Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to exploit satellite differential interferometric synthetic aperture radar (DInSAR) data to investigate land subsidence processes, using European Ground Motion Service (EGMS) Sentinel-1 DInSAR 2018−2022 datasets. The workflow involves the implementation of Persistent Scatterers (PS) time series classification through the PS-Time tool, deformation signal decomposition via independent component analysis (ICA), and drivers’ investigation through spatio-temporal correlation with geospatial and monitoring data. Subsidence time series at the three demonstration sites of Bologna, Ravenna and Carpi (Po Plain, Italy) were classified into linear and nonlinear (quadratic, discontinuous, uncorrelated) categories, and the mixed deformation signal of each PS was decomposed into independent components, allowing the identification of new spatial clusters with linear, accelerating/decelerating, and seasonal trends. The relationship between the different independent components and DInSAR-derived displacement velocity, acceleration, and seasonality was also analyzed via regression analysis. Correlation with geological and groundwater monitoring data supported the investigation of the relationship between the observed deformation and subsidence drivers, such as aquifer resource exploitation, local geological setting, and gas extraction/reinjection.

This study assesses subsidence-induced risk to urban infrastructure in three major Italian cities—Rome, Bologna, and Florence—by integrating satellite-based persistent scatterer interferometric synthetic aperture radar (PSInSAR) ground displacement data with urban vulnerability metrics into a novel risk assessment workflow, incorporating land use and population data from the Copernicus Land Monitoring Service (CLMS)—Urban Atlas. This analysis exploits ERS-1/2, ENVISAT, and COSMO-SkyMed PSInSAR datasets from the Italian Extraordinary Plan of Environmental Remote Sensing, plus Sentinel-1 datasets from CLMS—European Ground Motion Service (EGMS), and spans a 30-year period, thus capturing both historical and recent subsidence trends. Angular distortion is introduced as a critical parameter for assessing potential structural damage due to differential settlement, which helps to quantify subsidence-induced hazards more precisely. The results reveal variable subsidence hazard patterns across the three cities, with specific areas exhibiting significant differential ground deformation that poses risks to key infrastructure. A total of 36.15, 11.44, and 0.43 km² of land at high to very high risk are identified in Rome, Bologna, and Florence, respectively. By integrating geospatial and vulnerability data at the building-block level, this study offers a more comprehensive understanding of subsidence-induced risk, potentially contributing to improved management and mitigation strategies in urban areas. This study contributes to the limited literature on embedding PSInSAR data into urban risk assessment workflows and provides a replicable framework for future applications in other urban areas.

This study explores potential irrigation-related land subsidence in an agricultural area of Córdoba, Argentina, using a multi-temporal InSAR analysis on Sentinel-1 imagery (2017-2021) and GRACE-FO gravimetric data. On the basis of the available Sentinel-1 imagery in the study area, Line-Of-Sight (LOS) cumulative displacement maps were generated using both ascending (2017-2018) and descending (2018-2021) acquisitions. To improve data reliability, low-coherence pixels (threshold $<0.5$) were excluded. The resulting maps show LOS displacements ranging from -5 to -10 cm in the first year of analysis $(2017-2018)$, and from -15 to -25 cm over the subsequent three years (2018-2021). The deformation products were compared with GRACE-FO (Gravity Recovery and Climate Experiment Follow- On) gravimetric data to assess possible correlations between land deformation and equivalent water thickness variations. Although the lack of ground-based validation data limits definitive interpretation, the spatial and temporal patterns observed suggest a link between the position of the center-pivot irrigation fields, surface deformation and changes in water storage. These findings underscore the value of integrated satellite-based approaches for preliminary assessment of hydrologically driven ground motion in data-scarce agricultural regions.