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.
Dighe et al. (2026) studied this question.