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.

While groundwater is the second largest freshwater reservoir on Earth, it remains difficult to monitor, relying mainly on sparse and often unavailable well measurements. The GRACE/GRACE–FO missions enabled the estimation of regional terrestrial water storage changes, although their coarse spatial resolution remains a major limitation. In this study, an unsupervised deep learning approach was exploited to downscale groundwater storage anomalies (GWSA) from the GRACE-derived GFZ G3P product from 0.5° to 0.1° spatial resolution, covering from 2003 to 2023. Thirteen ERA5 climatic variables and a digital elevation model were used as predictors. The framework was tested over France and validated in the Paris Basin using piezometric level information from the IGRAC GGMN open database. Results show good agreement between the downscaled and the original G3P product, with average temporal and spatial correlations of r = 0.99. When compared with in situ measurements, the downscaled product outperforms the original G3P data at the basin level (r = 0.67 vs. r = 0.62), while maintaining the accuracy at the local well scale (r = 0.39 vs. r = 0.38, respectively). The results highlight the potential of machine learning for GRACE super–resolution, while the main limitations are discussed.

Terrestrial Water Storage Anomalies (TWSA) derived from satellite gravimetry provide a unique, spatially integrated measure of large-scale hydrological conditions and are widely used to characterize basin-scale wet and dry states. However, their applicability to regional hydrological monitoring remains limited by their coarse spatial resolution, which hampers the detection of localized storage anomalies relevant to flood onset, evolution, and groundwater variability.Here, it is investigated an unsupervised deep-learning framework based on convolutional neural networks to enhance the spatial resolution of TWSA products across simulated and real mission scenarios. The method relies on a U-Net architecture driven by ERA5-Land hydro-climatic variables, and is first applied to end-to-end closed-loop simulations of the NGGM and MAGIC next-generation gravity missions. Particular attention is given to 5-daily solutions, which represent a key expected advancement of future gravimetric constellations compared with the monthly solutions currently provided by GRACE(-FO). In this setting, simulated TWSA fields are downscaled from 3° to 1°, while monthly 2° simulations are also considered to assess the framework under GRACE-like temporal conditions. For the syntetic products, the downscaled fields are validated using the ESA Earth System Model 3.0 as the reference signal.Results show that the downscaled products preserve consistency with native gravimetric observations while recovering finer-scale spatial structures. To assess transferability, the same pipeline is applied to real GRACE(-FO) observations, downscaling monthly 3° TWSA products into 1° maps.The framework is also extended to GRACE-derived groundwater storage anomalies (GWSA) over France, downscaled from 0.5° to 0.1° and evaluated against independent in situ groundwater observations.Finally, downscaled synthetic TWSA fields are exploited for extreme-event monitoring using threshold-based indicators derived from climatological anomalies. The analysis assesses whether the increased spatial resolution retains localized anomaly patterns otherwise smoothed at coarse scales, supporting the monitoring of flood-prone conditions.

Flood risk assessment in low-relief areas remains a challenge for traditional morphometric indices, especially when applied to extensive flat terrains surrounded by mountainous regions. To address these limitations, this study introduces the Slope-Weighted Topographic Position Index (SWTPI), a modified version of the standard Topographic Position Index (TPI) designed to identify micromorphological variations in low-relief areas. By incorporating slope as a scaling factor, the index aims to capture fine-scale terrain variations in both steep and flat terrains, without being distorted by surrounding topography. To evaluate its performance, the SWTPI was applied to four geographically diverse regions—Argentina, India, Italy, and the United States—and its results were compared with those of the standard TPI. In the Córdoba case study, a scatterplot analysis against the JRC Global Surface Water dataset indicated that SWTPI values better align with historical water occurrence than TPI values. Although originally developed to support flood risk analysis, the SWTPI also demonstrates potential for a wide range of applications, including geomorphological mapping, soil characterization, and urban or agricultural planning; making it a promising tool for both scientific research and applied geospatial analysis.