Agricultural production becomes increasingly important as the world demand increases. Monitoring of crop species and soil moisture greatly assists agricultural management. The current study results in a software model, which conveniently extracts agricultural land cover from radar imagery. Based on the same data, soil-moisture, a key parameter of plant growth, can be assessed. Synthetic Aperture Radar data (TerraSAR-X and ALOS PALSAR) have been used. One principal key to information extraction is multi-direction/multi-distance texture analysis on a plot basis. Resulting sets of texture measures are stored in attribute tables. The subsequent classification imbeds elaborate approaches of the R Environment . Within the classification module various approaches such as SVM and ANN are available, in addition to different feature selection methods. The model is fully integrated to an ArcGIS user interface. On the other hand, from the TerraSAR data soil-moisture mapping has been performed. This important parameter was calculated for the study area using linear regression equations related to corresponding land-cover types. All results have been verified by synchronous field measurements.