To model the impact of the landcover on the climatic and hydrological cycles, an exact knowledge of two facts is necessary: the landuse and the amount of biomass. The study area for this work is the catchment of the Ammer river, which covers about 1200 km2 in the Bavarian Alpine Foreland. Optical remote sensing data are proved to provide good information sources to derive landuse classifications for large areas. But due to the fact that commonly used classification algorithms are solely based on the spectral information, this often leads to misclassifications, because different classes can show similar spectral signatures. To derive a sufficient landuse classification for the testsite purely from remote sensing data, shows up to be difficult. Due to the increasing cloudiness at the border of the alps, the use of multitemporal data is limited. Moreover, the diverse structure of the testsite limits the use of Bayes- theory based classification approach, non-spectral geographical ancillary data, such as climatic and soil data are integrated. Rules for influencing parameters were derived and taken into account in the classification procedure. The developed approach is based on the possibility theory and fuzzy subsets. The results are verified with a digital ground truth map and show a substantially increase of the classification accuracies. To calculate the evaporation, besides the landuse pattern the development of the vegetation cover is of importance. To monitor the vegetation dynamics, multitemporal optical data are not available. Therefore, ERS-SAR radar data are used for this task. Since grassland is the dominating agricultural landuse, investigations were made on the utilization of radar data for the determination of the temporal development of grassland biomass. It is shown that there is a correlation between the signal intensity and the vegetation height of meadows. Due to the fact that the height highly correlates with the biomass, the grassland biomass can be estimated in the testsite.