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.

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 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 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.