The aim of this work is to develop an innovative methodology to analyse the time series (TS) of interferometric satellite data. TS are important tools for the ground displacement monitoring, mostly in areas in which in situ instruments are scarce. The proposed methodology allows to classify the trend of TS in three classes (uncorrelated, linear, non-linear) and to obtain the parameters of non-linear time series to characterise the magnitude and timing of changes of ground instabilities. These parameters are the beginning and end of the non-linear deformation break(s), the length of the event(s) in days, and the quantification of the cumulative displacement in mm. The methodology was tested on two Sentinel-1 datasets (2014–2020) covering the Alpine and Apennine sectors of the Piemonte region, an area prone to slow-moving slope instabilities. The results were validated at the basin scale (Pellice-Chisone and Piota basin) and at a local scale (Brenvetto, Champlas du Col and Casaleggio Boiro landslides) comparing with in situ monitoring system measurements, possible triggering factors (rainfall, snow) and already-collected events of the territory. The good correlation of the results has proven that the methodology can be a useful tool to local and regional authorities for risk planning and management of the area, also in terms of near real-time monitoring of the territory both at local and regional scale.

Deep-seated gravitational slope deformations (DsGSDs) are widespread phenomena in the Alpine environment. Their dynamics, although very slow, endanger human settlements and connecting infrastructures. Monitoring such phenomena is mandatory to evaluate the impact on infrastructure networks and inhabited areas. Nowadays, the implementation of a tool useful to define and manage the interactions of DsGSDs evolution and the anthropic element remains a challenge, particularly in land use planning. Apart from on-site monitoring, which is commonly poorly used for DsGSDs observation, satellite-based interferometry represents the most comprehensive instrument for an effective spatial and temporal characterization of these phenomena. This paper provides a dedicated procedure to assess the usability of Advanced Differential Interferometric SAR (A-DInSAR) techniques to explore the DsGSDs behaviour and investigate their local interaction along anthropic elements. Combining multi-temporal A-DInSAR data, ERS-1/2 (1992–2000), Radarsat-1/2 (2003–2010), COSMO-SkyMed (2011–2018) and Sentinel-1 (2014–2018), over the Motta de Pletè and Champlas du Col DsGSDs, north-western Italy, a line-of-sight displacement investigation over a long-time span is implemented. Multi-temporal deformation maps are generated to define the deformation pattern and DsGSDs evolution over time. Subsequently, a local-scale analysis along the main anthropic elements is performed, exploiting V_slope values and ground deformation time series, integrated with ground-based ones, where available. This local analysis is aimed to recognize the most critical sections of the anthropic elements along with an higher level of damage, and potential risk is expected. Moreover, the obtained results are compared with a survey damage of the anthropic elements for a local cross-check and to strengthen the A-DInSAR methodology. Overall, the presented methodology provides a powerful tool to better define the DsGSDs local dynamics in correspondence of the main strategic infrastructures and inhabited areas, for a proper infrastructure maintenance and territorial planning strategy.

Land subsidence poses significant problems in regions facing water management challenges, primarily resulting from unsustainable groundwater abstraction that causes significant drops in piezometric levels. Recognizing the urgent need for effective mitigation measures against land subsidence, this study developed a multicriteria decision-making model on the basis of the analytic hierarchy process (AHP) to evaluate ten mitigation alternatives. Expert opinions were gathered through structured surveys in four Mediterranean areas: the coastal aquifer of Comacchio (Italy), the Alto Guadalentín aquifer (Spain), the Sarıgöl-Alaşehir aquifer in the Gediz River Basin (Türkiye), and the Azraq Wetland Reserve (Jordan). Quantitatively, results of the AHP analysis consistently ranked two alternatives (enhancing water use efficiency and strengthening legal control over groundwater abstraction) as the most preferred to mitigate land subsidence, with statistical tests (Friedman test and Dunn’s post-hoc test) confirming the robustness of these results. The innovative contribution of this work lies in its integration of diverse expert perspectives into a comprehensive, transferable decision-making tool that goes beyond previous approaches by combining technical, economic, environmental, and social dimensions. The results provide valuable, targeted guidance for policymakers and planners, offering sustainable strategies that can be adapted to different regional contexts.

Land subsidence induced by groundwater withdrawal affects many regions around the world and is considered one of the most extensive phenomena caused by human activity nowadays. The Gediz River Basin is located in the western part of Türkiye, and develops along a regional extension of a horst-graben system. This basin has an agricultural and industrial importance in the region, increasing the water demand and positioning the Gediz Basin as one of the most stressed basins in the country. The main aims of this study are to evaluate the role of tectonics and groundwater withdrawal on land subsidence and on the evolution of faults in the Gediz River Basin. Additionally, other conditioning factors such as soft soil thickness layers are also studied. For this purpose, we processed 123 SAR images in descending orbit and 98 in ascending orbit acquired from Sentinel-1 between 2016 and 2021 by the parallel solution of the Small BAseline Subset (SBAS) algorithm (P-SBAS), allocated in the Geohazard Exploitation Platform (GEP). Secondly, we applied an Independent Component Analysis (ICA) to the InSAR time series in order to separate spatiotemporal patterns of long-term deformation and seasonal variations. P-SBAS results reveal that the maximum subsidence rates measured along the line of sight (-6.40 cm/year) are mainly concentrated in agricultural and urban areas. The results also suggest that there is a direct relationship between InSAR deformation and soft soil thickness, indicating that land subsidence is induced by the compaction of aquitard layers due to the groundwater withdrawal and piezometric head depletion. The analysis of the time series through the ICA shows two types of spatiotemporal deformation trends, one of them corresponds to long term and quasi-linear deformation due to the compaction of the aquitard, and the other represents the long-term deformations with seasonal rebounds produced by the seasonal loading and unloading cycles due to water level fluctuations.   Acknowledgements This research was funded by the PRIMA programme supported by the European Union under grant agreement No 1924, project RESERVOIR and by ESA-MOST China DRAGON-5 project (ref. 59339)

Soils rich in expansive clay minerals undergo significant soil structures and volume changes due to moisture fluctuations, swelling when absorbing water and shrinking as they dry. The volume changes can lead to structural damage in buildings and land degradation, and cause the formation of deep, polygonal cracks on the surface during dry periods. This affects water retention, infiltration, and root development, and leads to issues in hydrological modelling since the soil's response to water content changes is nonlinear and site-specific.The aim of the study is to quantify soil volume changes by combining field measurements (soil water content) with laboratory analysis (HYPROP), correlating soil water content with volume changes over time at different soil depths. The time series of volume changes are then compared with Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) displacement time series.The study focuses on Oltrepò Pavese, an agricultural area and one of the largest wine-producing regions in Lombardy, northwestern Italy. In this area, a monitoring system comprising meteorological and hydrological probes has been installed in the soil since 2021 as part of the Drive Life project (https://www.drive-life.it/) and now it is managed in the framework of the NODES project, financed by MUR – M4C2 1.5 of PNRR, funded by the European Union's NextGenerationEU (Grant agreement no. ECS00000036).The quantification of the soil volume changes under varying soil water contents can enhance the accuracy of hydrological models.

The availability of Sentinel-1 dataset with high-temporal resolution of measures (6-12 days) and long time period, can be considered as a “near-real-time monitoring” since it provides a sampling frequency enough to track the evolution of some ground deformations (e.g. landslides, subsidence) if compared to other sensors. However, the analysis and elaborations of such huge dataset, covering large areas, could be tricky and time-consuming without a first exploitation to identify areas of potential interest for significant ground deformations. The A-InSAR Time Series (TS) interpretation is advantageous to understand the relation between ground movement processes and triggering factors (snow, heavy rainfall), both in areas where it is possible to compare A-InSAR TS with in-situ monitoring instruments, and in areas where in situ instruments are scarce or absent. Exploiting the availability of Sentinel-1 data, this work aims to develop a new methodology ("ONtheMOVE" - InterpolatiON of SAR Time series for the dEtection of ground deforMatiOneVEnts) to classify the trend of TS (uncorrelated, linear, non-linear); to identify breaks in non-linear TS; to provide the descriptive parameters (beginning and end of the break, length in days, cumulative displacement, the average rate of displacement) to characterize the magnitude and timing of changes in ground motion. The methodology has been tested on two Sentinel-1 datasets available from 2014 to 2020 in Piemonte region, in northwestern Italy, an area prone to slow-moving slope instabilities. The methodology can be applied to any type of satellite datasets characterized by low or high-temporal resolution of measures, and it can be tested in any areas to identify any ground instability (slow-moving landslides, subsidence) at local or regional scale. The thresholds used for event detection should be calibrated according to geological and geomorphological processes and characteristics of a specific site or regional site. This innovative methodology provides a supporting and integrated tool with conventional methods for planning and management of the area, furnishing a further validation of the real kinematic behaviour of ground movement processes of each test-site and where it is necessary doing further investigation. In addition, elaboration applied to Sentinel-1 data is helpful both for back analysis and for near real-time monitoring of the territory as regards the characterization and mapping of the kinematics of the ground instabilities, the assessment of susceptibility, hazard and risk.