Large-scale Sentinel-1 InSAR analysis over Norway: Influence of atmospheric water vapour distribution

Variations in the distribution of atmospheric water vapor are the most significant source of noise for large-scale interferometry analysis using C-band Sentinel-1 data. The propagated noise depending on partial pressure of dry air, temperature and partial pressure of water vapour, decreases the accuracy of interferometric synthetic aperture radar (InSAR) measurements in identifying and interpreting slow deformation signals. In this study, we evaluate the use of zenith total delay (ZTD) information derived from around 200 permanent global navigation satellite system (GNSS) stations with the separation between adjacent stations from around 13 km to 70 km, for correcting country-scale Sentinel-1 interferograms in Norway. The GNSS data was provided by the Norwegian mapping authorities. Several stochastic and deterministic techniques including linear interpolation, machine learning techniques like linear regression, support vector machines (SVM), and Gaussian processes (GPs), canonical correlation analysis (CCA), as well as the Archimedean Copula-based analysis are investigated for downscaling of GNSS and InSAR data in both temporal and spatial resolution. First, we derive the dependence structure between GNSS and InSAR atmospheric components. Then, we simulate the high temporal and spatial atmospheric components based on the captured relationship between GNSS and InSAR time series. Next, the generated solutions are compared with the correction derived from ERA-Interim to assess the performance of each method in characterizing the atmospheric component in large-scale Sentinel-1 interferograms. We utilize 14 SLC images of Sentinel-1 acquired in interferometric wide (IW) swath mode covering May–November 2016 from a descending track. Mainland Norway is fully covered by 27 frames of Sentinel-1 images acquired in a descending orbit along five tracks. We used Gamma software to consecutive frames of the SAR images along each acquisition track and for the interferometric proceeding. A network of small baselines interferograms have been designed and the created interferograms are then corrected based on the downscaled ZTD data. The results are presented and the derived ground surface displacement time-series are compared with the GNSS displacement time-series for validation.