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README.md

@@ -1,12 +1,12 @@
-# VIs_to_LAI: Simulate leaf area index from vegetation indices
+# VIs_to_LAI: Simulate Leaf Area Index from Vegetation Indices
 
 **Authors**: Jonghan Ko at Chonnam National University and Chi Tim Ng at Hang Seng University of Hong Kong
 
 **Collaborator**: Jong-oh Ban at Hallym Polytechnic University
 
-**Repository for the model**: https://github.com/RS-iCM/VIs_to_LAI
+**GitHub Repository**: [https://github.com/RS-iCM/VIs_to_LAI](https://github.com/RS-iCM/VIs_to_LAI)
 
-**Repository for bigger data**: https://huggingface.co/datasets/jonghanko/VIs_to_LAI/tree/main
+**HuggingFace Dataset**: [https://huggingface.co/datasets/jonghanko/VIs_to_LAI/tree/main](https://huggingface.co/datasets/jonghanko/VIs_to_LAI/tree/main)
 
 ---
 
@@ -18,27 +18,283 @@ VIsToLAI is a Python-based, open-source software framework designed to estimate
 
 ## Features
 
-- Support for multiple vegetation indices: NDVI, RDVI, OSAVI, and MTVI₁
-- Three modeling approaches: empirical exponential, Log–log, and machine learning regression
-- Extendable API for custom indices and algorithms
-- Pretrained models and reproducible Jupyter notebooks for rice, barley, wheat, and maize
-- 1D and 2D simulation workflows for time-series and geospatial projections
-- Built-in visualization for time-series plots, scatter diagnostics, and geospatial maps
-- Modular design for adaptation to various crops and regions
+- **Multiple Vegetation Indices**: Support for NDVI, RDVI, OSAVI, and MTVI₁
+- **Three Modeling Approaches**: 
+  - Empirical exponential regression
+  - Log–log regression
+  - Machine learning (Extra Trees, Gradient Boosting, DNN)
+- **Flexible Workflows**: 
+  - 1D time-series simulation for point/field data
+  - 2D geospatial simulation for regional mapping
+- **Pretrained Models**: Ready-to-use models for rice, barley, wheat, and maize
+- **Interactive Notebooks**: Jupyter notebooks for reproducible workflows
+- **Extensible API**: Modular design for custom indices and algorithms
+- **Built-in Visualization**: Time-series plots, scatter diagnostics, and geospatial maps
+- **Ensemble Methods**: Combine multiple models for improved accuracy
 
 ---
 
-## Requirements
+## Installation
+
+### Prerequisites
+
+- Python ≥ 3.8 (recommended: Python 3.10+)
+- pip package manager
 
-- Python ≥ 3.10  
-- numpy  
-- pandas  
-- matplotlib  
-- scikit-learn  
-- scipy 
+### Quick Install
 
-Install dependencies using:
+Install the package with all dependencies:
 
 ```bash
 pip install -r requirements.txt
+```
+
+Or install as an editable package:
+
+```bash
+pip install -e .
+```
+
+### Full Installation (with 2D/spatial features and Jupyter)
+
+For 2D mapping and geospatial analysis:
+
+```bash
+pip install -e ".[all]"
+```
+
+This includes:
+- Core dependencies
+- Cartopy (for 2D mapping and shapefile support)
+- tqdm (progress bars)
+- Jupyter notebooks
+
+### Optional Extras
+
+```bash
+# For 2D/spatial analysis only
+pip install -e ".[2d]"
+
+# For Jupyter development only
+pip install -e ".[dev]"
+```
+
+### Docker Installation
+
+Build and run with Docker:
+
+```bash
+# Build the image
+docker build -t vis-to-lai-crops .
+
+# Run with Docker Compose (recommended)
+docker-compose up --build
+```
+
+Access Jupyter Lab at `http://localhost:8888` (check container logs for token).
+
+---
+
+## Quick Start
+
+### 1D Time-Series Simulation
+
+Run a notebook for 1D LAI simulation:
+
+```bash
+jupyter notebook RUN_Python_Rice.ipynb
+```
+
+Or use Python directly:
+
+```python
+from codes.sim_VIs_to_LAI_crops import main
+import os
+
+# Set paths
+path = os.path.abspath(os.getcwd())
+para_FN = path + '/data/empirical_reg_parameters_rice.txt'
+wobs_FN2 = path + '/data/Rice_LAI_n_VIs.csv'
+data_FN = path + '/data/Rice_FN_NICS_2021.csv'
+output_FN = path + '/outputs/SLAI_rice.out'
+
+# Model files
+DNN_FN = path + '/models/rice_NN.h5'
+pkl_FN = path + '/models/pickle_extra_trees_Rice.pkl'
+pkl_seq_FN = path + '/models/pickle_extra_trees_Rice_seq.pkl'
+
+# Run simulation
+# reg_opt: 0=DNN, 1=ML, 3=NDVI-based, 4=four VIs-based, 5=log-log, 7=Ensemble
+main(DNN_FN, pkl_FN, pkl_seq_FN, 
+     reg_opt=7,    # Ensemble method
+     plot_opt=1,   # Show plot
+     file_opt=1,   # Save output
+     flag=5.5,     # Max LAI value
+     para_FN=para_FN,
+     wobs_FN2=wobs_FN2,
+     data_FN=data_FN,
+     output_FN=output_FN)
+```
+
+### 2D Geospatial Simulation
+
+For 2D regional mapping:
+
+```bash
+jupyter notebook RUN_Python_LAI_2D_Rice.ipynb
+```
+
+---
+
+## Available Notebooks
+
+### 1D Time-Series Notebooks
+- `RUN_Python_Rice.ipynb` - Rice LAI simulation
+- `RUN_Python_Barley.ipynb` - Barley LAI simulation
+- `RUN_Python_Wheat.ipynb` - Wheat LAI simulation
+- `RUN_Python_Maize.ipynb` - Maize LAI simulation
+
+### 2D Geospatial Notebooks
+- `RUN_Python_LAI_2D_Rice.ipynb` - Regional rice LAI mapping
+- `RUN_Python_LAI_2D_Maize.ipynb` - Regional maize LAI mapping
+
+---
+
+## Model Options
+
+The framework supports multiple regression options (`reg_opt` parameter):
+
+- **0**: Deep Neural Network (DNN)
+- **1**: Machine Learning - Extra Trees Regressor
+- **2**: Machine Learning - Sequential (with temporal features)
+- **3**: NDVI-based empirical regression
+- **4**: Four VIs-based empirical regression (ensemble of all VIs)
+- **5**: Log-log regression
+- **6**: Ensemble 1 (DNN + ML + VIs + Log-log)
+- **7**: Ensemble 2 (ML + VIs + Log-log) - **Recommended**
+
+---
+
+## Project Structure
+
+```
+VIs_to_LAI_crops/
+├── codes/                      # Core Python modules
+│   ├── sim_VIs_to_LAI_crops.py      # Main 1D simulation module
+│   ├── empirical_VIs_to_LAI_2D_*.py # 2D empirical modules
+│   └── each_crop_model/        # Crop-specific models
+├── data/                       # Input data (CSV, OBS, TXT)
+│   ├── *_LAI_n_VIs.csv        # Training data
+│   └── empirical_reg_parameters_*.txt  # Regression parameters
+├── models/                     # Pretrained models
+│   ├── *_NN.h5                # DNN models
+│   └── pickle_*.pkl           # ML models
+├── outputs/                    # Simulation outputs
+│   └── SLAI_*.out             # Simulated LAI files
+├── class_map_*/                # 2D class maps
+├── vis_*/                      # 2D vegetation indices
+├── Shape_*/                    # Shapefile boundaries (2D)
+├── RUN_Python_*.ipynb          # Jupyter notebooks
+├── setup.py                    # Package setup
+├── requirements.txt            # Python dependencies
+└── Dockerfile                  # Docker configuration
+```
+
+---
+
+## Requirements
+
+### Core Dependencies
+- numpy ≥ 1.20.0
+- pandas ≥ 1.3.0
+- scipy ≥ 1.7.0
+- scikit-learn ≥ 1.0.0
+- matplotlib ≥ 3.4.0
+- tensorflow ≥ 2.8.0
+- keras ≥ 2.8.0
+- h5py ≥ 3.0.0
+- pyyaml ≥ 5.4.0
+
+### Optional Dependencies (for 2D features)
+- cartopy ≥ 0.20.0 (geospatial mapping)
+- tqdm ≥ 4.64.0 (progress bars)
+
+### Development Dependencies
+- jupyter ≥ 1.0.0
+- ipykernel ≥ 6.0.0
+- notebook ≥ 6.4.0
+
+See `requirements.txt` for a complete list.
+
+---
+
+## Usage Examples
+
+### Example 1: Rice LAI Simulation with Ensemble Method
+
+```python
+from codes.sim_VIs_to_LAI_crops import main
+import os
+
+path = os.path.abspath(os.getcwd())
+main(
+    DNN_FN=path + '/models/rice_NN.h5',
+    pkl_FN=path + '/models/pickle_extra_trees_Rice.pkl',
+    pkl_seq_FN=path + '/models/pickle_extra_trees_Rice_seq.pkl',
+    reg_opt=7,  # Ensemble method
+    plot_opt=1,
+    file_opt=1,
+    flag=5.5,
+    para_FN=path + '/data/empirical_reg_parameters_rice.txt',
+    wobs_FN2=path + '/data/Rice_LAI_n_VIs.csv',
+    data_FN=path + '/data/Rice_FN_NICS_2021.csv',
+    output_FN=path + '/outputs/SLAI_rice.out'
+)
+```
+
+---
+
+## Citation
+
+If you use this software in your research, please cite:
+
+```bibtex
+@software{vistolai2024,
+  author = {Ko, Jonghan and Ng, Chi Tim},
+  title = {VIs_to_LAI: Simulate Leaf Area Index from Vegetation Indices},
+  year = {2024},
+  url = {https://github.com/RS-iCM/VIs_to_LAI}
+}
+```
+
+---
+
+## License
+
+[Specify your license here - e.g., MIT, Apache 2.0, etc.]
+
+---
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+---
+
+## Support
+
+For questions, issues, or contributions, please visit:
+- **GitHub Issues**: [https://github.com/RS-iCM/VIs_to_LAI/issues](https://github.com/RS-iCM/VIs_to_LAI/issues)
+- **HuggingFace**: [https://huggingface.co/datasets/jonghanko/VIs_to_LAI](https://huggingface.co/datasets/jonghanko/VIs_to_LAI)
+
+---
+
+## Acknowledgments
+
+- Chonnam National University
+- Hang Seng University of Hong Kong
+- Hallym Polytechnic University
+
+---
 
+**Last Updated**: August 2025

requirements.txt

@@ -1,6 +1,28 @@
-numpy>=1.22
-pandas>=1.5
-matplotlib>=3.5
-scikit-learn>=1.1
-scipy>=1.8
-tqdm>=4.64
+# Core dependencies for VIs_to_LAI_crops
+# These are the minimum required packages for basic functionality
+
+# Scientific computing and data manipulation
+numpy>=1.20.0
+pandas>=1.3.0
+scipy>=1.7.0
+
+# Machine learning
+scikit-learn>=1.0.0
+tensorflow>=2.8.0
+keras>=2.8.0
+
+# Data I/O and file handling
+h5py>=3.0.0
+pyyaml>=5.4.0
+
+# Visualization
+matplotlib>=3.4.0
+
+# Optional dependencies for 2D/spatial analysis (uncomment if needed)
+# cartopy>=0.20.0  # Required for 2D mapping and shapefile support
+# tqdm>=4.64.0     # Progress bars for long-running operations
+
+# Development dependencies (for Jupyter notebooks)
+# jupyter>=1.0.0
+# ipykernel>=6.0.0
+# notebook>=6.4.0