Combining Soil Moisture and MT-InSAR Data to Evaluate Regional Landslide Susceptibility in Weining, China

Landslide susceptibility maps (LSMs) play an important role in landslide hazard risk assessments, urban planning, and land resource management. While states of motion and dynamic factors are critical in the landslide formation process, these factors have not received due attention in existing LSM-generation research. In this study, we proposed a valuable method for dynamically updating and refining LSMs by combining soil moisture products with Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data. Based on a landslide inventory, we used time-series soil moisture data to construct an index system for evaluating landslide susceptibility. MT-InSAR technology was applied to invert the displacement time series. Furthermore, the surface deformation rate was projected in the direction of the steepest slope, and the data was resampled to a spatial resolution consistent with that of the LSM to update the generated LSM. The results showed that varying soil moisture conditions were accompanied by dynamic landslide susceptibility. A total of 22% of the analyzed pixels underwent significant susceptibility changes (either increases or decreases) following the updating and refining processes incorporating soil moisture and MT-InSAR compared to the LSMs derived based only on static factors. The relative landslide density index obtained based on actual landslides and the analyses of Dongfeng, Haila town, and Dajie township confirmed the improved slow landslide prediction reliability resulting from the reduction of the false alarm and omission rates.