Remote sensing data assimilation for regional crop growth modelling in the region of Bonn (Germany)

The study investigates the possibilities to improve the performance of CERES-Wheat crop growth model by assimilating information derived by optical and SAR Earth observation data. Biophysical parameter retrieval was done with the water cloud model for SAR data and the CLAIR model was applied to multispectral imagery. The CERES -Wheat model was calibrated using ground truth information. The re-initialization method with an adjustable planting date was selected as assimilation strategy. Modelling results generally improved by using all different kind of remote sensing data. However, best results were achieved by using information of the optical sensors only and not by a synergetic time series of all available data.