2022
DOI: 10.1109/tgrs.2021.3108751
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InSAR Phase Unwrapping Error Correction for Rapid Repeat Measurements of Water Level Change in Wetlands

Abstract: Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA's airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very … Show more

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Cited by 18 publications
(17 citation statements)
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“…The final mean inter-annual displacement velocity and its standard error were determined by fitting a linear deformation model to the movement time series. We performed quality controls on the data by manually masking areas where the results were deemed unreliable due to unwrapping errors, which manifest as a spatial discontinuity in displacement by integer multiples of half the radar wavelength (Oliver-Cabrera et al, 2022). We noted that errors and subsequent masked areas increased significantly with the inclusion of the 2019 image; thus, we concentrate our analyses here on averaged displacements deriving from the 2015 to 2017 imagery only, and we document data incorporating the 2019 data in Supporting Information S1.…”
Section: Insarmentioning
confidence: 99%
“…The final mean inter-annual displacement velocity and its standard error were determined by fitting a linear deformation model to the movement time series. We performed quality controls on the data by manually masking areas where the results were deemed unreliable due to unwrapping errors, which manifest as a spatial discontinuity in displacement by integer multiples of half the radar wavelength (Oliver-Cabrera et al, 2022). We noted that errors and subsequent masked areas increased significantly with the inclusion of the 2019 image; thus, we concentrate our analyses here on averaged displacements deriving from the 2015 to 2017 imagery only, and we document data incorporating the 2019 data in Supporting Information S1.…”
Section: Insarmentioning
confidence: 99%
“…Data preprocessing includes the calculation of time and space baselines between all Sentinel-1A image pairs. After registration and clipping, the DEM data is used to complete image registration, and the relative combination that satisfies a given threshold is selected to produce a differential interferogram set 75 . This study uses a 30 m resolution SRTM DEM to generate interferograms.…”
Section: Methodsmentioning
confidence: 99%
“…However, its effectivity will be highly dependent on the repeat pass schedule of the observing sensor as well as the type of vegetation observed. If the vegetation has a soft stem, its scattering properties will change rapidly and data acquisition will require a very short repeat observation time, whereas woody vegetation may allow for a longer temporal baseline in the SAR acquisition pattern (Oliver‐Cabrera et al., 2021). Note that the method cannot be applied to submerged vegetation, for example, seagrass.…”
Section: Methodsmentioning
confidence: 99%