To investigate the potentiality of a radar ground prediction radar satellite data (SAR) have been studied for the generation of clutter estimation models. The generation of clutter statistics as well as scrutinized analysis of specific dependencies and influences of backscatter processes and behaviour is the basis for statistical clutter prediction. Clutter predition makes an adjustment to the present situation necessary. This demands for certain calibration techniques, e.g. online-calibration.
Radar raw data from the orbital SAR-systems X-SAR, Radarsat and ERS-2 found the data base for radar backscatter calculation. In combination with a digital elevation model and the ground truth of the investigated area backscatter maps representing the backscatter coefficient have been processes. The ground truth categories have to be selected in a way that each category is linked to a certain homogeneous backscatter category. Each backscatter category is represented by its probability density function or distribution function, respectively. In case of an elaborated ground truth concerning geometrical dimensions, location, invariance and homogeneity the backscatter statistics will be stable.
Parametrical dependencies of different numerous backscatter processes have been proved by theoretical considerations and practical analysis of existing satellite radar data. It has been shown that backscatter behaviour is due to statistical regularities and laws that make a clutter prediction feasible and reliable.