Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets

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Abstract

Synthetic aperture radar (SAR) radiometric calibration is an essential step in inferring meaningful physical information about the target. The majority of current calibration processes are directly based on artificial calibrators, but the number of calibrators is always restricted. Cross calibration, which has been widely used for optical and meteorological satellites, performs calibration by another calibrated satellite. Distributed targets are used as the calibration reference, allowing for frequent calibration. However, the potential of introducing cross calibration to SAR satellites has not been confirmed, and two issues must be resolved: 1) the number of calibration targets available for cross calibration is limited to a few known distributed targets; 2) the imaging parameters of calibrated and uncalibrated SARs are different, which may cause errors when cross calibration is applied. In this article, a method for selecting stable distributed targets using time-series stability analysis was presented first. The saline-alkali land and urban areas were selected as the calibration targets with low and high backscattering, respectively. Second, different stable pixel extraction methods were adopted based on the characteristics of different targets. Third, the Oh model was used to correct the scattering difference caused by the incidence angle difference, which greatly improved the accuracy of the backscattering coefficients of the calibration targets. The cross calibration experiments on the same series of satellite (Sentinel-1 A/B) data revealed that the accuracy of cross calibration was comparable to the accuracy of the artificial calibrator-based method, with a difference of less than 0.48 dB (1).

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APA

Zhou, Y., Zhuang, L., Duan, J., Zhang, F., & Hong, W. (2022). Synthetic Aperture Radar Radiometric Cross Calibration Based on Distributed Targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 9599–9612. https://doi.org/10.1109/JSTARS.2022.3217285

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