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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.20259081 |
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Table of Contents:
- <div> <div>This work presents an end-to-end pipeline for spatio-temporal</div> <div> crop health assessment in the Jammu region of India. Multi-</div> <div> source Earth observation data Sentinel-2 surface reflectance,</div> <div> MODIS land-surface temperature, and CHIRPS precipitation are</div> <div> harmonised over 150 fixed sampling points to derive seven</div> <div> vegetation and hydro-climatic features. A composite Crop Health</div> <div> Score is modelled with classical and deep-learning regressors</div> <div> under a strict temporal hold-out, interpreted via SHAP, and</div> <div> extended to recursive multi-step forecasting. A Streamlit</div> <div> application exposes the trained model for on-demand,</div> <div> coordinate-level prediction.</div> </div>