Evaluation of alternative two-source remote sensing models in partitioning of land evapotranspiration

Abstract Remote sensing (RS)-based two-source models have been widely used to separate soil evaporation (E) and vegetation transpiration (T) in ecosystems. Alternative model assumptions and physical mechanisms employed in different two-source models cause the difference in model performance. Understanding of the characteristics and limitations of alternative two-source models is critical to ensure the most effective applications in various climatic zones and underlying surfaces. This study investigated two types of land surface temperature (LST) decomposition based two-source models, namely Two-Source Energy Balance Model (TSEB) and Modified Pixel Component Arranging and Comparing Algorithm (M-PCACA); as well as a meteorological factors-based Penman-Monteith type model (PM-MU) in the partitioning of E and T. The three models were compared at 21 flux stations across China, which included seven types of underlying surfaces and one isotope station. The study demonstrated that all three models were applicable in a large scale study for latent heat flux (LE) estimation. Overall, M-PCACA performed the best with average root-mean-square errors (RMSE) of 38.7 W/m2, while PM-MU performs the worst with average RMSE of 61.4 W/m2. For ET partitioning, the two models based on LST decomposition (TSEB and M-PCACA) gave more reliable estimates compared with stable water isotope observations. In addition, verification results for the seven types of underlying surfaces showed that the TSEB gave the best accuracy in the areas with high leaf area index (LAI) and vegetation height (e.g. forest), while the PM-MU had an advantage in the areas without vegetation (e.g. deserts and water bodies). Due to the characteristics of the land surface temperature-vegetation coverage (LST-fc) trapezoidal framework, the M-PCACA model had the most stable model performance for various underlying surfaces and gave the best estimates in grassland, wetland, and farmland areas. Using a thorough discussion, this study identified the sources of discrepancies induced by different physical mechanisms of three remote sensing-based two-source models. This investigation provided insights to better understand the alternative two-source models and provides guidance for model application.