Differential Synthetic Aperture Radar Interferometry Research Articles

On the RFI Detection in Differential Interferometric Synthetic Aperture Radar

Abstract. Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons.

Persistent scatterer interferometry (PSI) technique for road infrastructure monitoring: a case study of the roadway network of the Nile Delta (Egypt)

A dependable transportation infrastructure is an essential local resource, linking communities and facilitating economic expansion. The Nile Delta, renowned for its high population density, is experiencing subsidence as a result of natural forces exerting pressure on its sediments. Land subsidence exerts a substantial influence on infrastructure, economic, and social progress. This study examines the evaluation of land subsidence in roads experiencing various issues in the Nile Delta. Each year, subsidence results in significant damage and, in certain instances, complete disruption of numerous road networks, with potential durations ranging from a few days to several months. Conventional methods for identifying and monitoring subsidence on a large and intricate road network can be challenging due to the substantial time and resource requirements. Satellite-based monitoring is essential as it can effectively detect ground displacements caused by human activities (such as subterranean diggings and disruption of other infrastructures) or environmental dangers, specifically those associated with crucial hydrogeological incidents. Moreover, it possesses the benefit of efficiently encompassing extensive regions with precision. This study utilized the persistent scatterer interferometry (PSI) of SNAP-StaMPS workflow to employ differential interferometry synthetic aperture radar (DInSAR) processing methods for investigating the area. By utilizing Sentinel-1 data, we successfully acquired a comprehensive understanding of the deformation occurring in the study area, specifically in the roads where subsidence is persistently taking place. The data has demonstrated its value in enhancing our understanding of the safety status of infrastructure and facilitating maintenance actions. The accuracy of the radar interferometry results was confirmed through the use of in situ methods, including Global Navigation Satellite System (GNSS), Global Positioning System (GPS), smart phone, and unmanned aerial vehicle. This approach has demonstrated its capability to consistently detect vertical displacement in critical areas of roads. Furthermore, there is a strong correlation between the PSI interferometry and the in situ measurements when absolute values are taken into consideration. The findings of this study also demonstrate that open-source solutions are more effective, which encompass freely available data and software, offered through the COPERNICUS program, possess significant potential for monitoring vertical displacements in roads.

Intelligent Early Warning and Decision Platform for Long-Term Ground Subsidence in High-Density Areas for Sustainable Urban Development

Long-term ground subsidence (LTGS) is a relatively slow process. However, the accumulation of long-term subsidence has an adverse impact on the normal operation and safety of a subway, hindering sustainable urban development. A wide gap exists between early warning theory and its application in the control of LTGS during subway operation due to time span limitation. Providing decision support for LTGS in high-density urban areas during subway operation is difficult, and a collaborative decision system for real-time early warning and intelligent control is currently lacking. This study establishes the functional components of an intelligent early warning and decision platform, proposes a software system module, constructs an overall software framework structure, and develops a mobile intelligent early warning and decision platform. Moreover, this study introduces an early warning method for LTGS in high-density urban areas during subway operation. This method integrates an intelligent early warning decision-making platform, namely Differential Synthetic Aperture Radar Interferometry (DInSAR), land subsidence monitoring, operation tunnel subsidence monitoring, and other multisource data coupling. The method is applied to sections of the Hangzhou Metro Line 4 Phase I Project (Chengxing Road Station (CRS)–Civic Center Station (CCS)–Jiangjin Road Station (JRS) and Xinfeng Station (XS)–East Railway Station (ERS)–Pengbu Station (PS)). This work can serve as a reference for ensuring urban safety and promoting sustainable development.

Preliminary Geospatial and In Situ Reconnaissance of the 8 September 2023 Moroccan Atlas Earthquake Damage

This research investigates the post-earthquake performances of structures in four rural villages in the Moroccan Atlas, emphasizing common construction system characteristics and design flaws that render buildings susceptible to seismic events. Village selection was based on a prior multispectral satellite-image study, proving effective for planning high-impact, post-earthquake field campaigns. The significance of this research resides in on-site data collection, facilitating the physical assessment of earthquake-induced damage and identification of inherent vulnerabilities in construction systems. The constructions in the study area exhibited structural design deficiencies, inadequate construction techniques, and urban modifiers, leading to damage extensively documented in the literature, as well as less-documented unique damage. Predominant seismic-design shortcomings in the study area included subpar material quality, insufficient earthquake-resistant design, and unskilled labor. In situ data were complemented by a global geospatial approach using differential synthetic aperture radar interferometry with Copernicus Sentinel 1 data. Once calibrated the proposed methodology with field data, the analysis of remote sensing processing results, allow assessing the damages in other earthquake-affected areas, including those not visited in the field but also impacted by the seismic event.

Differential synthetic aperture radar (SAR) interferometry for detection land subsidence in Derna City, Libya

Abstract The country of Libya, situated on the Mediterranean fault zone, has a distinctive geodynamic regime due to the interplay between the Eurasian and African plates, which governs its tectonic evolution. In addition to its seismological significance, Libya is characterized by numerous subsidence and slope instabilities in regions with steep terrain. These geological phenomena have significant consequences for the built environment, as they pose an immediate danger to entire towns and essential infrastructure. Furthermore, infrequent weather phenomena, such as intense precipitation and thunderstorms, when coupled with the geological characteristics of some regions and the presence of seismically active terrain, have the potential to trigger landslide and land subsidence, resulting in significant harm to vital infrastructure. The current study utilizes the DInSAR technology to identify distinct subsidence occurrences that were induced by intense precipitation in coastal regions of Libya, specifically in Derna. These areas experienced significant flooding resulting in collapses during September 2023. A total of six pairs of co-event Interferometric Synthetic Aperture Radar (SAR) were utilized to generate displacement maps in the vertical, north-east, and north-west directions for the purpose of analysing the deformations. The aforementioned activities are conducted via Sentinel-1A images, which is freely accessible through the Copernicus program. Additionally, flood-prone zones were defined using Sentinel-1 GRD imagery. The Interferometric processing revealed multiple areas of subsidence. Subsidence rates of up to −14 cm were found in Derna city’s urban cores after flood. The findings suggest that subsidence may have an effect on the flood-proneness of the region of Derna City as Ground subsidence also occurred in the period immediately before the earthquake, at a rate of −14 cm.

Implementing the European Space Agency’s SentiNel Application Platform’s Open-Source Python Module for Differential Synthetic Aperture Radar Interferometry Coseismic Ground Deformation from Sentinel-1 Data

Differential SAR Interferometry is a largely exploited technique to study ground deformations. A key application is the detection of the effects promoted by earthquakes, including detailed variations in ground deformations at different scales. In this work, an implemented Python script (Snap2DQuake) based on the “snappy” module by SNAP software 9.0.8 (ESA) for the processing of satellite imagery is proposed. Snap2DQuake is aimed at producing detailed coseismic deformation maps using Sentinel-1 C-band data by the DInSAR technique. With this alternative approach, the processing is simplified, and several issues that may occur using the software are solved. The proposed tool has been tested on two case studies: the Mw 6.4 Petrinja earthquake (Croatia, December 2020) and the Mw 5.7 to Mw 6.3 earthquakes, which occurred near Tyrnavós (Greece, March 2021). The earthquakes, which occurred in two different tectonic contexts, are used to test and verify the validity of Snap2DQuake. Snap2DQuake allows us to provide detailed deformation maps along the vertical and E-W directions in perfect agreement with observations reported in previous works. These maps offer new insights into the deformation pattern linked to earthquakes, demonstrating the reliability of Snap2DQuake as an alternative tool for users working on different applications, even with basic coding skills.

A machine learning-based method for multi-satellite SAR data integration

Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.

Impact assessment of unsustainable airport development in the Himalayas using remote sensing: A case study of Pakyong Airport, Sikkim, India

Ground deformation is a widespread phenomenon that accelerates due to anthropogenic land development. Thus reclaimed/created land is more vulnerable to deformation and subsidence, especially in mountain areas. Advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) can be used to monitor such projects. The Pakyong Airport is an engineering feat, constructed by cutting a mountain and converting it into a tabletop in a landslide-prone zone and seismically active region of the Sikkim Himalayas. The cutting of hill slopes for airport construction and other anthropogenic activities has increased slope instability in the region. This paper studies the slow-moving landslides in the airport neighbourhood using A-DInSAR on Sentinel-1 time series data consisting of 64 images of ascending track and 82 images of the descending track. The time period of monitoring was from October 2014 to April 2018 (43 months). The images have been connected using the Minimum Spanning Tree graph for interferogram generation for estimating deformation. The atmospheric noise was removed, and the results enabled the identification of deformation (in line-of-sight) on the airstrip as well as in the neighbouring area, both the upslope and downslope of the airport. The deformation rates estimated were up to ±90 mm/year in Pakyong from both tracks. We could successfully capture such land movement associated with the Pakyong Airport construction and help assess the impacts of infrastructure construction on the slope stability of the area. The controlling factors such as precipitation, seismicity, geology and others were analysed with respect to the deformation obtained. This study helps in assessing the land deformation after construction (cutting and filling of the slope) in the area. The deformation detected in this study needs to be addressed for the safety of the residents as well as for the infrastructure present in the area.

Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

A Low-Cost and Fast Operational Procedure to Identify Potential Slope Instabilities in Cultural Heritage Sites

Italy is famous for its one-of-a-kind landscapes and the many cultural heritage sites characterizing the story of its regions. In central Italy, during the medieval age, some of them were built on the top of high and steep cliffs, often on the top of ancient ruins, to protect urban agglomerations, goods and people. The geographical locations of these centers allowed them to maintain their original conformation over time, but, at the same time, exposed them to a high risk of landslides. In this context, this research aimed to present an integrated and low-cost approach to study the potential landslide phenomena affecting two medieval towns. Field surveys and mapping were carried out through the use of innovative digital mapping tools to create a digital database directly on the field. Data gathered during field surveys were integrated with GIS analyses for an improved interpretation of the geological and geomorphological features. Due to the inaccessibility of the cliffs surrounding the two villages, a more detailed analysis of these areas was performed through the use of unmanned aerial vehicle-based photogrammetry, while advanced differential synthetic aperture radar interferometry (A-DInSAR) interpretation was undertaken to verify the stability of the buildings in proximity to the cliffs and other potential active failures. The results of the study highlighted the similar geometry and structural settings of the two areas. Kinematically, the intersection of three main joint sets tends to detach blocks (sometimes in high volumes) from the cliffs. The A-DInSAR analysis demonstrated the presence of a landslide failure along the northwest side of the Monte San Martino town. The buildings in proximity to the cliffs did not show evidence of movements. More generally, this research gives insights into the pro and cons of different survey and analysis approaches and into the benefits of their procedural integration in space and in time. Overall, the procedure developed here may be applied in similar contexts in order to understand the structural features driving slopes’ instabilities and create digital databases of geological/monitoring data.

Estimation of sea ice drift and concentration during melt season using C-band dual-polarimetric Sentinel-1 data

Characterizing sea ice changes over the Arctic can give proper insight into its extent in the current context of climate change. Sea ice motion's effect on ice area is influenced by seasonal conditions and the prevailing climate. However, the motion of thinner ice during the melt season can result in polynyas and cause significant ice loss that lasts the entire year. Sea ice drift and concentration during melt season are rarely discussed. The present study attempts to estimate sea ice drift during the melt season (July–August) using Differential Synthetic Aperture Radar Interferometry (DInSAR) with the usability of C-band Sentinel-1 data. The displacements/drifts were estimated using the phase shifts between two complex images with a temporal extent of 12 days. The study also proposed estimating sea ice concentration using polarimetric descriptors (power, purity, and randomness). The results revealed drifts of about 14–15 cm in the northeastern and southern parts followed by 45–50 cm in the middle parts and 20–25 cm along the coasts and open areas. Based on three polarimetric descriptors, thin ice and other ice fragments over open areas have shown less sea ice concentration as compared to the more concentrated pancake and pack ice with more compactness over the ice crystals. The study can be useful for computing drift in the melt season and carrying out spatiotemporal time-series analysis of seasonal sea ice drift/deformation studies. The algorithm and results can be used as a base study for the upcoming L- and S-band carrying NASA-ISRO Synthetic Aperture Radar (NISAR) mission.

Slope-Scale Evolution Categorization of Deep-Seated Slope Deformation Phenomena with Sentinel-1 Data

Deep-seated gravitational slope deformations (DsGSDs) are slope-scale phenomena which are widespread in mountainous regions. Despite interacting with human infrastructures and settlements, only a few cases are monitored with ground-based systems. Remote sensing technologies have recently become a consolidated instrument for monitoring and studying such widespread and slow processes. This paper proposes a three-step novel methodology to analyze the morpho-structural domain of DsGSDs by exploiting the advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) technique through (i) the analysis of A-DInSAR measurement point density and distribution defining a coverage threshold; (ii) the assessment of the actual ground deformation with respect to the orientation of phenomena based on slope, aspect, and C-index; and (iii) ground deformation mapping with previously ranked velocity interpolation. The methodology was tested on two differently oriented phenomena: the mainly north–south-oriented Croix de Fana and the mainly east–west-oriented Valtournenche DsGSD, located in the Aosta Valley Region, northern Italy. The results show a variation in the kinematic behavior between the morpho-structural domains, while also considering any other superimposed surficial deformations. This work provides the lines for the implementation of a rapid and low-cost tool based on the use of A-DInSAR measurements which are suitable for assessing the impact of any type of DsGSD on the anthropic facilities and infrastructures in mountainous areas.

Extensive and anomalous grounding line retreat at Vanderford Glacier, Vincennes Bay, Wilkes Land, East Antarctica

Abstract. Wilkes Land, East Antarctica, has been losing mass at an accelerating rate over recent decades in response to enhanced oceanic forcing. Overlying the Aurora Subglacial Basin, it has been referred to as the “weak underbelly” of the East Antarctic Ice Sheet and is drained by several major outlet glaciers. Despite their potential importance, few of these glaciers have been studied in detail. This includes the six outlet glaciers which drain into Vincennes Bay, a region recently discovered to have the warmest intrusions of modified Circumpolar Deep Water (mCDW) ever recorded in East Antarctica. Here, we use satellite imagery; differential synthetic aperture radar interferometry (DInSAR); and remotely sensed datasets of ice-surface velocity, ice-surface elevation and grounding line position to investigate ice dynamics between 1963 and 2022. Our results support previous observations of extensive grounding line retreat at Vanderford Glacier, measured at 18.6 km between 1996 and 2020. The persistent grounding line retreat, averaging 0.8 km yr−1, places Vanderford Glacier as the fastest retreating glacier in East Antarctica, and the third fastest in Antarctica, across decadal timescales. Such rapid retreat is consistent with the hypothesis that warm mCDW is able to access deep cavities formed below the Vanderford Ice Shelf, driving high rates of basal melting close to the grounding line. With a retrograde slope observed inland along the Vanderford Trench, such oceanic forcing may have significant implications for the future stability of Vanderford Glacier.

Ground subsidence associated with mining activity in the Ningdong coal base area, northwestern China revealed by InSAR time series analysis

Ningdong coal base area located in northwestern China is one of the largest coal-producing bases in China. The aim of this work is to investigate a regional-scale mining subsidence over the Ningdong coal base area, by using both conventional and advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methods. Fifteen L-band SAR images from ALOS-2 satellite and 102 C-band images from Sentinel-1A satellite spanning between November 2014 and July 2019 were used for the analysis. To increase the spatial extent of the displacement signal because of decorrelated effects, we modified the traditional Small Baseline Subset (SBAS) method to incorporate the coherence into the inverse problem, hereafter we call it coherence-based SBAS method. Instead of excluding decorrelated pixels present in the interferograms, we keep all the pixels in the time series analysis and down-weighted the decorrelated pixels with coherence. We performed the coherence-based SBAS method to both the two SAR datasets to obtain the subsidence rate maps and displacement time-series over the mining areas, and compared the results with that from the traditional stacking InSAR method. We evaluated the effectiveness of L-band and C-band DInSAR for monitoring mining subsidence by comparing differential interferograms and displacements derived from SBAS method between ALOS-2 and Sentinel-1A data. Compared to C-band, L-band SAR are less affected by phase aliasing due to large displacement gradients. The most significant subsidence was found at Maliantai mine with −264 mm/year detected by SBAS method from Sentinel-1 data. We validated the InSAR displacement accuracy by comparing both ALOS-2 and Sentinel-1 results with 18 GPS stations above five active mining regions. The average RMSE between InSAR and GPS measurements is 28.4 mm for Sentinel-1 data and 21 mm for ALOS-2 data. Our results demonstrate that the combined exploitation of L-band and C-band SAR data through both conventional and advanced DInSAR methods could be crucial to monitor ground subsidence in mining areas, which provides insights into subsidence dynamics and determine the characteristic surface response to longwall advance.

State of activity classification of deep-seated gravitational slope deformation at regional scale based on Sentinel-1 data

AbstractDeep-seated gravitational slope deformations (DsGSDs) are very slow slope instabilities that can have a long-term impact on anthropic structures and infrastructures. The characterization of their state of activity is, therefore, essential to evaluate it. By employing Differential Interferometry Synthetic Aperture Radar (DInSAR) techniques, a dedicated procedure, to explore the behavior and define the state of activity of 279 DsGSDs, inventoried in the regional landslide inventory of the Aosta Valley Region (Western Italian Alps), has been implemented. The proposed methodology consists of several steps. Firstly, Sentinel-1 data have been processed through a two-step, advanced, DInSAR processing scheme to detect and identify Persistent Scatterers (PSs). The velocity values measured along the radar Line of Sight (LOS) have been projected along the steepest slope. Subsequently, an analysis of PSs within DsGSD polygons, devoted to the assessment of Sentinel-1 data coverage, has been carried out; in particular, considering the PS abundance, computing voids in point distributions and assessing PS clustering to identify cases with adequate point number and distribution for a suitable definition of the state of activity. Finally, a spatial analysis based on cluster and outlier identification has been carried out to characterize the moving phenomena and their degree of variability in deformation rates. Overall, the implemented methodology provides a valid instrument to remotely define the state of activity of these huge phenomena, often wrongly underestimated or neglected in risk management, useful for a better definition of DsGSD impacts on anthropic elements for a proper land use planning.

Morphological Analysis of Anak Krakatau Volcano after 22 December 2018 Eruption using Differential Interferometry Synthetic Aperture Radar (DInSAR)

Anak Krakatau Volcano is an active volcano located in the Krakatau Complex, Sunda Strait, Indonesia. On 22 December 2018, the volcano experienced a major eruption that led to a tsunami that devastated the shores of the islands of Java and Sumatra and killed up to 437 people. The eruption also destroyed the volcano’s body and change its shape drastically and forming a large crater in the southwestern part. After that eruption, the volcano continues to grow up. This research aims to analyze the deformation of the Anak Krakatau Volcano post-2018 eruption by using the differential interferometry SAR method (DInSAR). In order to support the analysis, we additionally compare the DInSAR result with tectonic-volcanic activity. Sentinel 1-A type SLC satellite imagery data from 5 June 2019 to 7 January 2020; consisting of 19 images or 18 pairs as master and slave were used to producing a deformation map. DInSAR result shows the volcano was generally experiencing deflation during the period, ranging from -1.03 to -4.81 cm (-3.01 cm average). However, inflation also occurred ranging from 0 to 5.99 cm, correlating with shallow and deep volcanic activity and followed by eruptions in October 2019 when the highest activities were observed. Furthermore, coherence value should be highly considered along with DInSAR processing, and this research allows that coherence to be acceptable.

DInSAR Data Reveal an Intriguing Contemporaneous Onset of Deep Deflation below Vesuvio and the Ongoing Campi Flegrei Uplift

Campi Flegrei and Vesuvio volcanoes are only about 25 km apart, located on opposite sides of the densely inhabited area of Naples (Italy). Since neighbouring volcanoes may influence each other’s activity, it is of great interest to identify signs of any mutual interaction between Campi Flegrei and Vesuvio, or at least note coincidences in their recent deformation dynamics. After a large uplift, Campi Flegrei was generally subsiding from 1985 to 2001, while it has been uplifting—probably driven by deep magma inflation—at an accelerating rate since then. Here, we analysed the ground displacement in the whole Vesuvian area and its surroundings around the early 2000s using 1993–2010 ERS/ENVISAT ascending- and descending-orbit line-of-sight displacements obtained through the Small BAseline Subset Differential Synthetic Aperture Radar Interferometry technique. Although ground deformation is slow—a few millimetres per year—Empirical Orthogonal Function analysis shows a sudden trend change around 2001. Pre-2001 velocity maps confirm previously published results: subsidence mainly occurred inside the caldera rim—probably because of the sliding and compaction of young incoherent materials—and in a few spots around 10 km from the summital crater; eastward displacement occurred in a lobe east of Vesuvio, and westward displacement occurred in a lobe west of Vesuvio, as in the case of the spreading of the volcanic edifice and/or extensional tectonics. We attribute the subsidence spots to the previous high local number of new buildings per year. Post-2002 velocity maps provide evidence of a very different scenario: general subsidence in the whole Vesuvian area, westward displacement in a lobe east of Vesuvio, and eastward displacement in a lobe west of Vesuvio. This last arrangement of the ground displacement field is made even clearer by subtracting the post-2002 velocity from the pre-2001 value. The results of our analyses are consistent with the deflation of a deep pressurised source. Additionally, Vesuvio’s deep seismicity decreased at the beginning of 2002. The coincidence between the transition from deflation to inflation at Campi Flegrei and the onset of deflation below Vesuvio may suggest the possible transfer of magma and/or magmatic fluids between the two plumbing systems.

Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series

The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions.

Investigating Gravitational Slope Deformations with COSMO-SkyMed-Based Differential Interferometry: A Case Study of San Marco dei Cavoti

Landslides pose significant risks to towns and villages in Southern Italy, including the San Marco dei Cavoti hamlet (Benevento, Campania), where settlements have expanded into areas threatened by landslides, leading to property damage, disruption to the social fabric and loss of life. This study aims to investigate the surface deformations in the area using Differential Interferometry SAR (DInSAR) analysis on COSMO-SkyMed radar imagery and to assess the potential implications for landslide activity. The DInSAR analysis methodology allowed us to obtain high-precision results presented as time series diagrams and maps of cumulative displacement for the study area. Furthermore, the displacement rates derived from the DInSAR analysis were decomposed into vertical and horizontal components to provide better insights into the slope processes and their potential impacts on the San Marco dei Cavoti hamlet. Our significant findings revealed active slope movements and the uphill enlargement of previously inventoried landslides threatening the San Marco dei Cavoti hamlet. These insights contribute to a better understanding of the landslide dynamics in the region and highlight the areas that may require further investigation or intervention measures. In conclusion, this study demonstrates the effectiveness of DInSAR analysis in providing valuable insights into landslide dynamics and informing potential mitigation measures for at-risk communities. This technique could be applied to other landslide-prone regions to support informed decision-making and enhance the safety and resilience of affected communities.