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Connectivity Approach for Detecting Unreliable DInSAR Ice Velocity Measurements

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Differential Synthetic Aperture Radar Interferometry (DInSAR) allows for retrieval of ice velocity measurements of high resolution and accuracy. One of the main error sources in DInSAR is the phase unwrapping… Click to show full abstract

Differential Synthetic Aperture Radar Interferometry (DInSAR) allows for retrieval of ice velocity measurements of high resolution and accuracy. One of the main error sources in DInSAR is the phase unwrapping procedure. Unwrapping errors may be caused by several processes, including shear stresses associated with large motion gradients, which lead to loss of interferometric coherence. In many cases, unwrapping errors reach magnitudes corresponding to velocities of tens or even hundreds of meters per year. Traditional DInSAR implementations include pixel masking based on coherence thresholding, however such a masking is not always sufficient. Consequently, the state-of-the-art for ice velocity retrievals involves either manual inspection of individual measurements or simply discarding measurements in regions where ice flow exceeds a pre-defined threshold. Here, we instead apply a masking based on thresholding of a pixel connectivity estimate with respect to a reference point, which aims to detect unwrapping errors based only on the estimated coherence pattern. The method is tested on both simulated and real Sentinel-1 data from the Greenland Ice Sheet and effectively detects the majority of unwrapping errors (recall of 0.84 for the best performing threshold), although with a relatively low precision (0.52 for the best performing threshold). Importantly, higher magnitude unwrapping errors are associated with lower connectivity values, meaning that undetected errors have a significantly lower magnitude (median of 1.7 m/y, corresponding to a single phase cycle, compared to 40.5 m/y with no masking).

Keywords: connectivity; ice velocity; ice; dinsar; unwrapping errors

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2022

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