Leveraging Multi-Primary PS-InSAR Configurations for the Robust Estimation of Coastal Subsidence

Abstract

Interferometric synthetic aperture radar (InSAR) is a key technique used to constrain contributions of diverse processes to coastal subsidence, also known as vertical land motion (VLM). However, coastal environments can pose major challenges for InSAR due to natural disturbances that degrade interferogram quality. We describe a new multi-primary pairing strategy for persistent scatterer InSAR (PS-InSAR) to estimate subsidence in challenging coastal environments. Our method retains only consistent PS candidates across multi-primary substacks and solves for redundant velocity observations using SVD-based inversion, similar to the conventional small baseline subset (SBAS) method. Through simulations and a case study comparing with single-primary PS-InSAR and conventional SBAS techniques, we show that our pairing strategy reduces temporal and spatial uncertainty in subsidence estimates in the presence of strong but temporary decorrelation loss, even with increased distance from the reference point. Moreover, our method visibly dampens time-series variation and decreases standard error in our time-series fit by nearly 2x in our case study. Thus, we find that implementing a multi-primary PS-InSAR configuration is a simple method of increasing the robustness of VLM estimates in challenging coastal environments.