The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97

An Ensemble Kalman filter (EnKF) is used to assimilate airborne measurements of 1.4 GHz surface brightness temperature (TB) acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97) into the TOPMODEL-based Land–Atmosphere Transfer Scheme (TOPLATS). In this way, the potential of using EnKF-assimilated remote measurements of TB to compensate land surface model predictions for errors arising from a climatological description of rainfall is assessed. The use of a real remotely sensed data source allows for a more complete examination of the challenges faced in implementing assimilation strategies than previous studies where observations were synthetically generated. Results demonstrate that the EnKF is an effective and computationally competitive strategy for the assimilation of remotely sensed TB measurements into land surface models. The EnKF is capable of extracting spatial and temporal trends in root-zone (40 cm) soil water content from TB measurements based solely on surface (5 cm) conditions. The accuracy of surface state and flux predictions made with the EnKF, ESTAR TB measurements, and climatological rainfall data within the Central Facility site during SGP97 are shown to be superior to predictions derived from open loop modeling driven by sparse temporal sampling of rainfall at frequencies consistent with expectations of future missions designed to measure rainfall from space (6–10 observations per day). Specific assimilation challenges posed by inadequacies in land surface model physics and spatial support contrasts between model predictions and sensor retrievals are discussed.