LSC-GInSAR: a GNSS-enhanced InSAR approach by using least squares collocation

Abstract

SUMMARY High quality Interferometric Synthetic Aperture Radar (InSAR) interferograms are essential for determining surface deformation from InSAR time-series. However, InSAR interferograms are usually polluted by spatially correlated errors (SCEs), especially the unmodelled atmospheric phase delays. To mitigate spatially correlated errors and improve the quality of InSAR interferograms, we propose a new approach to incorporate the Global Navigation Satellite System (GNSS) data from continuously operating reference stations for enhancing InSAR interferograms via modelling SCEs as signals and solving the signals together with the systematic parameters using least squares collocation (LSC), which is referred to as the LSC-GInSAR approach. Our improvement for the GInSAR method of Neely et al. can correct more SCEs. The Sentinel-1 data over the southern Central Valley of California, USA, are processed with our LSC-GInSAR approach, which is compared to the GInSAR approach. The performance of the LSC-GInSAR approach is evaluated by external GNSS displacements. The results show that the LSC-GInSAR approach can effectively mitigate medium-to-long-wavelength SCEs. The displacements resolved by LSC-GInSAR are more consistent with the cGNSS observations than those resolved by GInSAR, with an average root mean square improvement of 14.3 per cent. In addition, the LSC-GInSAR approach reduced the average standard deviations of all 276 InSAR interferograms from 14.2 to 11.0 mm compared to that of the GInSAR approach.