Development of Geospatial Framework for Crop Water Stress and Drought Monitoring Using Remote Sensing and GIS for Sangamner Taluka, Maharashtra, India

Ganesh Sadashiv Dighe; Onkar Tukaram Gadge; Nikita Sanjay Deshmukh; Sayali Arun Ghule; Mayuri R. Gadhe

doi:10.22146/jgise.118467

Development of Geospatial Framework for Crop Water Stress and Drought Monitoring Using Remote Sensing and GIS for Sangamner Taluka, Maharashtra, India

https://doi.org/10.22146/jgise.118467

Ganesh Sadashiv Dighe^(1*), Onkar Tukaram Gadge⁽²⁾, Nikita Sanjay Deshmukh⁽³⁾, Sayali Arun Ghule⁽⁴⁾, Mayuri R. Gadhe⁽⁵⁾

(1) Department of Civil Engineering, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India
(2) Department of Civil Engineering, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India
(3) Department of Civil Engineering, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India
(4) Department of Civil Engineering, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India
(5) Department of Civil Engineering, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India
(*) Corresponding Author

Abstract

Agricultural drought in semi-arid India threatens food security, particularly during the Rabi season when crops rely on limited irrigation. This study presents an integrated geospatial framework for village-scale crop water stress and drought monitoring, demonstrated in Sangamner Taluka, Maharashtra. The framework combines Landsat 8/9 thermal and multispectral imagery with ERA5-Land soil moisture, CHIRPS rainfall, and MODIS evapotranspiration. An empirical Crop Water Stress Index (ES-CWSI) is computed using NDVI-derived emissivity and scene-specific percentile normalization of land surface temperature, enabling spatially relative stress assessment. A composite Drought Severity Index (DSI) conditionally integrates ES-CWSI, root-zone soil moisture, and rainfall deficits. Processing occurs across three layers: Google Earth Engine for quality-controlled data acquisition, QGIS and Python for index computation, and a web dashboard for visualization and internal validation. Analysis of the 2025–2026 Rabi season reveals substantial intra-taluka variability, with 58.9% of villages experiencing persistent high stress and peak stress occurring on 13 March 2026 (mean ES-CWSI = 0.579). Strong correlations between ES-CWSI and NDVI (𝜌 = −0.81), soil moisture (𝜌 = −0.68), and DSI (𝜌 = 0.96) confirm physical consistency. The framework provides an operationally robust, uncertainty-aware system suitable for regional agricultural advisory applications.

Keywords

Crop Water Stress Index; Agricultural Drought; Remote Sensing; Geographic Information Systems; Landsat Thermal Infrared; Rabi Season; Semi-arid Agriculture

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DOI: https://doi.org/10.22146/jgise.118467