ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been designed to contribute to furthering the knowledge of the Earth's water cycle. The terrestrial part of this mission, focusing on the spatial and temporal dynamics of soil moisture, will have a large impact on the understanding of climate-related processes and will help to improve the forecasts of climate change, weather and extreme-events.

MIRAS, the radiometer system onboard SMOS, records brightness temperatures which cannot be implemented into the relevant climate or weather forecast models directly. ESA provides a SMOS Level-2 product processed by an operational routine that includes a radiative transfer model, which in turn needs further information about the vegetation cover and surface conditions in order to generate a high accuracy soil moisture product. So far, this additional information is just basically parameterised and does not adequately consider the seasonal variability of the vegetation.

The accuracy of this soil moisture product can be enhanced by data assimilation techniques. A coupled model system containing a hydrometeorology model as well as a radiative transfer model will be integrated into a data assimilation framework using a sequential Monte Carlo algorithm, which is able to update both model states (e.g. soil moisture or brightness temperatures) and model parameters (e.g. surface roughness, vegetation opacity). This study will focus on the estimation of parameters for the radiative transfer model and their spatiotemporal dynamics by assimilating SMOS brightness temperature and in situ soil moisture observations.

Enhanced SMOS data products taking into account the uncertainty of the data will have a high impact on the scientific outcome produced by SMOS-data users. Strategies for an operational application of the proposed approach will be formulated. The approach of using additional world-wide available in situ observations during the processing may enhance also the accuracy of Level-2+ products from other ESA missions.