2. Methods
Our InSAR/GNSS integration approach is an extension of standard published methods (e.g., Tong et al. , 2013; Weiss et al.,2020) although in addition to secular velocity, we also calculate line-of-sight (LOS) displacement time series [e.g., Neely et al., 2019]. The GNSS weekly displacements were derived by means of a median filter [Klein et al. , 2019] of daily time series estimated as part of a NASA MEaSUREs project [Bock et al. , 2016]. Moreover, we use the secular velocity from a GNSS-only interseismic model [Zeng & Shen , 2017] to create semi-vertical vector InSAR time series from the LOS displacements. A brief description of the method follows:
  1. Gather Sentinel-1 Terrain Observations with Progressive Scans (TOPS) data from multiple tracks and re-assemble into common re-defined frames, typically 250 km by 500 km.
  2. Geometrically co-register all SAR acquisitions and construct all interferograms with perpendicular baseline < 150 m and temporal separation < 90 days [Xu et al. , 2017;Sandwell et al. , 2016b].
  3. Mask bodies of water and areas of persistent low coherence regions and replace them with nearest-neighbors [Shanker & Zebker , 2009]. This step improves the phase unwrapping accuracy which is done with Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping (SNAPHU) [Chen & Zebker , 2002].
  4. Perform elevation dependent atmospheric phase correction [Elliott et al. , 2008]. Compute the difference between the remaining InSAR phase and projected GNSS weekly solutions [Klein et al. , 2019], interpolate this difference, filter at 80-km wavelength and remove this difference from each interferogram.
  5. Construct time-series using a coherence-based SBAS approach integrated with atmospheric phase correction using common-scene stacking [Tymofyeyeva & Fialko , 2015; Tong & Schimdt , 2016;Xu et al. , 2017].
  6. Subtract a horizontal GNSS velocity model [e.g., Zeng & Shen , 2017] from the time-series to create semi-vertical InSAR time-series.
Since Sentinel-1 TOPS data is acquired under burst acquisition mode and there is occasional inconsistency in data coverage, especially in the early days of the mission, the frame boundaries in step 1) are a compromise between spatial coverage and acquisition numbers. The total number of interferograms generated here is 5230, connecting acquisitions from 910 dates over 9 tracks. Enhanced spectral diversity [Prats-Iraola et al. , 2012] is not performed in step 2, since it will remove an expected tectonic signal that will eventually supply a third InSAR component [Li et al., 2021]. The estimated mis-registration could be up to 2/1000 pixel/yr along SAFS and spread across the scenes, where a constant shift from ESD is inadequate, while the performance of bivariate approach [Wang et al., 2017] is yet to be evaluated. Moreover, the common scene stacking time series approach (step 5) is capable of mitigating along-track orbital errors by absorbing burst discontinuities, that are random in time, into atmospheric phase screens [Xu et al. , 2017]. The nearest-neighbor interpolation in step 3) is implemented so phases are allowed to vary properly along very long coastlines, and stay connected through snowy Sierras and heavy vegetations in northern California. Elevation dependency in step 4) is assumed as a bivariate quadratic polynomial thus spatial variations in atmospheric contribution are accounted for. The relatively large, 80-km wavelength filter, that is applied to the GNSS correction for each interferogram, is sufficient to absorb the large-scale atmospheric and orbital errors affecting the InSAR displacements and also accommodate areas such as the Central Valley having sparse GNSS coverage. A remove-restore approach [Tong et al. , 2013] using a purely horizontal secular velocity model is not used because it is incompatible with the significant vertical signal in Central Valley. Not only is this vertical deformation distributed over hundreds of kilometers, but it also has a sharp transition around the edges of the sedimentary basins. The interpolation of discrepancies between GNSS and InSAR is adopted here, taking advantage that though the vertical deformation changes dramatically over a large area, the differences from the two type of observations maybe systematic and vary slow enough in space to be well evaluated. When the final velocity is computed, the first and last four records are not used, mainly because the atmospheric correction approach gains less constraints when acquisitions are non-evenly distributed.