Probability Assessment of Rainfall-Induced Landslides Based on Safety Factors Using Soil Moisture Estimation From SAR Images

Slope stability models developed based on the physical mechanism of landslides show the effectiveness in landslide probability assessment, while they have rarely been applied in the field of radar remote sensing. Inspired by the related work, this article proposes a new quantitative method for rainfall-induced landslide probability assessment based on safety factors (SFs) using soil moisture estimation from synthetic aperture radar (SAR) images. In order to combine slope stability models with SAR measurement, first, soil moisture that plays a vital role in slope stability models is estimated by SAR techniques from vegetated slope terrain. In this article, we propose a new SAR data processing model for potential landslide areas and a modified physical-based scattering model for short vegetation. The estimated results are qualitatively verified by the tropical rainfall measuring mission (TRMM) instrument and are quantitatively verified by the field investigation. Second, we study the water table level that plays another vital role in slope stability models and cannot be retrieved from SAR data. The analysis indicates that it can be treated as a constant in the case of unsaturated soil moisture. Combining with other geotechnical parameters that do not change with external circumstances, we simplify the slope stability model, of which effectiveness is tested by the visual interpretation. Finally, the SF maps are obtained by the simplified slope stability model using soil moisture estimated from the corresponding SAR images. The field investigation shows that all the observed landslides are located in the unstable areas, indirectly verifying the proposed method.

Automated summary

This article proposes a new quantitative method for rainfall-induced landslide probability assessment based on safety factors (SFs) using soil moisture estimation from synthetic aperture radar (SAR) images. The effectiveness of the method is tested qualitatively and quantitatively through instrument verification and field investigation, showing that observed landslides are located in the unstable areas, indirectly verifying the proposed method.