The Campi Flegrei caldera (southern Italy) is an active volcano whose last eruption occurred in 1538. This caldera is characterized by ground displacements (subsidence or uplift), typically identified with the term "bradyseism". Over the last two decades the Campi Flegrei caldera was affected by a phase of progressively increasing uplift and it was accompanied by increasing seismicity and geochemical anomalies. The ground displacement pattern is monitored through GNSS and Differential SAR Interferometry (DInSAR) analysis, the latter focused on the Sentinel-1 SAR data exploitation. According to the available measurements, the displacement pattern is mainly radial with a maximum uplift in the area corresponding to Rione Terra (Pozzuoli). However, we have also detected a geodetic anomaly, near the Mt. Olibano–Accademia area, where most of the recent seismicity of the caldera is concentrated, which become clearly recognizable starting from 2021 and further developed during 2022 and 2023.

We present in this study an enhancement to our previous work, in which an automated method, based on Convolutional Neural Networks (CNNs), has been developed for identifying co-seismic ground deformation patterns within the Differential SAR Interferometry (DInSAR) maps generated through the EPOSAR service of the European Plate Observing System (EPOS) Research Infrastructure. The implemented improvements have been achieved through two main lines of actions. Firstly, the generation of the synthetic dataset, used to train the developed CNN, has been enhanced; this concerns the improvement of the simulation of possible atmospheric disturbance within the DInSAR interferograms, and the introduction of a simulator of phase unwrapping errors. Secondly, the original CNN-based deformation pattern detector layout, performing a binary classification, has been modified to account for a multiclass deformation pattern classification. This allows us extending the capability of the system which, in addition to detect co-seismic deformation patterns, may also provide information on the earthquake source characteristics. The presented solution will be included in the EPOSAR DInSAR processing chain.

Phase unwrapping (PhU) is a challenging step in multi-temporal (MT) Differential SAR Interferometry (DInSAR) techniques that may have a significant impact on the accuracy of the retrieved displacements. In this paper, we present a simple solution to enhance a previously developed Compressive Sensing (CS)-based PhU approach, allowing us to improve its performance in particularly challenging surface displacement scenarios. The proposed method, without altering the technical framework of the CS-based original solution, specifically targets the refinement of the MT interferometric sequence selection process. In particular, starting from the pivotal role of the Delaunay triangulation in the SAR data acquisitions temporal/perpendicular baseline plane to identify the bulk multi-temporal DInSAR interferograms sequence, we easily enrich this sequence by properly adding highly coherent interferograms which have not been initially selected. The overall study is focused on multi-look L-band interferograms of the SAOCOM-1 constellation, relevant to the Stromboli Island (Italy), and a comparative analysis between the results obtained by applying the enhanced method and its original version is presented, showing the improved capability of the former.

Over the past 30 years it has been widely demonstrated the effectiveness of the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to retrieve surface deformation information relevant to tectonically active areas. However, this technique exhibits some limitations due the presence of possible decorrelation effects, phase unwrapping errors, and artefacts due to the temporal/spatial variability of the atmospheric conditions between the SAR acquisition pairs exploited to generate the interferograms and the corresponding deformation maps or time series. A challenging situation may arise when an earthquake event occurs and a co-seismic DInSAR deformation map is generated to quickly support risk management operations. In this scenario, having an automatic process reducing the uncertainties of the retrieved, DInSAR-based, surface deformation information could highly improve the quality of the products made available to the scientific community and of the service provided to the national disaster recovery authorities. We present in this work a solution, based on standard CNN architectures embedded in the DInSAR processing chain of the EPOSAR service, developed within the European Plate Observing System (EPOS) Research Infrastructure, to automatically identify co-seismic ground deformation patterns.

We present in this work some advances of the Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach, allowing us to effectively process the recently available L-band SAR images acquired by the Argentinian SAOCOM-1 constellation. This activity has been carried out within the DInSAR-3M project, co-funded by the Italian Space Agency, which is aimed to develop technical solutions for the joint exploitation of multi-frequency (X-, C- and L- band) and multi-platform (spaceborne and airborne) DInSAR measurements. The main goal is the generation of surface deformation time series and corresponding mean displacement velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena.The assessment of the developed advances has been carried out by processing several SAOCOM-1 SAR datasets acquired over Italy between 2019 and 2022.

In this paper, we present an innovative Phase Unwrapping (PhU) procedure for Multi Temporal DInSAR techniques, based on the Extended Minimum Cost Flow technique schema. The developed algorithm benefits from the Compressive Sensing (CS) theory. In particular, the procedure consists of the cascade of three steps. At first, the set of spatially connected arcs between close pixels is identified by selecting the points to be unwrapped and linking them through the Delaunay algorithm; in the second step, the temporal PhU of all the arcs is carried out by using the CS approach and an L1-norm estimator. In the last step, the algorithm uses the unwrapped arcs to perform spatial PhU of each interferogram using MCF technique. To assess the performance of the developed approach, we analyze the area related to Stromboli volcano (Italy) by processing the dataset acquired by Sentinel-1 constellation over descending orbit from 2016 to 2021.

We present in this work a solution to identify Phase Unwrapping (PhU) errors in redundant sequences of small baseline Differential SAR Inteferometry (DInSAR) interferograms like those exploited by the Small Baseline Subset (SBAS) approach, which allows the retrieval of displacement time series. In particular, the temporal information is exploited through the temporal coherence parameter, which represents a point-like quality indicator of the PhU solutions within advanced DInSAR methods like the above mentioned SBAS technique. First of all we show that this parameter needs to be carefully considered because it may lose sensitivity when the number of images of the exploited dataset increases. Subsequently, we explore a different use of the temporal coherence, moving from a single valued parameter to a time-dependent function. This permits to regain sensitivity and to reduce the number of interferograms eligible to be corrected. The last part of the work is devoted to present a case study where the proposed solution allows the identification of PhU errors.

We present in this work the first results of the project referred to as DInSAR-3M, which is aimed to generate surface deformation time series and mean velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena. The project objective will be pursued through the use of the advanced Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach and the development of a solution for the integration of multi-frequency (X-, C- and L- band) and multi-platform DInSAR measurements. In the following, we present the first results focused on the interferometric exploitation of the recently available L-band SAR images acquired by the Argentinian SAOCOM constellation.

We present in this paper a comparative analysis focused on the estimation and removal of the Atmospheric Phase Screen (APS) from DInSAR deformation measurements relevant to volcanic areas characterized by significant displacements and topography. These are scenarios where it is often difficult to separate the interferometric phase component due to atmospheric artifacts from the one related to the actual ground deformation. In particular, we investigate the APS correction performance achieved by applying two different approaches: i) the first relying on the exploitation of the external meteorological ERA-5 data, ii) the second based on filtering out atmospheric contributions from the DInSAR time series by exploiting their statistical properties both in space and in time. For the experimental analysis we analyze very large Sentinel-1 datasets acquired over two volcanic areas particularly challenging from the point of view of the APS estimation: La Palma island (Canary, Spain), focusing on the last eruption occurred on 19 September 2021, and Mt. Etna (Sicily, Italy).

In a recent publication, Ansariet al.(2021) claimed (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommended a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors.” In particular, among various techniques, Ansariet al.(2021) identified the solution presented by Manuntaet al.(2019) as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansariet al.(2021) is not correct (at least) for what concerns the mentioned approach discussed by Manuntaet al.(2019). In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansariet al.(2021), we demonstrate that the generated InSAR products do not show any significant bias.

In this work, we present the ground deformation analysis of the Italian Peninsula carried out via the Sentinel-1 (S-1) Parallel Small BAseline Subset (P-SBAS) approach. In particular, we generate surface deformation time-series and the corresponding mean deformation velocity maps by considering the overall archive of S-1 data acquired from both ascending and descending orbits during the March 2015 - December 2018 period. Moreover, we perform a large-scale comparison of the retrieved results with the deformation measurements of the available GPS stations deployed on the Italian territory and provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL). Finally, we properly combine the ascending and descending deformation time-series in order to compute the vertical and east-west components of the retrieved surface displacements. The achieved results demonstrate the capability of the P-SBAS processing chain to efficiently process large S-1 datasets to investigate ground deformation dynamics of large portions of the Earth surface.

We present the implementation of a global archive of Differential Synthetic Aperture Radar Interferometry (DInSAR) co-seismic deformation maps. The archive has been generated by automatically processing all the Copernicus Sentinel-1 data spanning about 300 significant (at least Mw > 5.5) earthquakes all over the Earth. An empirical relation between magnitude and epicenter depth was considered to limit the study to those earthquakes that can likely induce ground deformation. DInSAR processing has been carried out within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, to benefit of high processing capacity. The generated DInSAR results are then made freely and openly available to the Solid Earth scientific community through the European Planet Observing System (EPOS) Research Infrastructure.

We present an automatic pipeline implemented within the Amazon Web Services (AWS) Cloud Computing platform for the interferometric processing of large Sentinel-1 (S1) multi-temporal SAR datasets, aimed at analyzing Earth surface deformation phenomena at wide spatial scale. The developed processing chain is based on the advanced DInSAR approach referred to as Small BAseline Subset (SBAS) technique, which allows producing, with centimeter to millimeter accuracy, surface deformation time series and the corresponding mean velocity maps from a temporal sequence of SAR images. The implemented solution addresses the aspects relevant to i) S1 input data archiving; ii) interferometric processing of S1 data sequences, performed in parallel on the AWS computing nodes through both multi-node and multi-core programming techniques; iii) storage of the generated interferometric products. The experimental results are focused on a national scale DInSAR analysis performed over the whole Italian territory by processing 18 S1 slices acquired from descending orbits between March 2015 and April 2017, corresponding to 2612 S1 acquisitions. Our analysis clearly shows that an effective integration of advanced remote sensing methods and new ICT technologies can successfully contribute to deeply investigate the Earth System processes and to address new challenges within the Big Data EO scenario.

We present an unsupervised and automatic system for volcano deformation monitoring via the Copernicus Sentinel-1 data. The system relies on the Parallel Small BAseline Subset (P-SBAS) approach, permitting us to generate updated displacement time series at every new Sentinel-1 acquisition over a selected area of interest in a fast and accurate way. The service is currently operative to monitor the main active Italian volcanoes in the framework of cooperation with the Italian Department of Civil Protection. The system is potentially extendable to every area on the Earth, thus making it suitable for surface displacement monitoring of a large variety of phenomena. Finally, the obtained results are made available to the scientific community through the EPOS Research Infrastructure.

In this work we present a cloud-computing based strategy for generating surface displacement time series and mean deformation velocity maps of very large areas by exploiting Sentinel-1 (S1) data. Our approach relies on the simultaneous exploitation of a fast access to the S1 data archives, High Performance Computing resources and external geodetic data.The presented pipeline is based on an advanced cloud-computing implementation of the differential SAR interferometry (DInSAR) Parallel Small BAseline Subset (P-SBAS) processing chain, which allows the fully unsupervised processing of huge Interferometric Wide Swath (IWS) Sentinel-1 data volumes.In particular, for what concerns the S1 data archives, we benefited from the NASA's Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), which stores and distributes S1 SAR data from Amazon Web Service (AWS) for advancing Earth science research. The ASF-DAAC allowed us to reach a very high performance level in terms of downloading time and system reliability. Moreover, we exploited the geodetic measurements provided from the Magnet + Global GPS Network Map of the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL), which supply GPS measurements daily updated and continuosly available. The GPS measurements were used to account for regional trends and to better discriminate the low (tectonic) and high (local) frequency deformation patterns.The presented solution is highly scalable and has been migrated to the Amazon Web Service (AWS) environment. It runs out along an automatic routine to generate the mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area.The developed pipeline has been tested on ascending and descending Sentinel-1 archives acquired over a large area of Southern California (US), which extends over about 150,000 square kilometers.

The Differential Synthetic Aperture Radar Interferometry (DInSAR) remote sensing technique permits to investigate the temporal behaviour of the detected displacements through the generation of the deformation time-series. In this scenario the Phase Unwrapping (PhU) is a crucial point where several errors could occur since the problem is intrinsically ill-posed. In this paper we present a technique to correct unavoidable PhU errors and limit their impact in the final deformation time-series. The proposed approach works pixel-by-pixel and is based on the combination of an L1-norm inversion for the identification of the possible PhU errors and a genetic algorithm (GA) for the search of the best fitting solution. We include the technique in the Small Baseline Subset (SBAS) DInSAR processing chain to correct the results of the Extended Minimum Cost Flow algorithm, but in principle it can be used as a correction step after a generic PhU procedure used in SBAS. Results from MonteCarlo simulations are shown in this paper while real data cases will be presented at the conference.

We present an unsupervised tool for the automatic generation of co-seismic displacement maps by using satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. The tool relies on the use of Sentinel-1 SLC Interferometric Wide Swath (IWS) data and main earthquake information, the latter retrieved from on-line global catalogs. In particular, the tool collects location, depth and magnitude of a seismic event and immediately triggers the data download and the interferometric process over the area affected by the earthquake. The generated DInSAR results (mainly interferograms and displacement maps) are then made available to the Solid Earth scientific community through the EPOS Research Infrastructure. Moreover, the implemented system is also used to serve the Italian Department of Civil Protection in case of main seismic events in Italy. Finally, due to the Sentinel-1 characteristics, the obtained results can contribute to the generation of a global database of DInSAR co-seismic displacement maps.

We exploit large DInSAR and GPS datasets to create a 4D image of the magma transfer processes at Mauna Loa volcano from 2005 to 2015. The datasets consists of 23 continuous GPS time series and 307 SAR images acquired from ascending and descending orbits by ENVISAT and COSMO-SkyMed satellites. The joint use of SAR data acquired from different orbits together with deformation data from GPS networks and geological information can significantly improve the constraints on the geometry and location of the sources responsible for the observed deformation. The analysis of these datasets has been realized using an innovative method that allows to image a complex configuration of different type of sources. The results suggest that the deformation pattern observed from 2005 to 2015 has been controlled by three deformation sources: the ascent of magma along a conduit, the opening of a dike and the slip along the basal decollement.

We investigate the deformation of Mt. Etna volcano (Italy) by exploiting the DInSAR technique referred to as the Small BAseline Subset (SBAS) algorithm. In particular, we take advantage of the multi-sensor data processing capability of the SBAS algorithm which allows us to generate Mt. Etna mean deformation velocity maps and the corresponding time series in the last twenty-five years. To achieve this task, we exploit different set of SAR data collected by ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 radar sensors in the 1992-2018 time interval. We, also, show the deformation pattern induced by the recent seismo-volcanic crisis occurring from 24 to 27 December 2018. The main results show that the East-West displacement map highlights the most significant entities, whose maximum values exceed 30 cm towards the West and 40 towards the East on the summit of the volcano. In this scenario, the measurements permit to model the geometry and characteristics of the causative deformation sources; preliminary results suggest the presence of two different sources: a shallow dyke feeding the eruptive activity at summit craters and a major strike-slip fault/s on the southeastern flank nucleating the 26thmainshock.

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

In this work we present a fully parallel SBAS-DInSAR processing chain for the effective and massive generation of Sentinel-1 (S-1) Interferometric Wide Swath (IWS) deformation time-series. The pursued strategy to develop such a parallel processing chain fully benefits from the data acquisition characteristics of the TOPS mode, consisting of a series of bursts that can be considered as separate images to be independently processed. Such a data structure fosters the intrinsic parallelization of the S-1 IWS interferometric data processing, which can automatically be carried out through the exploitation of high-performance distributed computing infrastructures, and of efficient algorithms for tackling the huge data flow provided by the S-1 constellation. In order to demonstrate the capability of the presented S-1 P-SBAS processing chain to deal with the huge amount of data acquired by the S-1 constellation, we process a very extended dataset acquired over Italy from which we compute the mean velocity maps and corresponding deformation time-series.

In this work we implement a completely automatic interferometric processing chain, based on the well-known advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the generation of Sentinel-l (S-1) Interferometric Wide Swath (IWS) deformation mean velocity maps and time-series of very wide areas, implemented within the Amazon Web Services (AWS) Elastic Cloud Compute environment. Our processing chain consists of the initial data query to the S-1 archive that we created on the AWS S3 storage, then the data transfer to the AWS computing nodes, the data processing and, finally, the transfer of the obtained interferometric results back to the original S3 storage. In order to demonstrate the capability of the implemented Cloud-based processing chain to deal with massive amount of data, we focus our analysis on the whole Italian territory by processing all the available data acquired both from ascending and descending orbits within the October 2014 - March 2017 time interval. As final result we combine the retrieved LOS displacements in order to compute the mean velocity maps and time-series of the vertical and East-West surface deformation components.

In this work we present an advanced interferometric processing chain, which is based on the DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, for the massive processing of SENTINEL-1 (S1) Interferometric Wide Swath (IWS) data. The P-SBAS S1 processing chain produces surface deformation time series, and the relevant mean velocity maps, in automatic and systematic way by efficiently exploiting Cloud Computing infrastructures, thus allowing us to perform DInSAR analyses at very large scale in reduced time frames. As experimental results, the overall mean deformation velocity map relevant to the Central and Southern Italy zone (from Lazio to Sicily), generated by processing in parallel about 300 S1 acquisitions within the Amazon Web Services Cloud Computing platform, is presented. Moreover, the displacement time series of some pixels located in volcanic deforming areas such as the Campi Flegrei Caldera (Napoli Bay area) and the Mt. Etna (Sicily) are shown.

In this work we present an efficient interferometric processing chain, based on the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the generation of Sentinel-1A (S1A) Interferometric Wide Swath deformation time-series, which is able to exploit distributed computing architectures. The presented S1A P-SBAS processing chain has been successfully implemented within the ESA Geohazard Exploitation Platform to provide an on-demand automatic service for the unsupervised generation of P-SBAS displacement time-series. To give an idea of the effectiveness of the presented S1A processing chain, as a preliminary result we show a 12-days interferometric analysis at continental scale, carried out by exploiting 150 S1A interferometric pairs acquired over Europe for an overall covered area of about 7,500,000 km2.

We present a case study on the migration to a Cloud Computing environment of the advanced differential synthetic aperture radar interferometry (DInSAR) technique, referred to as Small BAseline Subset (SBAS), which is widely used for the investigation of Earth surface deformation phenomena. In particular, we focus on the SBAS parallel algorithmic solution, namely P-SBAS, that allows the production of mean deformation velocity maps and the corresponding displacement time-series from a temporal sequence of radar images by exploiting distributed computing architectures. The Cloud migration is carried out by encapsulating the overall P-SBAS application in virtual machines running on the Cloud; moreover, the Cloud resources provisioning and configuration phases are implemented in an automatic way. Such an approach allows us to preserve the P-SBAS parallelization strategy and to straightforwardly evaluate its performance within a Cloud environment by comparing it with those achieved on a HPC in-house cluster. The results we present were achieved by using the Amazon Elastic Compute Cloud (EC2) of the Amazon Web Services (AWS) to process SAR datasets collected by the ENVISAT satellite and show that, thanks to the Cloud resources availability and flexibility, large DInSAR data volumes can be processed through the P-SBAS algorithm in short time frames and at reduced costs. As a case study, the mean deformation velocity map of the southern California area has been generated by processing 172 ENVISAT images. By exploiting 32 EC2 instances this processing took less than 17 hours to complete, with a cost of USD 850. Considering the available PB-scale archives of SAR data and the upcoming huge SAR data flow relevant to the recently launched (April 2014) Sentinel-1A and the forthcoming Sentinel-1B satellites, the exploitation of Cloud Computing solutions is particularly relevant because of the possibility to provide Cloud-based multi-user services allowing worldwide scientists to quickly process SAR data and to manage and access the achieved DInSAR results.

Often scientific applications are characterized by complex workflows and large datasets to manage. Usually, these applications run in dedicated high performance computing centers with low-latency interconnections which require a consistent initial cost. Public and private cloud computing environments, thanks to their features such as customized computing environments, flexibility, and elasticity represent a valid alternative with respect to HPC clusters in order to minimize costs and optimize processing. In this paper the migration of an advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methodology for the investigation of Earth surface deformation phenomena to the Amazon Web Services (AWS) cloud computing environment is presented. Such a technique which is referred to as Parallel Small Baseline Subset (P-SBAS) algorithm allows producing mean deformation velocity maps and the corresponding displacement time-series from a temporal sequence of radar images. Moreover, an experimental analysis aimed at evaluating the P-SBAS algorithm parallel performances which are achieved within the AWS cloud by exploiting two different families of instances and by taking into account different I/O and network bandwidth configurations is presented.

In the present radar remote sensing scenario, the huge availability of SAR data acquired by a number of satellite constellations is a key point for investigating Earth's surface at large scale. In particular, techniques as Differential SAR Interferometry (DInSAR) could strongly benefit from such data availability for measuring ground displacement at global scale. However, to efficiently and effectively exploit such amount of Big DInSAR Data, the development of new algorithms and techniques as well as the use of appropriate High Performance Computing infrastructure is becoming mandatory. In this work we present a number of case studies based on the recently proposed Parallel Small BAseline Subset (P-SBAS) DInSAR algorithm, that allows generating surface displacement maps in automatic and unsupervised way, aimed at providing effective solutions to the increased DInSAR data volume processing.

This work is aimed at describing the development of an efficient interferometric processing chain, based on the well-known advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) algorithm referred to as Small BAseline Subset (SBAS) technique, for the generation of Sentinel-1A (S1-A) Interferometric Wide Swath (IWS) deformation time-series. Due to the TOPS mode characterizing the IWS acquisitions, the existing SBAS processing chains was properly adapted with new procedures for efficiently handling the S1-A data. The developed SBAS-DInSAR chain has been tested on both S1-A and TOPS RadarSAT-2 interferometric dataset, clearly demonstrating the capability of the developed SBAS-DInSAR processing chain to effectively investigate land subsidence phenomena affecting large areas.

This paper presents the integration of the advanced Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) within the ESA's Grid Processing on Demand (G-POD) environment in the framework of ESA Geohazards Exploitation Platform (GEP). The aim of this activity is to set up a scientific service that allows, in unsupervised manner, the generation of SBAS-DInSAR products, such as surface mean deformation velocity map and the corresponding time series. In particular, such a web tool is aimed at efficiently exploit the huge ESA's SAR data archives (ERS and ENVISAT), giving a support to scientific users, especially those non-expert of SAR data processing, for interferometric analysis in a short time frame.

The current Remote Sensing scenario is characterized by the availability of huge archives of SAR data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we discuss the migration of the DInSAR technique referred to as Parallel Small BAseline Subset (P-SBAS), which is used for Earth's surface deformation investigation, to the Amazon Web Services (AWS) public Cloud Computing environment. An experimental analysis aimed at evaluating the P-SBAS scalable performances that are achieved within the Cloud environment is presented. The achieved results show very good parallel performances and allow us to identify the major bottlenecks that can hamper such behavior when the amount of data to process highly increases. Accordingly, we present an advanced P-SBAS implementation that is designed to overcome the identified bottlenecks. The experimental analysis is carried out by processing both Envisat and COSMO-SkyMed datasets and by exploiting both a High Performance Computing cluster as well as AWS public Cloud.

The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.

This work presents the Differential SAR Interferometry and pixel-offset analysis on the event landslide that struck Montescaglioso town (Matera, southern Italy) on December 3rd, 2013. The event occurred after adverse weather conditions that produced a ground displacement of several meters, causing a severe emergency situation. The analysis has shown the presence of two main directions of motion: a major and a minor movement along the South-SouthWest and South-SouthEast directions. The pixel-offset results are well in agreement with both the magnitude and the deformation mechanisms that have been identified and mapped during field observations.

The effective exploitation of the available huge SAR data archives in reasonable time-frames has motivated the development of P-SBAS, a parallel computing solution for the SBAS (Small BAseline Subset) processing chain. Hence, P-SBAS parallel solution represents a valuable tool for the analysis of the complex phenomena characterizing the surface deformation dynamics of Earth large areas, since it permits to exploit the parallelism offered by the modern computational platforms. In this paper, the performance of the parallel algorithm P-SBAS is investigated. The quantitative evaluation of the computational efficiency of the implemented parallel prototype in terms of achieved speedup is addressed to demonstrate the effectiveness of the proposed approach. An experimental analysis has been carried out on real data by employing a computational platform comprising 32 processors. In particular, the performance analysis has been conducted by exploiting different SAR datasets pertinent to different sensors (Envisat and Cosmo Sky-Med) and the factors limiting the inherent scalability are discussed.

In this paper the analysis of time series of fractal dimension maps generated from multi-pass SAR images is dealt with. The objective is twofold: on the one hand such an analysis is addressed to assess the performance of the algorithm for the fractal dimension estimation from a single SAR image, i.e., to verify the fractal estimation efficiency on natural scenes within the multiple sensor passes. On the other hand, by exploiting the statistics evaluated starting from the fractal time series, a final fractal dimension map, including only the areas in which the fractal estimation is efficient and strongly denoised from the speckle effect, is obtained. The analysis has been performed on a Cosmo-SkyMed data-set of 42 stripmap images spanning the time period from October 2009 to December 2012, acquired over the Somma-Vesuvius volcanic complex (South Italy), which is in a quiescent stage since the last eruption occurred in 1944.