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GInSAR: A cGPS correction for enhanced InSAR time series

TitleGInSAR: A cGPS correction for enhanced InSAR time series
Publication TypeJournal Article
Year of Publication2020
AuthorsNeely W.R, Borsa A.A, Silverii F.
Date Published2020/01
Type of ArticleArticle
ISBN Number0196-2892
Accession NumberWOS:000507307800010
Keywords& Photographic Technology; aquifer-system; deformation; Engineering; Geochemistry & Geophysics; global; global positioning system; GPS; Imaging Science; interferometry; interferometry (InSAR); interseismic strain accumulation; land subsidence; motion; Orbits; Positioning System (GPS); Remote sensing; san-andreas fault; strain; surface; Surface treatment; Synthetic aperture radar; terrain observation; Time series; time series analysis; topography

Earth surface displacements from interferometric synthetic aperture radar (InSAR) have long been used to study deformation from a wide range of geophysical processes. Whereas deformation rates can be robustly estimated from InSAR by averaging many individual deformation observations, noise in these observations has limited their utility for generating deformation time series. In this article, we introduce a novel combination of InSAR and Global Positioning System (GPS) data that align InSAR displacements to an absolute reference and reduces long-wavelength spatial errors prior to InSAR time series construction. We test our GInSAR (GPS-enhanced InSAR) methodology on Sentinel-1 data over the southern Central Valley, CA, USA, comparing GInSAR displacement velocities and time series with those from three other referencing techniques. We find that the GInSAR approach outperforms alternative methods, yielding mm-level displacement differences with respect to collocated cGPS. By contrast, other referencing methods can overestimate peak subsidence velocities in the Central Valley by upwards of 10, deviate by tens of millimeters relative to cGPS validation time series, and contain spatial biases absent in the GInSAR methodology. We also present a modification to the widely used small baseline subset (SBAS) technique for time series estimation, whereby we use a temporal connectedness constraint to regularize the mathematical inversion and increase the number of InSAR pixels with valid time series estimates.

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