Physics-Informed Earth Observation for High-Resolution Crop Evapotranspiration Mapping and Sustainable Agricultural Water Management

Accurate estimation of crop evapotranspiration (ETc) is fundamental to irrigation planning and sustainable agricultural water management, particularly under increasing climate variability and water scarcity. Conventional flux measurement systems, including eddy covariance towers and lysimeters, provide high-quality observations but are costly, maintenance-intensive, and spatially constrained, limiting their scalability for precision water management. This study presents AquaVolt-AI, a physics-informed machine learning framework that integrates Sentinel-2 optical imagery, NASA ECOSTRESS thermal observations, and meteorological data with the FAO-56 dual crop-coefficient formulation to generate spatially explicit ETc estimates without requiring dedicated on-site sensing infrastructure for routine operation. The framework couples a dynamic residual neural network with physics-based constraints and an automated state-estimation mechanism designed to maintain inference during satellite data gaps and external data-service interruptions. AquaVolt-AI was evaluated over 36 days (28 June–3 August 2026) at the UC Davis Russell Ranch Sustainable Agriculture Facility using ground-based CIMIS observations for validation and ECOSTRESS thermal data as an auxiliary model input. ETc was represented across a 16 × 16 virtual sensing grid comprising 256 spatial sectors at 10 m resolution. The framework achieved a root mean square error of 0.3000 mm day−1 and a mean absolute error of 0.2688 mm day−1. During a consecutive 9-day satellite data gap, the physics-informed state estimator maintained continuous ETc predictions without detectable empirical drift in the evaluated period. These findings demonstrate the feasibility of integrating Earth observation, meteorological information, and physics-informed machine learning within a low-infrastructure computational framework for spatially resolved ETc monitoring. The approach provides a scalable foundation for precision irrigation assessment and data-driven agricultural water management, although broader multi-season and multi-site validation is required to establish transferability across cropping systems and agroclimatic environments. The main novelty of this study is in coupling a bounded residual neural correction to the FAO-56 dual crop coefficient model within a fully serverless architecture, eliminating on-site sensing hardware while preserving physical plausibility during data outages.