For accurate regional modelling of (agro-)ecosystems, up-to-date land use information is essential to assess the impact of the permanent changing vegetation cover of agricultural land on matter fluxes in the soil-vegetation-atmosphere (SVA) system. In this regard, officially available land use datasets are mostly inadequate, since they only provide generalised information concerning agricultural land use. In this contribution, we present our work for the year 2008 on the generation of multi temporal, disaggregated land use data with the goal to derive a crop rotation map for the years 2008–2010 for the study area of the research project CRC/TR 32. For this purpose, the Multi-Data Approach (MDA) was used to integrate multitemporal remote sensing classifications with additional spatial information by the means of expert knowledge-based production rules. Our results show that the information content of a land use dataset is considerably enhanced by combining crop type information of multiple observations during each growing season. For a sufficient temporal coverage, the usage of multiple sensors is generally inevitable. Thus, datasets of ASTER, Landsat TM & ETM+ as well as IRS-P6 were incorporated. In terms of classification accuracy our analysis yielded similar results with support vector machines (SVM) and the classical maximum likelihood classifier (MLC) for all sensors, with SVM being mostly only slightly better. For the refinement of land parcel boundaries and the reduction of misclassification, the incorporation of the 'field block' (FB) vector information was very effective. 'Field blocks', provided by the chamber of agriculture, are coherent agricultural areas with (relatively) permanent boundaries. As a result, a much more accurate differentiation of agricultural land and non-agricultural land was achieved. With the enhanced annual MDA land use data of the three consecutive years containing crop type information sufficient information is available for the derivation of crop rotation. Again, adapted knowledge-based production rules are used for this purpose.

Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes.

In every interdisciplinary, long-term research project it is essential to manage and archive all heterogeneous research data, produced by the project participants during the project funding. This has to include sustainable storage, description with metadata, easy and secure provision, back up, and visualisation of all data. To ensure the accurate description of all project data with corresponding metadata, the design and implementation of a metadata management system is a significant duty. Thus, the sustainable use and search of all research results during and after the end of the project is particularly dependent on the implementation of a metadata management system. Therefore, this paper will describe the practical experiences gained during the development of a scientific research data management system (called the TR32DB) including the corresponding metadata management system for the multidisciplinary research project Transregional Collaborative Research Centre 32 (CRC/TR32) 'Patterns in Soil-Vegetation-Atmosphere Systems'. The entire system was developed according to the requirements of the funding agency, the user and project requirements, as well as according to recent standards and principles. The TR32DB is basically a combination of data storage, database, and web-interface. The metadata management system was designed, realized, and implemented to describe and access all project data via accurate metadata. Since the quantity and sort of descriptive metadata depends on the kind of data, a user-friendly multi-level approach was chosen to cover these requirements. Thus, the self-developed CRC/TR32 metadata framework is designed. It is a combination of general, CRC/TR32 specific, as well as data type specific properties.

Regional process-based (agro-)ecosystem modelling depends mainly on data availability of land use, weather, soil, and agricultural management. While land use, weather, and soil data are available from official sources or can be captured with monitoring systems, management data are usually derived from official statistics for administrative units. For numerous spatial modeling approaches, these data are not satisfying. Especially for process-based agro-ecosystem modeling on regional scales, spatially disaggregated and land use dependent information on agricultural management is a must. Information about date of sowing, dates of fertilization, dates of weeding etc. are required as input parameters by such models. These models consider nitrogen (N)- and carbon (C)-matter fluxes but essential amounts of N-/C-input and N-/C-output are determined by crop management. Therefore, in this contribution a RS- and GIS-based approach for regional generation of management data is introduced. The approach is based on the Multi-data Approach (MDA) for enhanced land use/land cover mapping. The MDA is a combined RS and GIS approach. The retrieved information from multitemporal and multisensoral remote sensing analysis is integrated into official land use data to enhance both the information level of existing land use data and the quality of the land use classification. The workflow of the MDA to generate enhanced land use and land cover data consists basically of two components: (a) the methods and data of the remote sensing analysis and (b) the methods and data of the GIS analysis. The MDA results in disaggregated land use data which can be used to link crop management information about the major crops and especially crop rotations like date of sowing, fertilization, irrigation, harvest etc. to the derived land use classes. Consequently, depending on the land use, a distinct management is given in a spatial context on regional scale. In this contribution, three case studies of different regions in Germany will be presented: (i) the dairy farm region "Württembergisches Allgäu", (ii) the arable land region "Kraichgau", and (iii) the diverse Rur-Watershed in Western Germany. For each of the study regions, a different MDA-based approach for regionalizing agricultural management is applied and will be discussed.