As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. Nevertheless, our method enabled satisfying crop mapping results. In most years, the remote sensing data basis was highly fragmented. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling.
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