From 1-D to 4-D: Enhancing Landslide Monitoring through InSAR-Derived Multi-Dimensional Movement Fields

Landslides are common geological phenomena that occur worldwide. When triggered by external factors such as earthquakes or rainfall, internal changes like increased shear stress or reduced shear strength can lead to accelerated landslide motion. In recent years, the intensification of human activities and the increasing frequency of extreme weather events have further raised the likelihood of catastrophic landslide events. Monitoring landslides is a critical approach to mitigating the associated risks. Particularly, advancements in spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology have provided higher spatial resolution data, significantly advancing the study of landslide dynamics. However, due to geometric limitations of spaceborne InSAR, the technology typically retrieves only one-dimensional line-of-sight (LOS) displacement, restricting its broader applicability. In this study, we employed advanced techniques such as SPFS and KFI-4D to extract multi-dimensional deformation fields by integrating multi-source SAR observations. We successfully derived 3-D and 4-D movement fields for the Xinpu landslide in the Three Goreges Reservoir (TGR) region of China and the Hooskanaden landslide on the west coast of the United States. Based on these results, we further applied the laws of mass conservation, a one-dimensional pore-water diffusion model, and geodynamic methods to estimate landslide kinematic parameters, including landslide thickness, effective hydraulic diffusivity, and strain invariants. These findings offer deeper insights into landslide movement behaviors. Additionally, we explored the potential of utilizing next-generation SAR satellites, such as NISAR, to obtain multi-dimensional landslide movement fields. The results indicate that integrating left-looking SAR observations from platforms like NISAR can significantly improve the accuracy of InSAR-derived multi-dimensional deformation fields and expand their application scenarios in landslide studies.