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One of the biggest challenges for the proper retrieval of the Earth surface deformation through the differential synthetic aperture radar interferometry (DInSAR) technique is the capability of effectively filtering out the atmospheric phase screen (APS), which often affects DInSAR measurements. In this work we investigate the effectiveness of the new auxiliary product developed by the European Space Agency, the extended timing annotation dataset (ETAD), to remove APS from both DInSAR interferograms and deformation time series generated at medium spatial resolution from Copernicus Sentinel-1 (S1) SAR images. First, we show that the straightforward application of the ETAD correction layers to medium-resolution DInSAR interferograms entails the generation of artifacts, introduced by the ETAD tropospheric layers. Accordingly, we present a methodology aimed at properly recalculating such tropospheric corrections by exploiting the standard deviation and the mean of the ETAD digital elevation model layers of the considered SAR images sequence and by taking into account the locally quasi-linear dependence of the tropospheric signal with respect to the elevation. Second, we carry out a thorough experimental analysis to evaluate the performance of the ETAD APS corrections for both interferograms and deformation time series. Finally, the performance evaluation analysis is extended to the results obtained by applying the APS corrections generated through the ECMWF ERA-5 data. An extensive comparison of the results achieved through the ETAD and ERA-5-based approaches is also discussed. The overall analysis is focused on an S1 dataset of 104 SLC images acquired over Central/Southern Italy from ascending orbits, between 2018 and 2020.

Campi Flegrei caldera is an Italian high-risk volcano experiencing a progressively more intense long-term uplift, accompanied by increasing seismicity and geochemical emissions over the last two decades. Ground deformation shows an axisymmetric bell-shaped pattern, with a maximum uplift of about 120 cm, from 2005, in the caldera central area. We analyzed Sentinel-1 and COSMO-SkyMed Multi-Temporal DInSAR measurements and GNSS data to reveal and investigate a geodetic anomaly that has clearly manifested since 2021, locally deviating from the typical bell-shaped deformation pattern. This anomaly is located east of Pozzuoli town, in the Mt. Olibano–Accademia area, covers an area of about 1.3 km2 and shows, in comparison to surrounding areas, a maximum uplift deficit of about 9 cm between 2021 and 2023. To investigate the anomaly causes, we analyzed the caldera seismicity and inverted the DInSAR data to determine the primary source of the ground deformation pattern, which is consistent with a penny-shaped source located approximately 3800 m beneath the Pozzuoli town, with a radius of about 1200 m. We also found that the time evolution of the uplift deficit in the geodetic anomaly area correlates well with the earthquake occurrence, with the greater magnitude events clustering in this area. These considerations suggest the geodetic anomaly is a local response to the tensile stress regime produced by the inflating primary deformation source. This phenomenon can be influenced by the Mt. Olibano–Accademia lava domes lithological heterogeneities that may induce a localized reaction to ground deformation during the inflationary phase. Our interpretation aligns with the concentration of earthquakes and hydrothermal fluid emissions in this area, indicating the presence of faults, fractures, and fluid circulation. Accordingly, the geodetic anomaly area represents a zone of crustal weakness that requires careful monitoring and study.

In this work, the airborne Synthetic Aperture Radar (SAR) infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) is described. This infrastructure allows IREA-CNR to plan and execute airborne SAR campaigns and to process the acquired data with a twofold aim. On one hand, the aim is to develop research activities; on the other hand, the aim is to support the emergency prevention and management activities of the Department of Civil Protection of the Italian Presidency of the Council of Ministers, for which IREA-CNR serves as National Centre of Competence. Such infrastructure consists of a flight segment and a ground segment that include a multi-frequency airborne SAR sensor based on the Frequency-Modulated Continuous Wave (FMCW) technology and operating in the X- and L-bands, an Information Technology (IT) platform for data storage and processing and an airborne SAR data processing chain. In this work, the technical aspects related to the flight and ground segments of the infrastructure are presented. Moreover, a discussion on the response times and characteristics of the final products that can be achieved with the infrastructure is provided with the aim of showing its capabilities to support the monitoring activities required in a possible emergency scenario. In particular, as a case study, the acquisition and subsequent interferometric processing of airborne SAR data relevant to the Stromboli volcanic area in the Sicily region, southern Italy, are presented

Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

In this work we describe the implementation of a processing chain for a fully automatic modeling of the seismic source parameters and its slip distribution through the inversion of the InSAR displacements generated from the EPOSAR service. This processing chain consists of a suite of procedures and algorithms handling a sequence of steps: selection of the highest quality InSAR datasets, definition of the area of interest, image sampling, non-linear and linear inversions to get, respectively, the source geometry and its slip distribution. A set of side procedures and interfaces also allows an interactive refinement and the publication of results, consisting of scientific data and graphical outputs. The whole procedure has been developed, tested and validated by considering 100 events with magnitudes between 5.5 and 8.2, worldwide distributed and covering an exhaustive range of mechanisms and tectonic contexts.Main aim of this work is describing the implementation of the automatic modeling procedures, used to produce solutions in real time, already during the emergency phase. These sources, validated by experts before their publication, can be a reference for operational purposes and initial scientific analyses. The creation of this repository sets also the framework to store, out of the emergency time, more sophisticated solutions, manually revised and/or with peer-review quality.

The 2019 Le Teil earthquake in Southern France may have been triggered by the stress change from 180 years of mass removal at a nearby cement quarry, according to satellite-based observations and seismological analyses of the rupture parameters

Structural health monitoring is a crucial issue in areas with different hazard sources, such as Italy. Among non-invasive monitoring techniques, remote sensing provides useful information in supporting the management process and safety evaluations, reducing the impact of disturbances on the functionality of construction systems. The ground displacement time-series based on the analysis of Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, as well as the information about the geology of the area and the geometry of the construction under monitoring, provides useful data for the built environment’s structural assessment. This paper focuses on the structural monitoring and damage assessment of constructions based on the GIS integration of DInSAR measurements, geological investigation, historical surveys and 3D modeling. The methodology is applied to the residential area of Valco San Paolo in the city of Rome (Italy). Once the geological interpretation has confirmed the results of the DInSAR measurements, a quick damage assessment that considers all the possible conditions of the pre-existing damage at the time zero of the monitoring is shown for a damaged manufact in the area. The presented results highlight how the strategy to correlate the DInSAR-monitored ground settlements with the damage scales allows potentially to monitor continuous construction systems.

The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011–2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.

Piton de la Fournaise is one of the most active worldwide volcanoes, located on the southeastern part of La Réunion Island. In this work, we focus on the eruption that occurred on the southeastern flank of this volcano, inside the Enclos Fouqué caldera, from 11 to 15 August 2019. This distal event was characterized by the opening of two eruptive fissures and accompanied by shallow volcano–tectonic earthquakes. We exploit the ground displacements using Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, which include the ground deformations generated during both the pre- and co-eruptive phases. To investigate the sources responsible for the detected ground displacements, we perform an analytical modeling of the retrieved DInSAR measurements. Our results reveal the presence of five volcanic sources (i.e., one sill-like source and four dikes), whose concomitant action during the pre- and co-eruptive phases generated the complex detected deformation pattern. The retrieved volcanic sources correlate well with the location of the opened fissures, the spatial distribution and the temporal evolution of the recorded seismicity, and other geophysical evidence already known in the literature.

This work presents the first results obtained by applying in situ and remote-sensing methodologies to monitor the Ponte della Musica-Armando Trovajoli located in Rome, within the activities of the WP6 “Structural Health Monitoring and Satellite Data” 2019-21 Reluis Project. In particular, the use of remote-sensing Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) measurements provided a spatial map of the displacement of the investigated infrastructure and the corresponding time-series, with the aim of monitoring deformation phenomena, focusing on the local scale analysis, which produces suitable results for urban monitoring and damage assessment. The DInSAR results have been integrated with the identification of the dynamic characteristics of the bridge, performed through an experimental campaign of ambient vibration measurements carried out in October 2020 and with the local-scale definition of the engineering geological setting of the foundation soil. The subsoil of the bridge is constituted by more than 50 m of recent alluvial deposits resting on Pliocene stiff clay acting as a geological bedrock. A substantially stable behavior of the bridge structural elements has been observed based on the analysis of both satellite and velocimetric data. This case represents a good example about how the integration of in situ sensors with remotely sensed data and the exploitation of a detailed knowledge regarding the on-site conditions represent a key factor for a sustainable structural and infrastructural monitoring and can support the planning both of maintenance and safety management.

We present an efficient Cloud Computing (CC) implementation of the Parallel Small BAseline Subset (P-SBAS) algorithm, which is an advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for the generation of Earth surface displacement time series through distributed computing infrastructures. The rationale of our approach consists in properly distributing the large data volumes and the processing tasks involved in the P-SBAS chain among the available (virtual and/or physical) computing nodes of the CC infrastructure, so that each one of these elements can concurrently work on data that are physically stored on its own local volume. To do this, both an ad hoc management of the data flow and an appropriate scheduling of the parallel jobs have been also implemented to properly handle the high complexity of the P-SBAS workflow. The proposed solution allows minimizing the overall data transfer and network load, thus improving the P-SBAS efficiency and scalability within the exploited CC environments. The presented P-SBAS implementation has been extensively validated through two experimental analyses, which have been carried out by exploiting the Amazon Web Services (AWS) Elastic Cloud Compute (EC2) resources. The former analysis involves the processing of a large (128 SAR images) COSMO-SkyMed dataset, which has been performed by exploiting up to 64 computing nodes, and is aimed at demonstrating the P-SBAS scalable performances. The latter allows us to show the P-SBAS capability to generate DInSAR results at a regional scale (150 000 km2 in Southern California) in a very short time (about 9 h), by simultaneously processing 18 ENVISAT frames that correspond to a total of 741 SAR images, exploiting in parallel 144 AWS computing nodes. The presented results confirm the effectiveness of the proposed P-SBAS CC solution, which may contribute to further extend the frontiers of the DInSAR investigation at a very large scale.

We present in this comment a Multi-Temporal SAR Interferometry (MT-InSAR) analysis showing that the results published by Milillo et al. (2019) in the Remote Sensing Journal, presenting the evidence of space geodetic observations relevant to displacements occurring before the collapse of the Morandi Bridge, happened in Genova (Italy) on the 14 August 2018, are questionable. In particular, we focus on the InSAR results obtained by Milillo et al. (2019) by processing the 3 m × 3 m resolution COSMO-SkyMed (CSK) data collected from ascending and descending orbits on the area of interest. These results, thanks to the high spatial resolution and the short revisit time characterizing this multi-orbit SAR dataset, represent the cornerstone of their analysis. The main findings of their study allow Milillo et al. to conclude that the InSAR processing of this COSMO-SkyMed dataset reveals the increased deformation magnitude over time of points located near the strands of the deck next to the collapsed pier, between 12 March 2017 and August 2018. In this comment, we show the results obtained by the IREA-CNR SAR team after processing the same ascending and descending CSK dataset, but by using two alternative and independent processing techniques: the Small BAseline Subset (SBAS) and the Advanced Tomographic SAR (TomoSAR) approaches, respectively. Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results, leading us to deeply disagree with the findings of their InSAR analysis.

We present an automatic and unsupervised tool for the systematic generation of Sentinel-1 (S1) differential synthetic aperture radar interferometry (DInSAR) coseismic products. In particular, the tool first retrieves the location, depth, and magnitude of every seismic event from interoperable online earthquake catalogs (e.g., the United States Geological Survey (USGS) and the Italian National Institute of Geophysics and Volcanology (INGV) and then, for significant (with respect to a set of selected thresholds) earthquakes, it automatically triggers the downloading of S1 data and their interferometric processing over the area affected by the earthquake. The automatic system we developed has also been implemented within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, with the aim of creating a global database of DInSAR S1 coseismic products, which consist of displacement maps and the associated wrapped interferograms and spatial coherences. This information will progressively be made freely available through the European Plate Observing System (EPOS) Research Infrastructure, thus providing the scientific community with a large catalog of DInSAR data that can be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. The developed tool can also support national and local authorities during seismic crises by quickly providing information on the surface deformation induced by earthquakes.

We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements.

In this work, we investigate the Mw 5.9 earthquake occurred on 7 November 2019 in the East-Azerbaijan region, in northwestern Iran, which is inserted in the tectonic framework of the East-Azerbaijan Plateau, a complex mountain belt that contains internal major fold-and-thrust belts. We first analyze the Differential Synthetic Aperture Radar Interferometry (DInSAR) measurements obtained by processing the data collected by the Sentinel-1 constellation along ascending and descending orbits; then, we invert the achieved results through analytical modelling, in order to better constrain the geometry and characteristics of the seismogenic source. The retrieved fault model shows a rather shallow seismic structure, with a center depth at about 3 km, approximately NE–SW-striking and southeast-dipping, characterized by a left-lateral strike-slip fault mechanism (strike = 29.17°, dip = 79.29°, rake = −4.94°) and by a maximum slip of 0.80 m. By comparing the inferred fault with the already published geological structures, the retrieved solution reveals a minor fault not reported in the geological maps available in the open literature, whose kinematics is compatible with that of the surrounding structures, with the local and regional stress states and with the performed field observations. Moreover, by taking into account the surrounding geological structures reported in literature, we also use the retrieved fault model to calculate the Coulomb Failure Function at the nearby receiver faults. We show that this event may have encouraged, with a positive loading, the activation of the considered receiver faults. This is also confirmed by the distribution of the aftershocks that occurred near the considered surrounding structures. The analysis of the seismic events nucleated along the left-lateral strike-slip minor faults of the East-Azerbaijan Plateau, such as the one analyzed in this work, is essential to improve our knowledge on the seismic hazard estimation in northwestern Iran.

Airborne Synthetic Aperture Radar (SAR) systems are gaining increasing interest within the remote sensing community due to their operational flexibility and observation capabilities. Among these systems, those exploiting the Frequency-Modulated Continuous-Wave (FMCW) technology are compact, lightweight, and comparatively low cost. For these reasons, they are becoming very attractive, since they can be easily mounted onboard ever-smaller and highly flexible aerial platforms, like helicopters or unmanned aerial vehicles (UAVs). In this work, we present the imaging and topographic capabilities of a novel Italian airborne SAR system developed in the frame of cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave S.r.l.). The system, which is named AXIS (standing for Airborne X-band Interferometric SAR), is based on FMCW technology and is equipped with a single-pass interferometric layout. In the work we first provide a description of the AXIS system. Then, we describe the acquisition campaign carried out in April 2018, just after the system completion. Finally, we perform an analysis of the radar data acquired during the campaign, by presenting a quantitative assessment of the quality of the SLC (Single Look Complex) SAR images and the interferometric products achievable through the system. The overall analysis aims at providing first reference values for future research and operational activities that will be conducted with this sensor.

This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, within the Grid Processing on Demand (G-POD) environment that is a part of the ESA’s Geohazards Exploitation Platform (GEP). The developed on-demand web tool, which is specifically addressed to scientists that are non-expert in DInSAR data processing, permits to set up an efficient on-line P-SBAS processing service to produce surface deformation mean velocity maps and time series in an unsupervised manner. Such results are obtained by exploiting the available huge ERS and ENVISAT SAR data archives; moreover, the implementation of the Sentinel-1 P-SBAS processing chain is in a rather advanced status and first results are already available. Thanks to the adopted strategy to co-locate both DInSAR algorithms and computational resources close to the SAR data archives, as well as the provided capability to easily generate the DInSAR results, the presented web tool may contribute to drastically expand the user community exploiting the DInSAR products and methodologies.

We present in this work a first performance assessment of the Parallel Small BAseline Subset (P-SBAS) algorithm, for the generation of Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) deformation maps and time series, which has been migrated to a Cloud Computing (CC) environment. In particular, we investigate the scalable performances of the P-SBAS algorithm by processing a selected ENVISAT ASAR image time series, which we use as a benchmark, and by exploiting the Amazon Web Services (AWS) CC platform. The presented analysis shows a very good match between the theoretical and experimental P-SBAS performances achieved within the CC environment. Moreover, the obtained results demonstrate that the implemented P-SBAS Cloud migration is able to process ENVISAT SAR image time series in short times (less than 7 h) and at low costs (about USD 200). The P-SBAS Cloud scalable performances are also compared to those achieved by exploiting an in-house High Performance Computing (HPC) cluster, showing that nearly no overhead is introduced by the presented Cloud solution. As a further outcome, the performed analysis allows us to identify the major bottlenecks that can hamper the P-SBAS performances within a CC environment, in the perspective of processing very huge SAR data flows such as those coming from the existing COSMO-SkyMed or the upcoming SENTINEL-1 constellation. This work represents a relevant step toward the challenging Earth Observation scenario focused on the joint exploitation of advanced DInSAR techniques and CC environments for the massive processing of Big SAR Data.

We present an innovative region-growing-based technique that permits to improve the surface displacement time-series retrieval capability of the two-scale Small BAseline Subset (SBAS) Differential Interferometric Synthetic Aperture Radar (DInSAR) approach in medium-to-low coherence regions. Starting from a sequence of multitemporal differential SAR interferograms, computed at the full spatial resolution scale, the developed method “propagates” the information on the deformation relevant to a set of high coherent SAR pixels [referred to as source pixels (SPs)], in correspondence to which SBAS-DInSAR deformation measurements have previously been estimated, to their less coherent neighbouring ones. In this framework, a minimum-norm constrained optimization problem, relying on the use of constrained Delaunay triangulations (CDTs), is solved, where the constraints represent the displacement values at the SP locations. Such DInSAR processing scheme, referred to as Constrained-Network Propagation (C-NetP), is easy to implement and, although specifically developed to work within the two-scale SBAS framework, it can be extended to wider DInSAR scenarios. The validity of the method has been investigated by processing a SAR dataset acquired over the city of Rome (Italy) by the Cosmo-SkyMed constellation from July 2010 to October 2012. The achieved results demonstrate that the proposed C-NetP method is capable to significantly increase the spatial density of the SBAS-DInSAR measurements, reaching an improvement of about 250%. Such an improvement allows revealing deformation patterns that are partially or completely hidden, by applying the conventional two-scale SBAS processing. This is particularly relevant in urban areas where the assessment and management of the risk associated to the deformation affecting infrastructures is strategic for decision makers and local authorities.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

This work presents the development of an efficient tool for managing, visualizing, analysing, and integrating with other data sources, the deformation time-series obtained by applying the advanced differential interferometric synthetic aperture radar (DInSAR) techniques. To implement such a tool we extend the functionalities of GeoNode, which is a web-based platform providing an open source framework based on the Open Geospatial Consortium (OGC) standards, that allows development of Geospatial Information Systems (GIS) and Spatial Data Infrastructures (SDI). In particular, our efforts have been dedicated to enable the GeoNode platform to effectively analyze and visualize the spatio/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. The examples we present, based on Sentinel-1 DInSAR results relevant to Italy, demonstrate the effectiveness of the extended version of the GeoNode platform.

This work is aimed at showing the present capabilities and future potentialities of an imaging radar system that can be mounted onboard flexible aerial platforms, such as helicopters or small airplanes, and may operate in the UHF and VHF frequency bands as Sounder and as Synthetic Aperture Radar (SAR). More specifically, the Sounder operates at 165 MHz, whereas the SAR may operate either at 450 MHz or at 860 MHz. In the work, we present the first results relevant to a set of Sounder and SAR data collected by the radar during a helicopter-borne campaign conducted in 2018 over a desert area in Erfoud, Morocco, just after the conclusion of a system upgrading procedure. In particular, a first analysis of the focusing capabilities of the Sounder mode and of the polarimetric and interferometric capabilities of the SAR mode is conducted. The overall system, originally developed by CO.RI.S.T.A. according to a ASI funding set up in 2010, has been upgraded in the frame of a contract signed in 2015 between ASI and different private and public Italian Research Institutes and Universities, namely CO.RI.S.T.A., IREA-CNR, Politecnico di Milano and University of Trento.

We present a new solution for the phase-preserving focusing of synthetic aperture radar (SAR) raw data acquired through the Terrain Observation with Progressive Scan (TOPS) mode. The proposed algorithm consists of a first interpolation stage of the TOPS raw data, which takes into account the Doppler Centroid frequency variations due to the azimuth antenna steering function, and allows us to unfold the azimuth spectra of the TOPS raw data. Subsequently, the interpolated signals are processed by using conventional phase-preserving SAR focusing methods that exploit frequency domain and spectral analyses algorithms, which are extensively used to efficiently process Stripmap and ScanSAR data. Accordingly, the developed focusing approach is easy to implement. In particular, the presented focusing approach exploits one of the available frequency domain Stripmap processing techniques. The only modification is represented by the inclusion, within the 2D frequency domain focusing step, of a spurious azimuth chirp signal with a properly selected azimuthal rate. This allows us to efficiently carry out the TOPS azimuth focusing through the SPECAN method. Furthermore, an important aspect of this algorithm is the possibility to easily achieve a constant and tunable output azimuth pixel size without any additional computing time; this is a remarkable feature with respect to the full-aperture TOPS-mode algorithms available in the existing literature. Moreover, although tailored on Sentinel-1 (S1) raw data, the proposed algorithm can be easily extended to process data collected through the TOPS mode by different radar sensors. The presented experimental results have been obtained by processing real Sentinel-1 raw data and confirm the effectiveness of the proposed algorithm.

IREA (an Institute of the Italian National Research Council) develops methodologies and technologies for acquisition, processing, fusion and interpretation of images and data obtained by using electromagnetic sensors (operating on satellite, aircraft and in situ), aimed at monitoring environment and territory, at non-invasive diagnostic and at electromagnetic risk assessment. These scientific activities have a significant follow-up in the fields of Cultural Heritage (CH) monitoring and protection. IREA is an excellence level Institute with a very solid international visibility, gained thanks to a very good scientific production and through the involvement in national and international projects. IREA is also involved in the organization of scientific events, conferences, summer schools and contributes to the editorial board of several international journals. Awards have been assigned to IREA laboratories and researchers who collaborate with many CH institutions in Italy and abroad.

We investigate the Mw 6.5 Norcia (Central Italy) earthquake by exploiting seismological data, DInSAR measurements, and a numerical modelling approach. In particular, we first retrieve the vertical component (uplift and subsidence) of the displacements affecting the hangingwall and the footwall blocks of the seismogenic faults identified, at depth, through the hypocenters distribution analysis. To do this, we combine the DInSAR measurements obtained from coseismic SAR data pairs collected by the ALOS-2 sensor from ascending and descending orbits. The achieved vertical deformation map displays three main deformation patterns: (i) a major subsidence that reaches the maximum value of about 98 cm near the epicentral zones nearby the town of Norcia; (ii) two smaller uplift lobes that affect both the hangingwall (reaching maximum values of about 14 cm) and the footwall blocks (reaching maximum values of about 10 cm). Starting from this evidence, we compute the rock volumes affected by uplift and subsidence phenomena, highlighting that those involved by the retrieved subsidence are characterized by significantly higher deformation values than those affected by uplift (about 14 times). In order to provide a possible interpretation of this volumetric asymmetry, we extend our analysis by applying a 2D numerical modelling approach based on the finite element method, implemented in a structural-mechanic framework, and exploiting the available geological and seismological data, and the ground deformation measurements retrieved from the multi-orbit ALOS-2 DInSAR analysis. In this case, we consider two different scenarios: the first one based on a single SW-dipping fault, the latter on a main SW-dipping fault and an antithetic zone. In this context, the model characterized by the occurrence of an antithetic zone presents the retrieved best fit coseismic surface deformation pattern. This result allows us to interpret the subsidence and uplift phenomena caused by the Mw 6.5 Norcia earthquake as the result of the gravitational sliding of the hangingwall along the main fault plane and the frictional force acting in the opposite direction, consistently with the double couple fault plane mechanism.

In this work, we investigate the sea surface monitoring capabilities of a Synthetic Aperture Radar (SAR) system equipped with a three-antenna hybrid Across Track (XT)/Along Track (AT) inteferometric configuration. To do this, we focus on the X-Band airborne InSAeS4 SAR system. Moreover, we propose a simple but effective methodology that allows simultaneous retrieval of the sea surface height and velocity by means of a straightforward, easy-to-implement, linear inversion procedure, which is very general and can be implemented with any system equipped with a three-antenna hybrid XT/AT Interferometric SAR (InSAR) configuration. In our case, we present an experiment carried out in January 2013 in South Italy over the coastline stretch of the Campania region including the Volturno River outlet. In this regard, we highlight that in situ measurements of the retrieved sea surface height and velocity at the time of the airborne mission are unfortunately not available. Notwithstanding, the obtained results show some interesting evidence that the estimated quantities are physically sound. This, on the one side, provides a preliminary validation of the effectiveness of the overall presented methodology and, on the other side, highlights the potentialities of the three-antenna hybrid XT/AT InSAR configuration of the InSAeS4 system for sea state monitoring.

On 16 September 2015, a Mw 8.3 interplate thrust earthquake ruptured offshore the Illapel region (Chile). Here, we perform coseismic slip fault modeling based on multi-orbit Sentinel 1-A (S1A) data. To do this, we generate ascending and descending S1A interferograms, whose combination allows us to retrieve the EW and vertical components of deformation. In particular, the EW displacement map highlights a westward displacement of about 210 cm, while the vertical map shows an uplift of about 25 cm along the coast, surrounded by a subsidence of about 20 cm. Following this analysis, we jointly invert the multi-orbit S1A interferograms by using an analytical approach to search for the coseismic fault parameters and related slip values. Most of the slip occurs northwest of the epicenter, with a maximum located in the shallowest 20 km. Finally, we refine our modeling approach by exploiting the Finite Element method, which allows us to take geological and structural complexities into account to simulate the slip along the slab curvature, the von Mises stress distribution, and the principal stress axes orientation. The von Mises stress distribution shows a close similarity to the depth distribution of the aftershock hypocenters. Likewise, the maximum principal stress orientation highlights a compressive regime in correspondence of the deeper portion of the slab and an extensional regime at its shallower segment; these findings are supported by seismological data.

We present in this work a first assessment of the imaging and topographic mapping capabilities of the InSAeS4 system, which is a single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR). In particular, we first provide a brief description of the InSAeS4 sensor. Then, we discuss the results of our analysis on the SAR and interferometric SAR products relevant to the first flight-test campaign. More specifically, we have exploited as reference the GPS measurements relevant to nine Corner Reflectors (CRs) deployed over the illuminated area during the campaign and a laser scanner Digital Elevation Model (DEM). From the analysis carried out on the CRs we achieved a mean geometric resolution, for the SAR products, of about 0.14 m in azimuth and 0.49 m in range, a positioning misalignment with standard deviation of 0.07 m in range and 0.08 m in azimuth, and a height error with standard deviation of 0.51 m. From the comparison with the laser scanner DEM we estimated a height error with standard deviation of 1.57 m.