Hydrological proxy derived from InSAR coherence in landslide characterization

Abstract

Quantifying landslide susceptibility saves lives, especially in populous areas exposed to wet climates. However, available hydrological data sets such as precipitation and soil moisture are usually from reanalysis with a few to tens of kilometers' coarse resolution compared to the dimensions of landslides. Here we aim to seek substitutes to characterize hydrological features with finer spacing for landslide susceptibility assessment encompassing the tectonically active California. We synergize remote sensing big data and derivatives including topographic characteristics, vegetation index, hydrological variables, land cover, and geological units in different machine learning architectures. Our results illuminate that the interferometric coherence derived from synthetic aperture radar (SAR) can be an effective hydrological proxy, providing enhanced resolution by three orders of magnitude to tens of meters and presenting satisfactory performance, with recalls >85 % and AUCs >90 % in our landslide susceptibility models. The consequent spatially continuous landslide susceptibility map further demonstrates the effectiveness of high-resolution SAR products in compensating for limitations in traditional hydrological data sets. The map and our inferred relationship with the mélange and the distance to faults improve our ability in landslide hazard mitigation.