Robust Stacking InSAR: Mitigating DEM Errors for Precise Deformation Rate Retrieval

Interferometric synthetic aperture radar (InSAR) has become an essential tool for monitoring surface deformation with high precision and wide spatial coverage. Among various InSAR techniques, the stacking InSAR approach is widely used for geological hazard assessments due to its computational efficiency and robustness against decorrelation noise. However, digital elevation model (DEM) errors remain a significant challenge, introducing spurious deformation signals and degrading deformation rate estimates. We present here a rigorous analysis of the impact of DEM errors on stacking InSAR-derived deformation rates and introduce an enhanced method that effectively mitigates these errors. Unlike conventional correction techniques that require explicit DEM error estimation, the proposed method leverages perpendicular baseline averaging, eliminating DEM-induced biases while maintaining computational simplicity. The method is validated using both simulated and real Sentinel-1A datasets from two tracks, with results compared against conventional stacking and small baseline subset (SBAS) approaches. The findings demonstrate that the proposed method significantly improves deformation rate estimation by suppressing DEM-induced artifacts, thereby enhancing the reliability of InSAR applications in geological hazard monitoring and deformation assessment.