The Soil Moisture and Ocean Salinity (SMOS) mission provides global measurements of L-band passive microwave emission from the Earth's surface, along with derived estimates (or retrievals) of surface soil moisture. Either of these observational datasets can help constrain estimates of surface and root zone soil moisture from a land surface model through data assimilation. If the L-band brightness temperatures are assimilated, the modeling system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and temperature) into modeled L-band brightness temperatures.

In this presentation, we compare global surface soil moisture estimates from the NASA GEOS-5 modeling and assimilation system against SMOS soil moisture retrievals and assess each dataset against in situ measurements from available networks. Preliminary results indicate that surface soil moisture estimates from SMOS and the model exhibit consistent temporal and spatial variations, but a slightly different temporal variability. Furthermore, the skill of SMOS and model estimates in terms of anomaly variations is comparable when validated against the in situ observations.

We also discuss the calibration of the RTM and assess the resulting modeled L-band brightness temperatures against multi-angular SMOS observations. Preliminary results show that after the (climatological) calibration of roughness and vegetation parameters the RTM can provide modeled L-band brightness temperatures with global mean absolute bias of less than 10 K against SMOS observations, across multiple incidence angles. Sufficiently unbiased estimates of brightness temperatures are a necessary precondition for their successful use in a radiance-based soil moisture assimilation system.