Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application

Abstract

Highlights: What are the main findings? A global analysis of 1000+ landslides revealed consistent patterns in SAR backscatter change. Empirical findings were integrated into a physical conceptual model linking SAR backscatter to landslide surface changes. What is the implication of the main finding? The conceptual model fills a key gap in understanding landslide signatures in SAR backscatter, enabling more reliable interpretation across diverse environments. It provides a foundation for advancing rapid landslide detection and automated methods, supporting disaster response and climate resilience in areas where optical satellite data are limited. Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response.