded_by": item.get("requested_downloaded_by"), + "processed_by": item.get("processed_by"), + "files": sorted(file_list), + "path": dataset_path + } + except Exception as e: + print(f"Error reading {status_path}: {e}") + + return datasets + + def build_tree(self): + self.tree.delete(*self.tree.get_children()) + selected_statuses = {k for k, v in self.status_filters.items() if v.get()} + + for dataset_name, data in self.datasets.items(): + if data.get("status") not in selected_statuses: + continue + + dataset_node = self.tree.insert("", "end", text=dataset_name, open=False) + node_map = {"": dataset_node} + + for rel_path in data["files"]: + parts = rel_path.split(os.sep) + current = dataset_node + for i, part in enumerate(parts): + sub_path = os.path.join(*parts[:i + 1]) + if sub_path not in node_map: + node_map[sub_path] = self.tree.insert(current, "end", text=part, open=False) + current = node_map[sub_path] + + def on_tree_select(self, event): + selected_id = self.tree.focus() + item = self.tree.item(selected_id) + text = item["text"] + parent_id = self.tree.parent(selected_id) + + if parent_id == "": + dataset_name = text + data = self.datasets[dataset_name] + self.info_text.delete(1.0, tk.END) + lines = [ + f"Name: {data['name']}", + f"Title: {data['title']}", + ] + if data.get("abstract"): + lines.append(f"Abstract: {data['abstract']}") + lines.append(f"Request needed: {data['request_needed']}") + lines.append(f"Status: {data['status']}") + if data['notes'] is not None: + lines.append(f"Notes: {data['notes']}") + self.info_text.insert(tk.END, "\n".join(lines)) + self.clear_viewer() + return + + # Traverse upward to get dataset name + full_path_parts = [] + current_id = selected_id + while True: + parent = self.tree.parent(current_id) + if parent == "": + dataset_name = self.tree.item(current_id)["text"] + break + full_path_parts.insert(0, self.tree.item(current_id)["text"]) + current_id = parent + + rel_file_path = os.path.join(*full_path_parts) + full_file_path = os.path.join(self.datasets[dataset_name]["path"], "processed", rel_file_path) + + self.info_text.delete(1.0, tk.END) + + if os.path.isdir(full_file_path): + self.info_text.insert(tk.END, f"Directory: {rel_file_path} from dataset: {dataset_name}\n") + self.clear_viewer() + else: + self.info_text.insert(tk.END, f"File: {rel_file_path} from dataset: {dataset_name}\n") + self.load_file(full_file_path) + + def clear_viewer(self): + for widget in self.viewer_frame.winfo_children(): + widget.destroy() + + def load_file(self, path): + self.clear_viewer() + + if path.endswith(".csv"): + try: + df = pd.read_csv(path, low_memory=False) + self.show_csv(df) + self.info_text.insert(tk.END, f"Number of entries: {len(df)}\n") + except Exception as e: + self.info_text.insert(tk.END, f"Error reading CSV:\n{e}") + + elif path.endswith(".png"): + try: + self.viewer_frame.update_idletasks() + frame_w = self.viewer_frame.winfo_width() + frame_h = self.viewer_frame.winfo_height() + + img = Image.open(path) + img_w, img_h = img.size + + ratio_w = frame_w / img_w + ratio_h = frame_h / img_h + scale = min(ratio_w, ratio_h) + + new_w = int(img_w * scale) + new_h = int(img_h * scale) + resized = img.resize((new_w, new_h), Image.LANCZOS) + photo = ImageTk.PhotoImage(resized) + + label = tk.Label(self.viewer_frame, image=photo) + label.image = photo + label.pack(expand=True) + self.info_text.insert(tk.END, f"Shape: {img_h}x{img_w}\n") + except Exception as e: + self.info_text.insert(tk.END, f"Error displaying PNG:\n{e}") + + elif path.endswith(".json"): + try: + with open(path, "r") as f: + content = json.load(f) + text_widget = tk.Text(self.viewer_frame, wrap=tk.NONE) + text_widget.insert(tk.END, json.dumps(content, indent=2)) + text_widget.configure(state="disabled") + text_widget.pack(fill=tk.BOTH, expand=True) + except Exception as e: + self.info_text.insert(tk.END, f"Error reading JSON:\n{e}") + + elif path.endswith(".tif"): + try: + with rasterio.open(path) as src: + shape = (src.height, src.width) + dtype = src.dtypes[0] + nodata = src.nodata + crs = src.crs + transform = src.transform + + summary = [ + f"Shape: {shape}", + f"Datatype: {dtype}", + f"NoData value: {nodata}", + f"CRS: {crs}", + f"Transform:\n{transform}", + ] + + text_widget = tk.Text(self.viewer_frame, wrap=tk.NONE) + text_widget.insert(tk.END, "\n".join(summary)) + text_widget.configure(state="disabled") + text_widget.pack(fill=tk.BOTH, expand=True) + + except Exception as e: + self.info_text.insert(tk.END, f"Error reading TIF:\n{e}") + + else: + try: + with open(path, "r", encoding="utf-8") as f: + content = f.read() + text_widget = tk.Text(self.viewer_frame, wrap=tk.NONE) + text_widget.insert(tk.END, content) + text_widget.configure(state="disabled") + text_widget.pack(fill=tk.BOTH, expand=True) + except Exception as e: + self.info_text.insert(tk.END, f"Unsupported format for quick view: {os.path.basename(path)}\n") + + def show_csv(self, df): + df = df.head(100) + table = ttk.Treeview(self.viewer_frame, show="headings") + table.pack(fill=tk.BOTH, expand=True) + + scroll_y = tk.Scrollbar(self.viewer_frame, orient="vertical", command=table.yview) + scroll_y.pack(side=tk.RIGHT, fill=tk.Y) + + scroll_x = tk.Scrollbar(self.viewer_frame, orient="horizontal", command=table.xview) + scroll_x.pack(side=tk.BOTTOM, fill=tk.X) + + table.configure(yscrollcommand=scroll_y.set, xscrollcommand=scroll_x.set) + table["columns"] = list(df.columns) + + for col in df.columns: + col_values = df[col].astype(str).head(20).tolist() + max_len = max([len(col)] + [len(val) for val in col_values]) + width_px = max(80, min(400, max_len * 7)) + table.heading(col, text=col) + table.column(col, width=width_px, anchor="w", stretch=False) + + for _, row in df.iterrows(): + values = [self.format_value(val) for val in row] + table.insert("", "end", values=values) + + @staticmethod + def format_value(val): + if isinstance(val, float): + return f"{val:.6g}" + return str(val) + + +# === Run Viewer === +if __name__ == "__main__": + host_name_ = "esdac" + app = DatasetTreeViewer(host_name_) + try: + icon = tk.PhotoImage(file="resources/viewer.png") + app.iconphoto(True, icon) + except Exception as ex: + print("Failed to load icon:", ex) + app.mainloop()