Use of a Kalman filter for the retrieval of surface BRDF coefficients with a time-evolving model based on the ECOCLIMAP land cover classification

The goal of this study is to demonstrate the asset in using a Kalman filter to improve the spatial coherence and time consistency of surface Bidirectional Reflectance Distribution Function (BRDF) and albedo retrievals from moderate resolution sensor data sets. For this purpose, we use a simple surface model describing BRDF seasonal evolution for the land cover classes of the ECOCLIMAP database. The application of temporal composition windows used so far for BRDF retrieval is limited in regions characterized by a high frequency of cloud coverage, which induces a lot of gaps in the temporal series. Instead, the present method ensures a continuous production of surface BRDF parameters thanks to the Kalman filter recursive data processing. An application of the method is performed with SPOT/VEGETATION data over the western Africa equatorial region for the year 2003. Compared to presently available products from VEGETATION and MODIS instruments, this new approach allows to fill the gaps and improves the retrieved parameters time consistency. Another interesting possibility of the Kalman filter is the production of surface biophysical variables in quasi-real-time for applications that require a frequent update of the surface parameters.