This paper presents results of a study investigating the potential for improvement of a physically-based model approach, when the static input data is enhanced by dynamic remote sensing information. The model PROMET (PROcess of Radiation Mass and Energy Transfer), which is normally used to simulate the water and energy fluxes at the landscape level, was applied on a field scale to simulate crop growth and yield. The remote sensing input data was derived from hyperspectral images of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). PROMET was set up for a field scale model run for two test fields grown with winter wheat (Triticum aestivum L.) mapping the crop development of the seasons 2004 and 2005. During the model runs, information on the absorptive capacity of the leaves for two canopy layers (sunlit and shaded layer) was updated using remotely sensed chlorophyll measurements. The chlorophyll contents of these two vegetation layers were assessed using angular CHRIS data. Control data were acquired through field measurements, which were conducted throughout the growing periods of both years and also accompanying the satellite overpasses. The stand-alone model was able to reproduce the average development of the crop and yield reasonably well, but the spatial heterogeneity was severely underestimated and yield was overestimated by approximately 20%. The combination of remote sensing data with the model led to an improvement of the spatial heterogeneity of the crop development and yield. The use of ground truth data to improve the modeling accuracy can be made possible.