ID-GInSAR: an improved methodology for integrating GNSS to enhance InSAR-based deformation monitoring

SUMMARY This study presents an enhanced method for integrating Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR), referred to as iterative decomposition-based GNSS-enhanced InSAR (ID-GInSAR), to address both spatially correlated components (SCCs) and topographically correlated components (TCCs) in interferogram errors. While traditional GInSAR (GNSS-enhanced InSAR) is effective in mitigating long-wavelength SCCs, it often overlooks TCCs, which are particularly significant in regions with steep topographic gradients. The proposed ID-GInSAR approach employs an iterative decomposition process to decouple and independently model SCCs and TCCs, utilizing a combination of exponential and statistical models. The method is validated using Sentinel-1 SAR and GNSS data from California’s southern Central Valley. Results demonstrate that ID-GInSAR significantly lowers noise in interferograms, enhances the robustness of displacement time-series and improves the accuracy of co-seismic deformation and velocity field estimates. Specifically, ID-GInSAR reduces the root mean square (RMS) between GNSS and InSAR by up to 55 per cent in individual interferograms and by an average of 30.4 per cent in displacement time-series compared to traditional GInSAR methods. Furthermore, ID-GInSAR effectively highlights subtle transient deformation, such as coseismic offsets, and provides more robust velocity fields over shorter time spans (less than three years). Finally, we compare our method with other approaches, including Remove/Filter/Restore (RFR) and GACOS, and discuss their applicability scenarios. Collectively, ID-GInSAR provides an alternative integration method for regions with complex topography where ground-based GNSS observations are available.