Joint Spatio-Temporal Correction for Both Turbulent and Vertically Stratified Delays in Time Series SAR Interferometry

Over the past several decades, synthetic aperture radar interferometry (InSAR) has revolutionized surface deformation mapping from space. However, tropospheric artifacts-manifested as turbulent delays (TDs) and vertically stratified delays (VSDs)-remain a critical bottleneck limiting the accuracy of InSAR measurements, as a consequence of the spatiotemporal dynamics of atmospheric properties (e.g., temperature, humidity and pressure). Current tropospheric correction methods primarily target either TDs or VSDs in isolation, which significantly impedes the complete mitigation of tropospheric noise in interferograms. To address this limitation, in this paper, a joint spatio-temporal correction (JSTC) method was proposed for mitigating both TDs and VSDs. In this method, first a dual-scale temporal low-pass filtering (DTLF) strategy is employed to suppress TDs, leveraging their high-frequency characteristics in the temporal domain. Improved quadtree segmentation is subsequently applied to account for the spatial heterogeneity of VSDs. The JSTC method is validated using both simulated datasets and real InSAR observations in the rugged Hengduan Mountains of southwestern China. The experimental results demonstrate that the JSTC outperforms conventional approaches such as GACOS, Global Linear (GL) and DTLF, reducing the phase standard deviation (STD) by 83.82%, 62.07% and 84.40%, respectively. This study provides a robust solution for increasing the reliability of InSAR-based deformation monitoring under complex atmospheric conditions.