Application of SMOS Soil Moisture and Brightness Temperature at High Resolution With a Bias Correction Operator

The assimilation of soil moisture and brightness temperature (TB) are expected to improve the modeling of land surface processes, but are only available at a resolution that is far coarser than the scale of many hydrological processes. Due to systematic differences between model states and satellite observations, a bias correction operator is a necessary step in land data assimilation schemes and was evaluated as a method to disaggregate coarse-scale satellite observations to fine-scale model grid cells (${\sim}\text{800}\;\text {m}$). This was done by coupling the Modlisation Environmentale Communautaire-Surface Hydrology (MESH) Hydrological Land-Surface Scheme to the Community Microwave Emissions Model (CMEM) to simulate soil moisture and TB. By comparison, MESH-CMEM was found to be in good agreement with observations from the Soil Moisture and Ocean Salinity (SMOS) satellite at the scale of SMOS data products (${\text R} \approx \text{0.55}$), with simulated TB being better correlated than soil moisture retrievals. Following bias correction, TB and soil moisture retrievals at 800-m resolution had comparable performance to coarse-resolution SMOS data. Bias correction of TB was more reliable than soil moisture. These findings indicate that both TB and soil moisture retrievals can be assimilated in a land surface model at moderate-to-high resolution with a simple observation operator.