Upload README.md

README.md

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 # 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
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 **Collaborator**: Jong-oh Ban at Hallym Polytechnic University
 
 **Repository for the model**: https://github.com/RS-iCM/VIs_to_LAI
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 **Repository for bigger data**: https://huggingface.co/datasets/jonghanko/VIs_to_LAI/tree/main
 
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 ## Overview
 
-VIsToLAI is a Python-based, open-source software framework designed to estimate leaf area index (LAI) from time-series of satellite-derived vegetation indices (NDVI, RDVI, OSAVI, MTVI₁). By integrating empirical regression, log–log and machine learning modules, VIsToLAI offers a flexible, scalable workflow that bypasses destructive sampling and intensive calibration. Pretrained models, an extensible API, and interactive Jupyter notebooks streamline data ingestion, model execution, and visualization. Demonstrated on staple crops under varied conditions, VIsToLAI accurately reconstructs LAI dynamics and integrates seamlessly into remote sensing workflows for precision agriculture, crop monitoring, and ecological modeling.
+VIsToLAI is a Python-based, open-source software framework designed to estimate leaf area index (LAI) from time series of satellite-derived vegetation indices (NDVI, RDVI, OSAVI, and MTVI1). By integrating empirical regression, Log–log, and machine learning modules, VIsToLAI offers a flexible, scalable workflow that bypasses destructive sampling and intensive calibration. Pretrained models, an extensible API, and interactive Jupyter notebooks streamline data ingestion, model execution, and visualization. Demonstrated on staple crops under varied conditions, VIsToLAI accurately reconstructs LAI dynamics and integrates seamlessly into remote sensing workflows for precision agriculture, crop monitoring, and ecological modeling.
 
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