Monitoring slow-moving landslides through PS-InSAR and antecedent precipitation index: a case study of Petacciato, Italy

Monitoring slow-moving landslides is critical for mitigating socio-economic risks. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) enables precise detection of ground deformation in landslide-prone areas. This study utilized COSMO-SkyMed PS-InSAR time-series data from both ascending and descending orbits to investigate the Petacciato landslide in Italy over the period2011–2022. To evaluate moisture-related triggering mechanisms, we incorporated the Antecedent Precipitation Index (API) as a proxy for soil moisture retention. Monthly API values were derived to assess the influence of cumulative rainfall on slope stability and potential landslide reactivation. The Sequential Turning Point Detection (STPD)algorithm was applied to identify significant trend reversals inground displacement. These turning points were then linked to critical API threshold days. Maps from 2011 to 2022 highlight key deformation events, especially in 2015, 2016, 2017, 2018, and 2019.A comparative analysis of STPD events and API trends over a two-month window yielded a correspondence rate of 38% for ascending and 52% for descending orbits. Additionally, Global Precipitation Measurement (GPM) data showed strong agreement with local rain-fall records (Pearson’s r ≥ 0:85), reinforcing the reliability of satellite-based precipitation data. Significant API peaks often preceded major displacement trend reversals, particularly during wetter months. For instance, a peak API value (≥ 90 mm) in March 2015 was followed by a marked ground displacement shift in April 2015. These results underscore the importance of integrating API with PS-InSAR timeseries to enhance early warning systems and inform risk mitigation strategies in landslide-vulnerable regions.