Actual evapotranspiration is the largest outgoing term in most agricultural water balances, and remote sensing is the only practical way to map it over large areas. Competing energy-balance models exist, but their relative merits — and the effect of the choices made around them — are rarely tested together.
ETMapper-GEE estimates remote-sensing actual ET from Landsat inside Google Earth Engine using four models: SEBAL, METRIC, the surface temperature–vegetation triangle (TriAng), and SSEBop. It crosses these with two extrapolation approaches (evaporative fraction, ET fraction), two reference ET types (grass, alfalfa), and two climate forcing datasets (ERA5-Land, CFSv2). The results are evaluated against German flux-tower observations from 2020 to 2022.
Accurate estimation of actual evapotranspiration (ETa) 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-ETa 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 (ETo) and alfalfa (ETr)), 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 (R²) 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 ETo resulted in at least 4 % higher R² and reduction in RMSE by at least 29 % compared to ETr. Forcing ETMapper with ERA5 yielded better accuracy (R² > 4 %, RMSE < 12 %) than when using CFSv2. This study provides an integrated framework for RS-ETa estimation, supporting water-related Sustainable Development Goals, especially in agricultural contexts.