Integrating Sentinel Landsat Fusion with ALEXI Framework for Physically Based Evapotranspiration Estimation in a Monsoon-Dominated River Basin

Evapotranspiration (ET) is a key component of terrestrial water balance and is vital in monsoon driven river basins where strong seasonal variability in land atmosphere interactions governs water availability and agricultural productivity. This study presents a basin scale ET estimation framework that integrates Atmosphere Land Exchange Inverse (ALEXI) model with multi-sensor optical thermal remote sensing through Sentinel Landsat data fusion for the Mahanadi River Basin, India. The approach enhances spatial and temporal characterization of land surface processes while retaining the physically based foundation of the ALEXI model. ALEXI is a two-source energy balance model that partitions surface fluxes between soil and vegetation canopy components and estimates latent heat flux based on the temporal increase in land surface temperature (LST) from early morning to mid-morning. In this study, thermal information from Landsat is combined with the high resolution surface reflectance, vegetation indices, and land cover information derived from Sentinel-2 to better represent surface heterogeneity across agricultural, forested, and mixed land-use areas. Meteorological forcing, including air temperature, wind speed, humidity, and incoming solar radiation, has been used to model atmospheric boundary layer (ABL) growth and derive sensible heat flux, while latent heat flux has been computed as a residual of the surface energy balance and converted to ET. The fusion of Sentinel and Landsat data improves spatial detail in canopy soil energy partitioning, enabling more accurate ET estimation in fragmented agricultural landscapes characteristic of the Mahanadi Basin. The temperature differential nature of ALEXI reduces sensitivity to absolute LST biases and atmospheric correction uncertainties, making it particularly suitable for large, cloud prone monsoon basins. The resulting ET estimates capture seasonal water use dynamics and drought stress patterns across kharif and rabi cropping cycles. This integrated ALEXI multi sensor framework provides a scalable and physically consistent approach for basin-scale hydrological assessment, offering valuable insights for irrigation management, drought monitoring, and sustainable water resources planning in data-scarce regions.