Abstract:
The Soil Moisture and Ocean Salinity (SMOS) mission provides a new collection of surface soil moisture retrievals based on L-band microwave observations. Satellite-based data assimilation helps to constrain simulated surface soil moisture, but also other land surface model variables, like root-zone soil moisture. These corrections may contribute to improved weather and climate predictions, or smaller scale processes like flood event estimations.

An Ensemble Kalman filter is used to assimilate SMOS retrievals into the Catchment land surface model. Validation against in situ data network across the globe shows that SMOS observations contribute to an improved temporal variability in surface as well as root-zone soil moisture estimates. Issues related to the spatial scale mismatch and biases between observing and modelling systems, inherent to satellite-based data assimilation, will be discussed and illustrated.