An Optimized Method for Slope Deformation Monitoring via Parallel-Track InSAR Data Fusion

Abstract. This study addresses geometric distortion in SAR imagery caused by complex topography in reservoir areas, which adversely affects the accuracy of InSAR monitoring. A multi-orbit data fusion technique is proposed, integrating the R-Index(im) with an improved Geometric Distortion (GD) Index. Using the study landslide mass at Danjiangkou Reservoir as a case study, the research leverages Sentinel-1A satellite data. The GD index model is optimized by incorporating a shadow effect coefficient (Sh). Combined with the R-Index(im) 's capability for distortion-region identification, an adaptive weighted fusion model is established. Empirical analysis demonstrates that this approach effectively corrects deformation monitoring biases caused by geometric distortion. Compared to single-orbit data, the fusion results reduce deformation rate errors by 0.02 mm/year and 0.36 mm/year, while expanding the monitoring coverage by 32 %. Verification against GNSS ground-truth data confirms that this method significantly enhances the accuracy and reliability of InSAR monitoring in complex terrain, offering an effective technical solution for reservoir landslide early warning.