A Multi-model approach for remote sensing-based actual evapotranspiration mapping using Google Earth Engine (ETMapper-GEE)

• A framework was proposed to map and assess ET a in the GEE environment. • ETMapper framework was evaluated using the ICOS flux dataset in Germany. • Grass reference ET (ET o ) from ERA5-Land meteorology is better for ET a modeling. • The EF extrapolation approach outperformed the ETF extrapolation approach. • Feature space model outperformed iterative and non-iterative models. Accurate estimation of actual evapotranspiration (ET a ) through remote sensing (RS) is essential for effective large-scale water management. We developed an EvapoTranspiration Mapper in the Google Earth Engine environment (ETMapper-GEE) to estimate RS-ET a using Landsat satellite data employing four models: Surface Energy Balance Algorithm for Land (SEBAL), Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), surface temperature-vegetation-based triangle (TriAng), and Operational Simplified Surface Energy Balance (SSEBop). The estimation integrates extrapolation approaches (Evaporative Fraction (EF) and EvapoTranspiration Fraction (ETF)), reference ET types (grass (ET o ) and alfalfa (ET r )), and climate forcing datasets (the fifth generation of the European ReAnalysis (ERA5-Land) and the Climate Forecast System version 2 (CFSv2)). The ETMapper was evaluated against observed data from flux towers in Germany for the period 2020 to 2022. The results showed that EF outperformed the ETF approach, with a more than an 8 % higher correlation of determination (R 2 ) and 35 % lower Root Mean Square Error (RMSE) compared to the other approaches. Among the EF approaches, TriAng (RMSE = 1.38 mm d -1 ) exhibited the best performance, followed by METRIC (1.69 mm d -1 ) and SEBAL (2.07 mm d -1 ). Using ETMapper with ET o resulted in at least 4 % higher R 2 and reduction in RMSE by at least 29 % compared to ET r . Forcing ETMapper with ERA5 yielded better accuracy (R 2 > 4 %, RMSE < 12 %) than when using CFSv2. This study provides an integrated framework for RS-ET a estimation, supporting water-related Sustainable Development Goals, especially in agricultural contexts.