Satellite-Based Estimation of Evapotranspiration over India Using Remote Sensing Models: A Brief Review

Evapotranspiration (ET) is a major component of the hydrological cycle and plays an important role in agricultural water management, drought assessment, ecosystem functioning and regional water-resource planning.Its accurate estimation remains challenging in India because of substantial climatic variability, monsoon seasonality, heterogeneous land cover, intensive irrigation and the limited spatial distribution of ground-based measurement systems. Satellite remote sensing provides an effective means of estimating the spatial distribution of evapotranspiration by integrating observations of land-surface temperature, vegetation characteristics, surface reflectance and meteorological conditions. This review examines the principal approaches used for satellite-based ET estimation and discusses their applicability to Indian agro-climatic conditions. Particular attention is given to surface energy balance models, including the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) and the Operational Simplified Surface Energy Balance model (SSEBop). Satellitedriven products and complementary approaches, including MODIS-based evapotranspiration, the GlobalLand Evaporation Amsterdam Model (GLEAM) and vegetation-based methods, are also considered.Indian applications demonstrate the potential of remote sensing for crop-water assessment, regional water accounting, drought monitoring and the analysis of long-term evapotranspiration variability.However, differences in input data, spatial and temporal resolution, calibration procedures and validation strategies complicate comparisons between models.Cloud contamination, thermal-infrared data gaps, meteorological uncertainty and scale mismatches between satellite pixels and ground observations remain important limitations.The review identifies priorities for improving ET estimation in India through systematic regional validation, transparent uncertainty analysis, multi-source data integration and the development of operational workflows that account for the country's diverse climatic and agricultural conditions.