Sequential Assimilation ofERS-1SAR Data into a Coupled Land Surface–Hydrological Model Using an Extended Kalman Filter

A first attempt to sequentially assimilate European Space Agency (ESA) Remote Sensing Satellite (ERS) synthetic aperture radar (SAR) estimations of surface soil moisture in the production scheme of a lumped rainfall–runoff model has been conducted. The methodology developed is based on the use of an extended Kalman filter to assimilate the SAR retrievals in a land surface scheme (a two-layer hydrological model). This study was performed in the Orgeval agricultural river basin (104 km²), a subcatchment of the Marne River, 70 km east of Paris, France. Assimilation was tested over a 2-yr period (1996 and 1997), corresponding to 25 SAR measurements. The improvements observed in simulating flood events demonstrate the potential of sequential assimilation techniques for monitoring surface functioning models with remote sensing data. It was demonstrated that the method could correct for some errors or uncertainties in the input data (precipitation and evapotranspiration), provided that these errors are not greater than 10%. The overall agreement between uncertainties predicted through the extended Kalman filter scheme compared to uncertainties obtained through the ensemble technique reaffirms the validity of the extended Kalman filter scheme but also demonstrates its limits. Questions are raised concerning the determination of sequential model errors.