We analyzed a highly complex soilscape of fluvial sediments by a hierarchical expert system. Using (i) inquiries, (ii) relief analysis on basis of a DEM 5, and (iii) soils' apparent electrical conductivity (EM38) as a database, we first defined zones of identical pedogenic context. Next, multi-temporal remote sensing data of winter wheat were obtained by an airborne multi-spectral scanner (Daedalus-ATM), which gives radiometric information with a geometric (ground) resolution of 1 m(2) (pixel size). Leaf area index (LAI) was semi-physically modeled using red and near-infrared canopy reflectances and related to above-ground biomass. Further, the resulting spatial patterns of vegetation parameters were processed by image analysis methods, i.e. an opening-closing procedure using a circular element with a radius of 5 in. These coarser patterns of LAI and biomass, respectively, were interpreted as patterns of site quality within each zone of pedogenic context. By our multi-temporal approach we were able to distinguish between stationary and time-variant pattern. Combined with point calibration on basis of a 50-m raster we identified available water capacity (AWC) and O(2) deficiency due to stagnant water as the most important soil properties constituting site quality for plant growth. Our results will be used for precision agriculture practices in future.