Assessing the potential of different satellite soil moisture products in landslide hazard assessment

With the development of remote sensing technology, satellite-based soil moisture estimates become more and available, potential using satellite products in landslide hazard assessment has been widely recognized. However, to our knowledge, there is a lack studies exploring performance difference various for such an application. Therefore, this study aims compare several state-of-the-art on their potentials applications. The selected include ESA CCI dataset, SMAP Level-3 (L3), enhanced Level-4 (L4) surface, root zone datasets. Specifically, completeness different datasets calculated assess applicability practical To investigate relationship between commonly used rainfall information predictions, correlation with antecedent cumulated also carried out. In addition, explore whether can provide valuable assessment, infiltration events are identified based time series moisture, significance event characteristics (such as duration, change, etc.) occurrence then investigated Bayesian analysis. This out landslide-prone area, Emilia-Romagna region northern Italy. Results show that L4 product does not have any missing values, beneficial continuous monitoring landslides. As rainfall, rational spatial distribution Pearson coefficients compared other datasets, which be better explained by slope TWI (topographic wetness index). analysis shows prior knowledge probability improved ‘SMAP moisture’-derived events, indicating its greater region.