Generating continuous information products on land use and the intensity of agricultural production from high resolution satellite data

For a sustainable and efficient land management, spatially distributed and up-to-date information on the land surface is of central importance. A continuous flow of land management information is the basis to improve decisions on use, cultivation intensity and allocation of resources (e.g. water for irrigation). Remote sensing is in a unique position to contribute to this task as it is globally available and provides specific information about current crop status. The M⁴Land concept is designed to derive information products for a sustainable management of the land surface. In this paper, the methodology and the results for the autonomous development of three products, i.e. land use, crop cycle and intensity of agricultural use, is presented for two test sites and for several years. In the M⁴Land system, a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal and multi-sensoral satellite images.