A_OPTRAM-ET: An automatic optical trapezoid model for evapotranspiration estimation and its global-scale assessments

Abstract

Remotely sensed evapotranspiration (ET) at a high spatial resolution (30 m) has wide-ranging applications in agriculture, hydrology and meteorology. The original optical trapezoid model for ET (O_OPTRAM-ET), which does not require thermal remote sensing, shows potential for high-resolution ET estimation. However, the non-automated O_OPTRAM-ET heavily depends on visual interpretation or optimization with in situ measurements, limiting its practical utility. In this study, a SpatioTemporal Aggregated Regression algorithm (STAR) is proposed to develop an automatic trapezoid model for ET (A_OPTRAM-ET), implemented within the Google Earth Engine environment, and evaluated globally at both moderate and high resolutions (500 m and 30 m, respectively). Through the integration of an aggregation algorithm across multiple dimensions to automatically determine its parameters, A_OPTRAM-ET can operate efficiently without the need for ground-based measurements as input. Evaluation against in situ ET demonstrates that the proposed A_OPTRAM-ET model effectively estimates ET across various land cover types and satellite platforms. The overall root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC) when compared with in situ latent heat flux (LE) measurements are 35.5 W·m⁻² (41.3 W·m⁻², 40.0 W·m⁻², 36.1 W·m⁻²,), 26.3 W·m⁻² (28.9 W·m⁻², 28.7 W·m⁻², 25.8 W·m⁻²,), and 0.78 (0.73, 0.70, 0.72) for Sentinel-2 (Landsat-8, Landsat-5, MOD09GA), respectively. The A_OPTRAM-ET model exhibits a stable accuracy over long time periods (approximately 10 years). When compared with other published ET datasets, ET estimated by the A_OPTRAM-ET model is better with the land cover types of cropland and shrubland. Additionally, global ET derived from the A_OPTRAM-ET model shows trends consistent with other published ET datasets over the period 2001–2020, while offering enhanced spatial details. Therefore, the proposed A_OPTRAM-ET model provides an efficient, high-resolution, and globally applicable method for ET estimation, with significant practical values for agriculture, hydrology, and related fields.