L-band Data Assimilation for Improved Surface Modelling

Accurate land surface process modelling might be limited due to lack of reliable model input data. Key surface variables as land cover information or soil moisture conditions have been proven to be observable by remote sensing systems. The integration of remote sensing data into land surface process models might therefore help to improve their simulations results. Longer wavelength SAR data has a higher sensitivity to soil moisture content than higher frequency systems. Recent (ALOS) and planed (e.g. TerraSAR-L) SAR systems are therefore expected to provide valuable information about soil moisture dynamics. The present study investigates the potential to retrieve land cover information and geophysical parameters from L-band SAR data. The retrieval results are assimilated into a state-of-the-art land surface model to evaluate the merit of L-band SAR data assimilation.