Enhancing Evapotranspiration Estimations Using a Single Source Energy Balance Model with Input of Composited Thermal Infrared Temperatures

Evapotranspiration (ET) plays an important role in water resources, crop management and other fields. Thermal infrared (TIR) land surface temperature (LST) provides essential information for estimating ET by using single-source energy balance method. Nevertheless, variations in the viewing angles of remote sensing sensors can introduce angular effects in the retrieval of LST, potentially causing significant incompatibility issues in estimating ET. To alleviate this uncertainty, we adopt a viable approach by using two composited LSTs derived from the integration of soil and vegetation component temperatures based on Sentinel-3 SLSTR and two kinds of fraction vegetation coverage data. This composited LST was subsequently applied in a single-source energy balance system (SEBS) model driven by measured and ERA5 reanalysis meteorological data in Heihe River Basin in China, respectively. The results demonstrate that our improved approach with two kinds of composited LST reduced the root mean square error (RMSE) of estimated ET by 4.84 W/m2 and 8.81 W/m2 using measured meteorological data driven model; and using ERA5 meteorological data driven model, the RMSE was reduced by 6.78 W/m2 and 10.97 W/m2, with a reduction observed at each site.