Spatio-Temporal Crop Health Assessment for the Jammu Region using Multi-Source Remote Sensing and Machine Learning
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901540429627392 |
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| author | Verma, Divyam |
| author_facet | Verma, Divyam |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20259081 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Spatio-Temporal Crop Health Assessment for the Jammu Region using Multi-Source Remote Sensing and Machine Learning Verma, Divyam remote sensing precision agriculture crop health Sentinel-2 MODIS CHRIPS Google Earth Engine Machine Learning Jammu and Kashmir <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> |
| title | Spatio-Temporal Crop Health Assessment for the Jammu Region using Multi-Source Remote Sensing and Machine Learning |
| topic | remote sensing precision agriculture crop health Sentinel-2 MODIS CHRIPS Google Earth Engine Machine Learning Jammu and Kashmir |
| url | https://doi.org/10.5281/zenodo.20259081 |