Radio frequency interference (RFI) is a known issue in low-frequency radar remote sensing. In synthetic aperture radar (SAR) image processing, RFI can cause severe degradation of image quality, distortion of polarimetric signatures, and an increase of the SAR phase noise level. To address this issue, a processing system was developed that is capable of reliably detecting, characterizing, and mitigating RFI signatures in SAR observations. In addition to being the basis for image correction, the robust RFI-detection algorithms developed in this paper are used to retrieve a wealth of RFI-related information that allows for mapping, characterizing, and classifying RFI signatures across large spatial scales. The extracted RFI information is expected to be valuable input for SAR-system design, sensoroperations, and the development of effective RFI-mitigation strategies. The concepts of RFI detection, analysis, and mapping are outlined. Large-scale RFI mapping results are shown. In casestudies, the benefit of detailed RFI information for customized RFI filtering and sensor operations is exemplified.
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