For numerous spatial applications, land use data are of central importance and have to be available in a spatial data infrastructure for regional modeling. This also counts for the research project TR32 which focuses on SVA modeling in a regional context. The land use data should be organized in a land use information system according to international data standards providing general metadata including information about data quality. Usually, land use data are available from official sources, but they lack the desired information detail for many purposes. For example, in official land use maps,
agricultural land use is generally differentiated between arable land, grassland, orchards and some special land use classes like paddy fields. For detailed (agro-)ecosystem modeling, this information resolution is rather poor. Here, disaggregated land use data which provide information about the major crops and crop rotations as well as management data like date of sowing, fertilization, irrigation, harvest etc. are needed. The analysis of multispectral, hyperspectral and/or radar data from satellite or airborne sensors is a standard method to retrieve such kind of information with remote sensing methodologies. By using a Multi-Data Approach (MDA), the retrieved information from remote sensing analysis is integrated into official land use data by GIS technologies to enhance both the information level (e. g. crop rotations) of existing land use data and the quality of the land use classification.