Satellite-derived seasonal fluctuations in surface displacement and soil moisture: Implications for landslide activity

Seasonal surface fluctuations are commonly influenced by environmental variations, with both water presence and geological conditions playing key roles in surface deformation. These short-term signals often complicate the interpretation of time series data, particularly in complex and mountainous regions where in situ hydrological data such as groundwater levels, pore water pressure, and soil moisture are scarce or difficult to obtain. This study investigates two representative slow-moving landslides exhibiting seasonal variations by applying the small baseline subset (SBAS) method, one of the core approaches within the multitemporal InSAR (MTInSAR) framework. The SBAS-derived time series are further analyzed in combination with satellite-derived soil moisture data. Given the limited availability of high-resolution hydrological observations, satellite-derived soil moisture was adopted as a validated proxy to represent the average hydrological conditions over each landslide area. The results reveal notable patterns of seasonal surface fluctuations driven by hydrological variations, and demonstrate that their expression is further modulated by lithological conditions. Based on available data, we infer that in sedimentary rock areas, a high water storage coefficient causes hydrological loading to dominate seasonal surface displacement, resulting in a negative correlation. In contrast, pore water pressure plays a dominant role in metamorphic rock areas, leading to a positive correlation. This study demonstrates the potential of satellite-derived hydrological data to complement InSAR time series in regions with scarce in situ monitoring. These findings offer valuable and useful insights for a further understanding slow-moving landslide behaviors, particularly in distinguishing and identifying accelerated movement signals from seasonal fluctuations, and for improving slope failure early warning systems. • Seasonal fluctuations, driven by water and geology, complicate deformation analysis. • Satellite data offer an alternative to scarce in situ deformation and hydrology data. • Displacement and hydrology: negative in sedimentary rocks, positive in metamorphic. • Findings improve understanding of landslides for early warning system enhancement.