Ultra-Long-Term Time-Series Subsidence Estimation for Urban Area Based on Combined Interferometric Subset Stacking and Data Fusion Algorithm (ISSDF)

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring.