A coupled remote sensing and the Surface Energy Balance with Topography Algorithm (SEBTA) to estimate actual evapotranspiration over heterogeneous terrain

Evapotranspiration (ET) may be used as an ecological indicator to address the ecosystem complexity. The accurate measurement of ET is of great significance for studying environmental sustainability, global climate changes, and biodiversity. Remote sensing technologies are capable of monitoring both energy and water fluxes on the surface of the Earth. With this advancement, existing models, such as SEBAL, S SEBI and SEBS, enable us to estimate the regional ET with limited temporal and spatial coverage in the study areas. This paper extends the existing modeling efforts with the inclusion of new components for ET estimation at different temporal and spatial scales under heterogeneous terrain with varying elevations, slopes and aspects. Following a coupled remote sensing and surface energy balance approach, this study emphasizes the structure and function of the Surface Energy Balance with Topography Algorithm (SEBTA). With the aid of the elevation and landscape information, such as slope and aspect parameters derived from the digital elevation model (DEM), and the vegetation cover derived from satellite images, the SEBTA can account for the dynamic impacts of heterogeneous terrain and changing land cover with some varying kinetic parameters (i.e., roughness and zero-plane displacement). Besides, the dry and wet pixels can be recognized automatically and dynamically in image processing thereby making the SEBTA more sensitive to derive the sensible heat flux for ET estimation. To prove the application potential, the SEBTA was carried out Correspondence to: C. S. Liu ([email protected]) to present the robust estimates of 24 h solar radiation over time, which leads to the smooth simulation of the ET over seasons in northern China where the regional climate and vegetation cover in different seasons compound the ET calculations. The SEBTA was validated by the measured data at the ground level. During validation, it shows that the consistency index reached 0.92 and the correlation coefficient was 0.87.