Land subsidence is a geological hazard with complex spatiotemporal evolution. Accurate monitoring and scientific susceptibility assessment of land subsidence are of great importance for disaster prevention, mitigation, and urban planning. Jiangdong New District (JND) of Haikou City is located in a soft-soil coastal sedimentary area with fragile geological conditions. Coupled with extensive land reclamation and construction projects in recent years, JND faces a serious threat of land subsidence. In this study, an integrated land subsidence susceptibility assessment framework that combines ordinary least squares, multiscale geographically weighted regression, and the analytic hierarchy process (AHP) is proposed. By considering both the global impacts and spatial heterogeneity of influence factors, the weights of the evaluation factors in the judgment matrix of AHP are optimized, thus enhancing the scientificity of susceptibility assessment. The proposed method is successfully applied to JND. The results show that land subsidence in JND is mainly concentrated along Jiangdong Avenue, around Lingshan Town, and near Meilan Airport, with a maximum deformation rate of -50.1 mm/yr and a maximum cumulative subsidence of 285.9 mm. Annual precipitation, the thickness of expansive soil, and the thickness of soft soil are the primary natural factors driving subsidence, whereas the distance to roads is the key anthropogenic factor. The complex and significant synergistic effects exist among these factors. High- and moderate-susceptibility areas account for 10.69% and 26.97% of JND, respectively, primarily distributed along road networks and in land reclamation areas. The integrated framework proposed in this study offers a robust and adaptive reference for land subsidence risk management.

Time-series analysis is a crucial component of Synthetic Aperture Radar (SAR) pixel offset tracking (POT), directly impacting the accuracy of deformation monitoring. However, most existing time-series approaches struggle with precise long-term monitoring, particularly for landslide experiencing long-term large deformation. The widely used Pixel Offset-Small Baseline Subset (PO-SBAS) method supports long-term analysis but often underestimates deformation in high-gradient regions. To overcome this limitation, we introduce a Robust Sequential Pixel Offset Tracking (RS-POT) method. RS-POT initially derives short-term deformations using a single-reference POT approach, then sequentially integrates them into a complete deformation time series through a robust fusion strategy. This ensures both long-term continuity and high accuracy in capturing large deformations. Simulation results show that RS-POT provides accurate deformation estimates in 94.42% of the study area. In a case study of the Qiantaishan landslide, RS-POT outperforms PO-SBAS through reducing the root mean square error (RMSE) with reference to global navigation satellite system (GNSS) measurements by 51.51% in the azimuth direction and 43.61% in the line-of-sight (LOS) direction. Additionally, due to fewer image pairs being required, RS-POT improves computational efficiency by 122% compared to PO-SBAS. Further simulations confirm that RS-POT performs reliably under large-gradient deformation conditions, and it is applicable to various landslide types, including traction, thrust, and homogeneous landslides. These results demonstrate that RS-POT offers a more accurate and efficient solution for long-term landslide deformation monitoring.

Phase unwrapping (PU) is a key step in synthetic aperture radar interferometry (InSAR) techniques, as its accuracy directly determines the precision of deformation estimation. Despite the widespread use of InSAR, many existing PU techniques, such as minimum cost flow (MCF), Snaphu, and recently developed deep-learning models, struggle to maintain high accuracy when faced with large deformation gradients. To address this issue, this study proposes a phase gradient rate constrained minimum cost flow (PGR-MCF) method. It uses the phase gradient rate (PGR) calculated from time series differential interferograms through stacking and fusion, to constrain weighting of the arcs during MCF unwrapping process. By optimizing the unwrapping path to prioritize low-gradient areas, the PGR-MCF method significantly improves unwrapping accuracy in high-gradient regions. In simulated experiments, the PGR-MCF method correctly unwrapped 99.5 % of the pixels in large deformation zones. In the application to Guobu landslide, the PGR-MCF method reduces the root mean square errors (RMSEs) between InSAR and global navigation satellite system (GNSS) deformation time series by more than 69 %, compared to other tested methods. This method does not require external datasets or prior models, ensuring its broad applicability. Additionally, it reliably unwraps interferograms with large spatiotemporal baselines, thus increasing the number of reliable unwrapped interferograms for time-series deformation inversion and improving deformation monitoring accuracy. Moreover, it has been proven to be an effective PU method for deformation estimation in regions with large deformation gradients.

The 2017 eruption of Kambalny volcano on the Kamchatka Peninsula provides an opportunity to investigate the coupling between shallow magma pressurization and co-eruptive deformation in an open-vent volcanic system. In this study, multi-orbit InSAR data from Sentinel-1 and ALOS-2 satellites are processed to derive co-eruptive deformation fields. The deformation pattern shows a near-axisymmetric uplift centered on the crater and an asymmetric displacement extending southeastward. Coherence and Normalized Difference Snow Index (NDSI) analyses indicate that snow cover, vegetation, and steep topography have caused decorrelation in the ALOS-2 interferograms. Fusion of the ascending and descending datasets reveals a vertical uplift of up to 15 cm and NW-SE-oriented horizontal extension. Then, Bayesian inversion using the GBIS platform is conducted to constrain the pressure sources responsible for the observed deformation. Comparative modeling of single (Penny, Dike) and composite (Penny + Dike) source models indicates that the Penny + Dike configuration yields the best statistical and physical fit with minimized residuals, reproducing both localized uplift and asymmetric extension. The optimal solution suggests magma injection along an inclined NW-SE trending dike has fed a shallow disk-shaped cavity, reflecting coupled processes of dike propagation and hydrothermal re-pressurization. The estimated volume change is approximately 7.6 ± 4.85 × 105 m3, a value consistent with an independently calculated Dense Rock Equivalent volume of 6.6 × 105 m3. This estimate agrees well with field observations and supports the interpretation of the 2017 Kambalny eruption as a moderate-scale (VEI 3) event. Our results indicate that the integration of InSAR with geodetic modeling provides powerful constraints on the dynamics of shallow magmatic and hydrothermal systems. The Penny + Dike model offers a physically realistic framework complementing the seismic tomography results and enhancing our understanding of magma transport and pressurization processes beneath Kambalny volcano.

We examine the 6 February 2023 Türkiye–Syria earthquakes using an extensive SAR dataset, addressing some limitations of previous studies. Large surface displacements caused significant loss of coherence in the Sentinel-1 Differential SAR Interferometry (DInSAR) results and prior analyses using Pixel Offset Tracking (POT) were limited by the poor azimuthal resolution of the available Sentinel-1 and ALOS-2 SAR images. For the first time, we present high-azimuth-resolution displacement measurements obtained thanks to the SAOCOM-1 sensors. Azimuth information is important considering that the main movements occurred were horizontal and, in some areas, with an important N-S component. By exploiting multi-frequency Sentinel-1, ALOS-2, and SAOCOM-1 SAR data and applying the DInSAR and POT techniques, where appropriate, we derived a detailed displacement field and retrieved an elaborated fault model comprising 22 segments; this model accurately characterizes the geometry and kinematics of the two main faults. Maximum slip reaches ∼15 m for both faults, and the total seismic moment corresponds to Mw 7.9. A finite-fault ShakeMap generated from this source model shows improved agreement with near-field ground motions relative to point-source formulations, while on-fault static stress changes identify low-slip areas that remained unbroken during rupture. The three-dimensional displacement field reveals a broad uplifted region and opposing horizontal motions between the two main ruptures, indicating distributed deformation within an interfault block that accommodates part of the Arabia–Anatolia convergence. This off-fault deformation has not been documented previously for the 2023 sequence and provides new constraints on strain partitioning and future seismic hazard along the East Anatolian Fault Zone.

This paper presents the preliminary findings of two experiments conducted to evaluate the capability of high-resolution X-band SAR data to identify plastic litter in aquatic environments. First, a small plastic island was designed, by collecting plastic bottles, water cans, and polystyrene boxes, and deployed in Lake Massacciuccoli, central Italy, concurrently with the acquisition of a Cosmo-SkyMed radar image. Then, the experiment was repeated in the marine environment of the Gulf of La Spezia, by deploying a larger plastic island for a longer time interval during the overpasses of several satellites of the Cosmo-SkyMed and ICEYE missions. The availability of multiple single- and dual-polarization data allows assessing how sensor parameters, such as spatial resolution, geometry of view, and polarization, impact on plastic identification. Results show that the plastic island has a radar signature different from the surrounding water with slightly higher backscatter values, confirming the effectiveness of high-resolution X-band SAR data in detecting floating plastic patches of significant size. Regarding polarization, HH seems to exhibit better performance than VV whereas both co-polarizations outperform HV and VH. Moreover, low incidence angles are likely more suitable than high ones, providing a stronger backscattered signal. These are the first experiments performed in the framework of the SPACE IT UP project.

Distributed Scatterer synthetic aperture radar interferometry (DS-InSAR) has significantly expanded the applicability of InSAR in low-coherence areas, yet its performance critically depends on the accurate identification of statistically homogeneous pixels (SHPs). Existing amplitude-based SHP methods fail to account for phase ramp interference, whereas phase-based alternatives suffer from poor noise robustness. To address these limitations, a Phase Gradient Rate (PGR)-constrained SHP selection method (PGR-SHP) is proposed. The proposed method combines PGR-based deformation boundary detection with amplitude-based statistical homogeneity testing. Both simulated and real-data experiments validated the efficacy of the proposed method in identifying deformation boundaries across various noise conditions and mitigating interferometric phase blurring. With the adoption of the PGR-SHP method, the number of effective points in the three different landslides increased by 192%, 146%, and 169% relative to the original Stacking-InSAR results obtained without SHP selection, respectively. The research outcomes show that the proposed method is well suited for landslide deformation monitoring in complex geological environments, providing more reliable deformation measurements essential for landslide mechanism analysis.

Peatlands are vital ecosystems that provide substantial ecological, economic, and societal benefits, making their protection and restoration essential for both current and future generations. Although they cover only a small fraction of the Earth's surface, peatlands are among the most effective natural carbon sinks and play a critical role in regulating the global climate. However, these ecosystems face mounting threats from environmental pressures, including urbanization, infrastructure development, unsustainable agriculture, drainage, peat extraction, forestry, rising sea levels and climate change itself. Encouragingly, growing recognition of their importance has led to an increase in monitoring and restoration efforts worldwide. As part of the ESA-funded WorldPeatland project, we studied three moorland sites in England using advanced remote sensing techniques, supported and validated by auxiliary datasets. By generating ground displacement maps and time series through the multi-temporal Interferometric Synthetic Aperture Radar method known as Enhanced Persistent Scatterers, we were able to analyse both recent and long-term peatland dynamics to assess their condition and ongoing changes. Widespread subsidence was observed across all investigated areas, suggesting that the rate of peat degradation currently exceeds the rate of peat formation. Key contributing factors include the removal of protective vegetation likely due to wildfires, agricultural activities and anthropogenic land surface management practices. Additionally, the lowering of water levels, exacerbated by climate change, further threatens peatland aggradation. These processes lead to peat drying, which in turn accelerates oxidation, erosion, and decomposition.

This study is to analyze the ability of full-resolution speckled Synthetic Aperture Radar (SAR) imagery to estimate the water-coverage status of intermittent lakes. The methodology models SAR speckled measurements using a compound K-model which is shown to allow distinguishing between two cases: the lake full covered by water (namely the fractional area coverage is around 100%) or completely empty (namely the fractional area coverage tends to 0).The model is first verified using a data set of SAR scenes collocated with ancillary optical measurements and, then, applied to a time-series of SAR imagery to monitor the variation of water-area coverage in an Italian intermittent lake.Experimental results confirm the soundness of the proposed approach confirming that speckle can be successfully used to extract information about intermittent lakes.

The present study demonstrates the application of time series techniques with Differential Interferometry Radar (MT-InSAR) using images from the Sentinel-1 (C-band) and SAOCOM (L-band) radar sensors. The main objective was to identify and assess ground deformation at the San Miguel volcano, one of the most active volcanoes in El Salvador, for study and monitoring purposes. Various approaches were employed to enhance phase signal quality, including the use of Small Baseline Subset (SBAS) and Persistent Scatterers (PS) MT-InSAR methodologies, as well as atmospheric corrections using both the GACOS (Generic Atmospheric Correction Online Service for InSAR) data and an altitude-dependent linear model able to estimate and then remove the stratified component of the troposphere. Additionally, orbital corrections were performed, and the impact of Digital Elevation Model (DEM) accuracy and updates of the topography on phase, especially for SAOCOM L-band images, were evaluated. The InSAR results revealed subsidence in the volcano crater showing a maximum rate of -25 mm per year, then we modeled the retrieved deformation patterns as a system of normal faults simulating two concentric craters. Moreover, limited deformation was detected in the western upper flank of the volcano during the 2023 period using SAOCOM data. We also observed that the volcano was strongly affected by atmospheric disturbances, although the performed corrections by using GACOS information did not yield to fully satisfactory results. In our work, the importance of using updated and accurate DEMs when processing L-band images has been emphasized. Finally, our study suggests to continue using SAR images for monitoring San Miguel volcano activity, implementing longer time series with SAOCOM, and performing comparisons between SAR data acquired from both C- and L-band, possibly covering the same period, to gain a more comprehensive understanding of the deformation occurring at San Miguel volcano, and to improve the understanding of the volcanic activity.

In this study, microwave satellite images, and in particular Synthetic Aperture Radar (SAR), are used to detect volcanic lava flow. We propose methods that exploit both single and dual polarization channels to map the deposits that occurred in three case studies: the Etna Volcano (Italy) 2007-2008 eruption and the Sangay Volcano (Ecuador) eruption that took place in December 2021. The objective of the work is to analyse the different information carried out by single and dual polarization data, in different volcanic environments, aiming to identify the most suitable solution for each setting. For the Etna case study, we applied two change detection methods. The first one exploits the normalised difference and is used on a pair single polarization (SP) image captured by ENVISAT mission. The second method relies on the dual polarization (DP) data acquired by ALOS-PALSAR satellite. The latter is based on the covariance matrix derived from DP images, and the normalized difference of pre and post-event matrices. As far as the Sangay case study, the results are undertaken over a pair of images collected by the C-band Sentinel-1 DP SAR sensor. The outputs are then analyzed to detect the different phenomena (i.e., lava flow, pyroclastic currents, landslides), that occurred over the scene. The work demonstrates the complementary evidence provided by the co- and cross-polarized channels, suggesting the combination of them to obtain additional and more accurate information. This activity is part of a INGV funded project, SAFARI - an AI-based StrAtegy For volcano hAzaRd monItoring from space, a research project that aims at developing a comprehensive space-based strategy for the near-real-time characterization of volcanic state of activity, based on the extraction of satellite-derived input parameters to physical models for rapid scenario forecasting during the eruptive phases.

The Platform for Earth Observation from Space (PEOS) is a cutting-edge e-infrastructure developed at the Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy. It embraces research areas related to seismology, volcanology, space weather and geodesy, integrating heterogeneous products for Earth Observation, natural hazard monitoring and surveillance which leverage space data acquired from satellites and ground-based platforms. PEOS' goals are twofold: first, to provide an integrated, flexible environment for hosting, executing, and orchestrating scientific algorithms while managing the whole data-product lifecycle; second, to ensure the dissemination of FAIR (Findable, Accessible, Interoperable, Reusable) data to the scientific community and support products provision towards operational services.

We investigated the post-seismic period of the March 2021 Damasi-Tyrnavos (Thessaly, Greece) normal fault earthquakes by applying the multi-temporal interferometric Small Baseline Subset method. We processed 68 ascending Sentinel-1 acquisitions between 2020/03/15 and 2022/09/12. Our results identified three areas on the hanging wall of the ruptured faults showing non-linear deformation trends (systematic motion away from the satellite), and another area, on the footwall (systematic motion towards the satellite), interpreted as due to a post-seismic effect. Inversion of the InSAR data indicated the occurrence of afterslip co-planar to the sequence’s two largest fault planes (M 6.3 and M 6.0, respectively). Most of the afterslip, with a peak of about 0.2 m, occurred on the fault corresponding to the 4 March 2021 event, at a depth of 7.5 km, while the fault corresponding to the M 6.3 event only showed very shallow adjustments and minor features at the border of the coseismic pattern. The transient uplift affected the footwall of the 3 March 2021 event and may indicate that the rupture nearly reached the surface towards the SW of the epicenter. The afterslip showed a fast phase lasting between March and August 2021 (5 months) and a second phase from March 2022 up to September 2022. A correlation between afterslip and relocated hypocenters indicates that most of the afterslip was aseismic. The moment release of the afterslip (fast phase) is about 7% that of the mainshocks.

In 2023, seismic activity of considerable magnitude occurred along the Türkiye-Syria border, characterised by an Mw 7.8 earthquake on the 6th of February and was followed by an Mw 7.5 event, nine hours later. These earthquakes, which are the strongest recorded in recent years, resulted in over 50,000 casualties and are related with the activity of the East Anatolian Fault Zone —a 600 km-long plate boundary where the Arabian and Anatolian plates meet. To analyse these seismic events, we leveraged data from diverse satellites, including SAOCOM-1, Sentinel-1, and ALOS-2. Employing InSAR techniques, such as conventional interferometry and Pixel Offset tracking, we assessed surface deformations caused by the events. The high-resolution Synthetic Aperture Radar displacement results underwent non-linear and linear inversions, enabling the creation of detailed variable slip fault models. A meticulous multiscale sampling approach was applied, that facilitated a comprehensive examination of the tectonic structures triggering these events. The fault zone exhibited a pronounced left-lateral strike-slip character, with components of dip-slip movements observed in specific segments. Additionally, we capitalised the detailed slip models, to estimate the distribution of the intensity of ground motions in the affected region.

The coastal plains of the Italian peninsula and its main islands are highly exposed to the ongoing sea-level rise triggered by global warming and often accelerated by land subsidence. In the frame of the GAIA Project, funded by the Italian Ministry of University and Research, here we focus on the current and expected relative sea level trend at 2030-2050-2100 and 2150 for 39 main coastal plains which are affected by spatially variable rates of Vertical Land Movements (VLM). To estimate the current VLM rates we have used geodetic data from about 27 years of continuous GNSS observations at selected stations located within 5 km from the coast and InSAR data from the Copernicus European Ground Motion Service (https://egms.land.copernicus.eu/). The latter were integrated with additional InSAR data sets to extend the data time series to the last decade. We provide revised sea level rise projections for the entire Italian region by including the estimated VLM in the SL projections released by the IPCC in the AR6 Report for different Shared Socio-economic Pathways and global warming levels (www.ipcc.ch). To reinforce the analysis and the interpretations, we also considered the sea level data recorded at the tide gauge stations belonging to the PSMSL (https://psmsl.org) and ISPRA (https://www.mareografico.it/) networks. Results show the current IPCC projections are often underestimated and not representative of the expected future sea levels since they neglect the effect of VLM due to tectonics and local factors. Finally, we show detailed maps of the expected flooding scenarios for 39 main coastal plains of the Italian region, projected on high resolution DEM obtained by the spatial analysis of LiDAR data available from the Italian Ministero dell’Ambiente e della Tutela del Territorio. The geoprocessing, that included the reanalysis of the vertical datum of the original LiDAR acquisition to project the scenarios on the mean sea level, highlighted that about 10.000 km2 of the coasts are yet exposed to multiple coastal hazard. Enhanced impacts on the environment, human activities and coastal infrastructures, are expected, requiring adaptation measures to face the ongoing sea level rise.

This study is to explore the ability of Radarsat Constellation Mission (RCM) Synthetic Aperture Radar measurements collected under the Hybrid-polarity (HP) mode to observe coastal area. The study focuses on two applications, namely: coastline and aquaculture, and the proposed methodology consists of exploring m-chi polarimetric features that stem from the HP measurements. The experimental part consists of processing actual RCM HP scenes collected off the coast of Piombino, Italy to demonstrate the soundness of the proposed rationale.

This study investigates the scattering properties of wind turbines, utilizing synthetic aperture radar datasets collected by ALOS PALSAR-2, RADARSAT-2, and PAZ satellite missions. The radar imagery spans L-, C- and X-band frequencies keeping almost the same incidence angle and pixel spacing. Focused on the Robin Rigg offshore wind farm in Solway Firth, UK, the analysis deals with assessing backscattering properties through multi-polarization, i. e., co- and crosspolarized, normalized radar cross-sections and reflection symmetry. The study evaluates the features of the turbines in relation to a target-free surrounding sea surface. The experimental results indicate that the incident wavelength and the relative orientationsignificantly impacts the detectability of wind turbines. Additionally, reflection symmetry is identified as a reliable and effective parameter for characterizing wind turbines backscattering. The results also suggest that increasing (decreasing) the incident wavelength increases (reduces) the co-polarized backscattering and the co-/cross-polarized correlation of wind turbines.

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. Despite these improvements, classifying and interpreting such vast datasets remains a significant challenge. InSAR analysts and geologists frequently have to manually analyze the time series from Persistent Scatterer Interferometry (PSI) to model the complexity of geological and tectonic phenomena. This process is time-consuming and impractical for large-scale monitoring. Utilizing Artificial Intelligence (AI) to classify and detect deformation processes presents a promising solution. In this study, vertical ground deformation time series from northeastern Italy were obtained from the European Ground Motion Service and classified by experts into different deformation categories. Convolutional and pre-trained neural networks were then trained and tested using both numerical time-series data and trend images. The application of the best performing trained network to test data showed an accuracy of 83%. Such a result demonstrates that neural networks can successfully identify areas experiencing distinct geodynamic processes, emphasizing the potential of AI to improve PSI data interpretation.

In this study, dual-polarimetric (DP) feature derived from Sentinel-1 Synthetic Aperture Radar (SAR) data is used to detect burned area (BA) related to the wildfire that hit Stromboli Island on May 2022. First, a DP change detection feature based on the difference of covariance matrices obtained from images collected before and after is exploited to detect the BA. Finally, the BA is extracted using a clustering algorithm based on k-means. To assess the performance of the proposed approach, the DP detector is contrasted with single polarimetric ones, and optical imagery from Sentinel-2 used as reference data for evaluating the accuracy of the DP method. Experimental results, obtained by processing a pair of C-band Sentinel-1 imagery collected before and after the 2022 Stromboli Island, wildfire shows that the proposed feature is able to detect the BA from the surroundings. In addition, the DP feature outperforms the SP ones.