Assimilation of Hyperspectral Multi-directional Chris-data in a Coupled Radiative Transfer and Crop Growth Model

Spatially distributed information on canopy parameters is an important input to precision agriculture. Remote sensing data, and especially high-resolution hyperspectral data of sensors such as the multi-directional satellite-sensor CHRIS or the airborne spectrometer AVIS provide such information. In order to derive the canopy parameters leaf area index and fraction of mature material as well as yield maps for winter wheat an approach using a coupling between the radiative transfer model SLC and the crop-growth model PROMET-V is introduced. The multi-angular feature of CHRIS was used to derive information on the development of canopy structure of wheat crops during maturing. This data was used for a better derivation of the canopy parameters leaf area index and fraction of senescent material. The canopy parameters thus derived were assimilated into the crop growth model as a spatially distributed update on canopy development to model the intra-field distribution of yield. The results were validated against ground truth.