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

Agricultural drought and crop water stress pose persistent challenges in semi-arid regions of India, particularly during the Rabi season when irrigation demand is high, and rainfall is limited. Remote sensing provides spatially explicit observations for monitoring crop water conditions; however, many existing approaches rely on single indicators, lack uncertainty handling, or are not readily transferable to operational decision-support systems. This study presents an integrated geospatial framework for village-scale crop water stress and drought monitoring using satellite remote sensing and GIS, demonstrated for Sangamner Taluka, Maharashtra, India. The framework combines Landsat 8/9 thermal and multispectral observations with hydro-meteorological variables derived from ERA5-Land, CHIRPS, and MODIS. An empirical satellite-based Crop Water Stress Index (ES-CWSI) is computed using NDVI-derived emissivity and percentile-based normalisation of land surface temperature within each scene, enabling spatially relative assessment of crop stress during the Rabi season. Supporting indicators, including antecedent rainfall, root-zone soil moisture, and evapotranspiration, are integrated conditionally to derive a composite Drought Severity Index (DSI) designed for decision support rather than long-term climatological analysis. All processing steps are explicitly reproducible and implemented across three layers: data acquisition and quality control in Google Earth Engine, spatial modelling in a GIS environment, and visualisation with embedded validation analytics through a web-based dashboard. Results reveal substantial intra-taluka variability in crop water stress and drought severity, highlighting villages with persistent or localised stress patterns. The proposed framework demonstrates how multi-source satellite data can be systematically integrated into an operationally robust, uncertainty-aware drought monitoring system suitable for regional agricultural advisory applications.