Assessing the impact of end‐member selection on the accuracy of satellite‐based spatial variability models for actual evapotranspiration estimation. Issue 5 (28th May 2013) - Long, Di; Singh, Vijay P.
Key Points: Endmembers largely determine the magnitudes of EF retrievals Endmembers have minor effect on standard deviation and skewness of EF retrievals Predictability of ET models depends mostly on the ability of Ts to capture EF Abstract : [1] This study examines the impact of end‐member (i.e., hot and cold extremes) selection on the performance and mechanisms of error propagation in satellite‐based spatial variability models for estimating actual evapotranspiration, using the triangle, surface energy balance algorithm for land (SEBAL), and mapping evapotranspiration with high resolution and internalized calibration (METRIC) models. These models were applied to the soil moisture‐atmosphere coupling experiment site in central Iowa on two Landsat Thematic Mapper/Enhanced Thematic Mapper Plus acquisition dates in 2002. Evaporative fraction (EF, defined as the ratio of latent heat flux to availability energy) estimates from the three models at field and watershed scales were examined using varying end‐members. Results show that the end‐members fundamentally determine the magnitudes of EF retrievals at both field and watershed scales. The hot and cold extremes exercise a similar impact on the discrepancy between the EF estimates and the ground‐based measurements, i.e., given a hot (cold) extreme, the EF estimates tend to increase with increasing temperature of cold (hot) extreme, and decrease with decreasing temperature of cold (hot) extreme. The coefficient of determination between the EF estimates and the ground‐based measurements depends principally on the capability of remotely sensed surface temperature ( T s ) to capture EF (i.e., depending on the correlation between T s and EF measurements), being slightly influenced by the end‐members. Varying the end‐members does not substantially affect the standard deviation and skewness of the EF frequency distributions from the same model at the watershed scale. However, different models generate markedly different EF frequency distributions due to differing model physics, especially the limiting edges of EF defined in the remotely sensed vegetation fraction ( f c ) and T s space. In general, the end‐members cannot be properly determined because (1) they do not necessarily exist within a scene, varying with the spatial extent, resolution, and quality of satellite images being used and/or (2) different operators can select different end‐members. Furthermore, the limiting edge of EF = 0 in the f c ‐ T s space varies with the model, with SEBAL‐type models having inherently an increasing curvilinear limiting edge of EF = 0 with f c . The spatial variability models therefore require careful calibration in order to deduce reasonable EF‐limiting edges and then confine the magnitudes of EF estimates.