Near Real-Time Estimation of Daytime and Nighttime Evapotranspiration Using GOES-R Observations and Machine Learning Models

Abstract. Evapotranspiration (ET) is a critical component of the water cycle, influencing climate, agriculture, and water resource management. However, most satellite-derived ET products are limited to daily or coarser temporal resolutions, despite the strong diurnal variability of ET processes. Existing satellite-based ET retrievals are largely restricted to daytime conditions,  when nighttime ET is a small but often non-trivial flux. In this study, we introduce the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems ET (ALIVE_ET), a near real-time, five-minute ET estimation framework, leveraging geostationary satellite observations from the GOES-R Advanced Baseline Imager (ABI) and machine learning models under both clear and cloudy conditions. We test Gradient Boosting Regression (GBR) and Long Short-Term Memory (LSTM) models to assess their ability to estimate ET variations across the diurnal cycle. GBR captures daytime ET with an R² of 0.74 (RMSE of 0.059 mm hh^-1 equivalent to about 74 W m^-2) while maintaining low computational cost. For nighttime ET, where R² decreases by about 0.50 compared to daytime, LSTM models trained on time-series observations perform better, achieving an R² of 0.24 (RMSE of 0.014 mm hh^-1) by leveraging temporal dependencies in land surface temperature (LST) and past ABI observations. Comparisons against daily ET estimates from the physically-based ALEXI remote sensing model demonstrates good agreement but opportunities for improvement. This study demonstrates the potential of integrating machine learning with geostationary remote sensing to advance high-temporal-resolution ET estimation.