A pixel-wise calculation of soil evaporative efficiency with thermal/optical remote sensing and meteorological reanalysis data for downscaling microwave soil moisture

Abstract

Soil evaporative efficiency (SEE) was estimated as the relative difference of soil temperature (Ts) to its maximum (Tsmax) and minimum (Tsmin) values at ‘minimum and maximum’ soil moisture (SM), i.e., SEE= (Tsmax−Ts)/(Tsmax−Tsmin). This thermal indicator of SM (SMI) has been proven effective in downscaling satellite microwave SM in some local areas. However, the Tsmax and Tsmin were usually determined in an empirical way which is not conducive to a wider range of applications or global downscaling. In addition to the Ts-based SMI (SEE), solo-LST (Land Surface Temperature)-based SMI and composite index of LST and optical SMIs have also been widely used in downscaling microwave SM. However, they have rarely been evaluated with spatially distributed SM at a similar spatial resolution, leading to a vague understanding of their pros and cons in the downscaling. In this study, we first implemented a pixel-wise and theoretically calculating of SEE with LST/FVC space and meteorological reanalysis data. Then, a critical evaluation with satellite, aircraft, and in-situ SM observations was made via the SMPAVEX15 and SMAPVEX16 aircraft-based field experiments. Results indicated that the thermal SMIs presented a better performance than the optical SMIs for depicting the variations of SM. Among the thermal SMIs, Ts-based SEE showed better performance than the solo-LST-based index and the composite index temperature-vegetation-soil moisture dryness index (TVMDI). The results demonstrate the validity and necessity of obtaining the Ts-based SEE for downscaling satellite microwave SM with thermal/optical data. The provided pixel-wise method was validated in this study for calculating the Ts-based SEE, which is suggested for future downscaling of satellite microwave SM with microwave and thermal/optical fusion. However, the disadvantages of cloud contamination, the decoupling effect, and uncertainties brought by complex meteorological conditions and reanalysis data urgently need more research efforts.