Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at $0.1^{\circ }\times 0.1^{\circ }$ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ($r\ge 0.7$) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures.

The growing availability of multi-sensor Persistent Scatterer (PS) data offers high-precision, multi-temporal measurements for monitoring ground displacements across large areas. The ability to detect ground deformation phenomena is largely determined by PS density, influenced by the sensor resolution and site-specific characteristics. This research proposes a robust data fusion method to integrate multi-band/ multi-sensor PS products, prioritizing spatial dimensions to enhance coverage and exploit complementary information from diverse sensors. The method employs a weighted least squares approach with an adaptive weighting scheme to generate synthetic measurement points that synthesize deformations along the East-West and Up-Down components, effectively addressing challenges posed by sparse and unevenly distributed data. The methodology, validated through Sentinel-1 (S1) and COSMO-SkyMed (CSK) data integration, employs quantitative metrics and field observations to assess the reliability of fusion predictions. Statistical analysis demonstrates superior performance, with R-squared values of 0.950 for Up-Down and 0.868 for East-West components. Spatial coverage expands from approximately 10$\%$ (S1) and 22$\%$ (CSK) to 67$\%$ of the total area of interest. The enhanced detection capabilities enable comprehensive monitoring of ground deformation processes, uncovering patterns otherwise undetectable through single-sensor analysis and providing crucial information for hazard assessment.

Persistent Scatterer Interferometry (PSI) is a powerful multitemporal A-DInSAR (Advanced Differential Synthetic Aperture Radar Interferometry) technique widely used for monitoring and measuring Earth’s surface displacements over large areas with sub-centimetric precision. The capability to detect ground deformation processes relies on the available PSI spatial density, strictly related to the resolution of the considered sensor and the presence of stable natural and artificial reflectors. A new data fusion approach, developed as part of the “MUSAR” project funded by ASI (Italian Space Agency), integrates multi-band SAR sensors to improve data coverage of PSI data by synthesizing multi-sensor displacement information. The integration of multi-mission PSI generates synthetic measurement points, named Ground Deformation Markers (GD-Markers), featuring vertical (Up-Down) and horizontal (Est-West) components of the displacements. The fusion of PSI data extracted by C-band Sentinel-1 images from the Copernicus initiative and the COSMO-SkyMed constellation in the X-band from ASI contributed to creating a dataset with high information content.Each GD-Markers cluster with displacement measurements identifies a specific deformation process in the region of interest. After selecting the relevant cluster of points, the deformation processes were classified into different categories (e.g., landslide, subsidence) to improve their understanding and evaluation for mitigating natural-related hazards. This study aimed to develop a machine learning-based classification system, starting from GD-Markers point clouds, which support the automatization of ground displacement identification and characterization. The synthetic points were characterized as individual entities or point clouds, formed by a discrete cluster of points in space, to evaluate the advantage of treating each point independently or incorporating local neighborhood information. The structured point data were analyzed using a supervised Random Forest (RF) approach to evaluate the performance of point cloud classification and categorization for identifying the best initial setting. Each point was assigned a label representing a deformation process in point cloud classification, while one label is provided for the entire point cloud dataset with categorization.Comparing models’ performances allowed the definition of the best possible approach for classifying the deformation processes observed by GD-Markers point clouds. The analysis assessed the effectiveness of the classification of single points or clusters to identify the optimal setup that achieves an accurate segmentation between adjacent deformation processes. Identifying this initial setting was essential for selecting and developing advanced deep-learning approaches.