GEE-DisALEXI: cloud-based implementation of the DisALEXI model for evapotranspiration monitoring using Google Earth Engine

Evapotranspiration (ET), a key component of the terrestrial water and energy cycles, is essential for understanding ecosystem productivity, water supplies, vegetation water use, and vegetation health. While traditional ground-based methods offer direct ET measurements, they are limited in spatial coverage and scalability. Two Source Energy Balance (TSEB) based satellite remote sensing ET retrieval algorithms have emerged as a powerful tool for estimating ET across diverse landscapes, providing robust field-to-regional ET estimates. With increasing needs for field-scale ET data for applications in agriculture, forest and water resources management, traditional ET computing relying on local servers is challenged for data storage and computing capability. The integration of ET models into the cloud-based platform via Google Earth Engine (GEE) enables scalable, high-resolution ET data production and delivery to stakeholders. This paper presents the cloud implementation of Disaggregation of the Atmosphere Land Exchange Inverse model (DisALEXI) on GEE, detailing technical enhancements, model evaluation across biomes and climate zones, and comparison with water balance estimated ET at basin-scale. Among all the land cover types assessed, GEE-DisALEXI consistently exhibited the best performance in croplands across all time scales, particularly during the growing season, where the model achieves a normalized MAE of 16.8 % at monthly timesteps. The annual bias of DisALEXI ET comparing with water balance estimated ET at Hydrologic Unit Code (HUC) 08 basins is −0.36 %. An anomaly of the ratio between ET and reference ET is calculated at regional scale and is compared with US. Drought Monitor data to explore the capability of using ET metrics for drought monitoring over different climate zones. The ET metric shows good correlation with US. Drought Monitor drought signal and is the strongest over humid areas. We also discuss the unique features, current limitations, and future directions for improving GEE-DisALEXI, including opportunities for enhanced forcing data and parameterization, to advance cloud-based ET modeling for water and agriculture management.