Spatial assessment of evapotranspiration in the Munneru river sub-basin through remote sensing and GIS-based approaches

Water plays a vital role in sustaining agricultural productivity, yet its availability is increasingly threatened by population growth, urbanization, and climate change. Efficient irrigation and water management rely on accurate estimation of crop water requirements and evapotranspiration (ET). Remote sensing and GIS-based techniques provide advanced tools for spatial assessment of water demand and crop water use across extensive agricultural regions. The present study focuses on the Munneru River Sub-Basin in Telangana, employing geospatial methods to estimate crop water requirements, promote sustainable irrigation planning, and enhance water use efficiency. The Munneru River Sub-Basin, a tributary of the Krishna River, spans approximately 9,500 km², largely dominated by agricultural land. Climatic variables from 2015-2025 were analyzed using the FAO-56 Penman-Monteith method to compute reference evapotranspiration (ETo). Landsat-9 and SRTM DEM (30 m) data were processed in QGIS and Google Earth Engine to derive Land Use/Land Cover (LULC), NDVI, and Land Surface Temperature (LST) maps. Crop coefficients (Kc) were obtained using Kc = 1.25 × NDVI - 0.05. The SEBAL model estimated actual evapotranspiration (ETa), while CROPWAT 8.0 computed crop water demand. Results showed that NDVI effectively captured vegetation dynamics, and a strong correlation (R² = 0.87) between NDVI and Kc validated remote sensing-based estimation. Paddy exhibited the highest water requirement (719.4 mm), followed by maize (448.1 mm) and chillies (395.0 mm). Spatial analysis revealed higher water demand in command areas with paddy fields. The study emphasizes adopting precision irrigation, crop diversification, and efficient water management for sustainable agriculture.