An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes

An intercomparison of output from two models estimating spatially distributed surface energy fluxes from remotely sensed imagery is conducted. A major difference between the two models is whether the soil and vegetation components of the scene are treated separately (Two-Source Energy Balance; TSEB approach) or as a lumped composite (one-source approach; Surface Energy Balance Algorithm for Land; SEBAL) in the parameterization of radiative and turbulent exchanges with the overlying air. Comparisons are performed using data from two largescale field experiments covering sub-humid grassland (Southern Great Plains '97) and semi-arid rangeland (Monsoon '90) having very different landscape properties. In general, there was reasonable agreement between flux output from both models versus a handful of flux tower observations. However, spatial intercomparisons of model output over the full modeling domains yielded relatively large discrepancies (on the order of 100 W m− 2) in sensible heat flux (H) that are related to land cover. In particular, bare soil and sparsely vegetated areas yielded the largest discrepancies, with TSEB fluxes being in better agreement with tower observations. Modifications to SEBAL inputs that reduced discrepancies with TSEB and observations for bare soil and shrub classes tended to increase differences for other land cover classes. In particular, improvements to SEBAL inputs of surface roughness for momentum tended to exacerbate errors with respect to observed fluxes. These results suggest that some of the simplifying assumptions in SEBAL may not be strictly applicable over the wide range in conditions present within these landscapes. An analysis of TSEB and SEBAL sensitivity to uncertainties in primary inputs indicated that errors in surface temperature or surface-air temperature differences had the greatest impact on H estimates. Inputs of secondary importance were fractional vegetation cover for TSEB, while for SEBAL, the selection of pixels representing wet and dry moisture end-member conditions significantly influenced flux predictions. The models were also run using input fields derived from both local and remote data sources, to test performance under conditions of varying ancillary data availability. In this case, both models performed similarly under both constraints.