A synthetic aperture radar (SAR) system requires external absolute calibration so that radiometric measurements can be exploited in numerous scientific and commercial applications. Besides estimating a calibration factor, metrological standards also demand the derivation of a respective calibration uncertainty. This uncertainty is currently not systematically determined. Here for the first time it is proposed to use hierarchical modeling and Bayesian statistics as a consistent method for handling and analyzing the hierarchical data typically acquired during external calibration campaigns. Through the use of Markov chain Monte Carlo simulations, a joint posterior probability can be conveniently derived from measurement data despite the necessary grouping of data samples. The applicability of the method is demonstrated through a case study: The radar reflectivity of DLR's new C-band Kalibri transponder is derived through a series of RADARSAT-2 acquisitions and a comparison with reference point targets (corner reflectors). The systematic derivation of calibration uncertainties is seen as an important step toward traceable radiometric calibration of synthetic aperture radars.
The technological advancement of the synthetic aperture radar (SAR) principle leads to an innovative challenge for the calibration as well. In order to provide an active reference target for an accurate absolute radiometric calibration the knowledge of the target's backscattering characteristics is essential. For the recently developed DLR C-band transponder named "Kalibri" several strategies for an accurate determination of the radar cross section (RCS) have been analyzed. Based on a comparison with respect to accuracy and feasibility, several recommendations for the best transponder calibration strategy were established. The resulting RCS of the transponders retrieved from the most suitable measurement method is presented as well as a cross-validation to prove the plausibility of these results.
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