Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China

Land surface temperature (LST) derived from satellite thermal infrared (TIR) remote sensing is widely used in estimating land surface evapotranspiration (ET) through energy balance theory. However, as satellite TIR remote sensing is frequently affected by clouds, the derived LST with spatial missing makes it impossible to estimate seamless ET for large areas. In this study, based on the all-weather LST (AWLST) generated through the synergistic use of TIR and passive microwave (PMW) remote sensing, we propose an estimation scheme of all-weather ET (AWET) for the River Source Region of Southwest China (RSR-SC) with complex environmental characteristics of frequent clouds and fog. Specifically, the parameters of the Surface Energy Balance System (SEBS), which have high impact on the model results, are first accurately determined by a global sensitivity analysis. Second, the parametric calculation schemes of turbulent exchange, such as surface roughness heights for momentum (z₀ₘ) and heat (z₀ₕ) transfer, and soil heat flux (G₀) of the SEBS model are refined. Third, the daily ET temporal upscaling method is developed. Then a long-term AWET product for the RSR-SC is generated. Comparison against ground measurements from 12 eddy covariance (EC) sites indicates a good accuracy of the AWET product: the mean absolute percent error (MAPE) and root mean square error (RMSE) of the daily ET estimates are 36 % and 0.88 mm/d, respectively. From aspects of temporal variation of daily ET, monthly spatiotemporal distribution patterns, interannual variation of different land covers and spatial distribution comparison with other ET products, the generated AWET exhibits to be realistic, reflecting relatively subtle variations. Further investigation indicates that the response of surface ET to the warming temperatures exhibits the spatial heterogeneity of the change trend. This study highlights the importance of estimating daily AWET at a MODIS-like scale using AWLST data to significantly benefit water resource monitoring and conduct runoff prediction over large areas and watershed scales.