The synergistic use of optical and SAR data for applications in agriculture and precision farming is analyzed. Plant parameters derived from optical sensors have proven to be very valuable inputs for accurate crop growth modeling and biomass monitoring. Information on the temporal development of Leaf Area Index (LAI) and structural canopy changes, such as harvest, strongly support the simulation of plant development and yield formation in a realistic and spatially distributed manner. The accuracy of LAI retrieval, based on RapidEye data using radiative transfer simulations with SLC, has been successfully validated with in-situ measurements for wheat, maize and rapeseed. Thus, LAI derived from optical data covering many fields and observing the whole crop cycle served for analyzing the sensitivity of TerraSAR-X to LAI for wheat. A high correlation between LAI values and radar backscatter especially in VV polarization was observed on field basis. Derivation of LAI from SAR-data can successfully complement LAI derived from optical data and thus stabilize the necessary data sources for plant modeling, making the data less dependent on weather conditions. Additionally, a structural indicator for the determination of the harvest date was found in the VH/VV backscatter ratio.Index Termsplant parameter retrieval, LAI, yield estimation, X-band radar, SLC

Complex process-based land surface models require detailed input parameters. While the process-describing parameters mostly are well known from laboratory research, the spatial parameters, such as terrain, land use or soil maps, often are available at coarse spatial resolutions only. The mass and energy balance of the land surface is strongly determined by plant growth. Depending on the application of the model, especially the spatial distribution of growth influencing site characteristics, such as soil properties for example, therefore is of major importance. This study demonstrates, how Earth Observation data can be used to overcome the lack of spatial detail, applying the land surface model PROMET to a precision farming task, i.e. site specific cereal yield modelling on a farm in northeast Germany. Maps of photosynthetically active leaf area, generated from RapidEye and Landsat TM data, were assimilated and the model output was successfully validated against measured yield maps for the summer of 2010.