Land Subsidence Detection in Penang Island Using PS-SBAS InSAR with Adaptive Machine Learning-Based Weighting

Land subsidence poses a significant threat to infrastructure stability and urban sustainability, particularly in rapidly developing coastal regions. In this study, land subsidence over Penang Island, Malaysia, was investigated by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques using both ascending and descending Sentinel-1 datasets. The combined PS-SBAS framework enables high-resolution and reliable deformation monitoring by exploiting the complementary advantages of the two approaches. To further improve deformation retrieval accuracy, an adaptive machine learning-based weighting strategy was incorporated into the InSAR time-series inversion process. Specifically, a data-driven model was employed to evaluate the reliability of interferometric observations using multiple quality indicators, enabling adaptive weighting of interferometric pairs and suppressing the influence of low-quality or noisy measurements. This strategy enhances the robustness and stability of deformation estimation without requiring additional external datasets. The results reveal spatially heterogeneous subsidence patterns across Penang Island, with pronounced deformation mainly concentrated in coastal and urbanized regions. Compared with conventional approaches, the proposed framework demonstrates improved temporal consistency and reduced sensitivity to noise, resulting in more reliable deformation time series. The findings provide valuable insights into regional subsidence dynamics and demonstrate the potential of the proposed framework for InSAR-based deformation monitoring in complex environments.