Assessment of an extended SPARSE model for estimating evapotranspiration from directional thermal infrared data

The spatial distribution of evapotranspiration is often obtained from dual source energy balance models forced by surface temperature data. The use of multi-angular remotely-sensed thermal data in such methods makes them susceptible to directional-anisotropy/thermal-radiation directionality effects that may result from the satellite's position, relative to the Sun, at overpass time. It is therefore important to have these effects accounted for to ensure realistic flux retrievals irrespective of sensor viewing position. At present, dual source models generally interpret surface temperature according to two sources, representing the soil surface and the vegetation. This may be insufficient to adequately represent the limiting temperature conditions that not only depend on the source type but also on their exposure to the Sun. Here, we present a modified version of the SPARSE (Soil Plant Atmosphere Remote Sensing Evapotranspiration) model, wherein the original SPARSE is modified to incorporate sunlit/shaded soil/vegetation elements and coupled with a radiative transfer model that links these four component emissions to out-of-canopy directional radiances as observed by remote sensors. An initial evaluation is carried out to check the model's capability in retrieving surface fluxes over diverse environments instrumented with in-situ thermo-radiometers. When run with nadir-acquired thermal data, both algorithms show no observable difference in their retrieval of total fluxes. We nonetheless show that by incorporating the solar direction and discriminating between sunlit and shaded elements, the partitioning of these overall fluxes between the soil and vegetation can be improved especially in water-stressed environments. We also test the sensitivity of flux and component temperature estimates to the viewing direction of the thermal sensor by using two sets of TIR data (nadir and oblique) acquired simultaneously to force the models and show that sensitivity to viewing direction is significantly reduced. This is an important aspect particularly when using high resolution spatial and temporal data from Earth observation missions that inherently have to consider a wide-range of viewing angles in their design.