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The large availability of Synthetic Aperture Radar (SAR) data collected over the last decade by several satellite missions, such as COSMO-SkyMed, TerraSAR-X and Sentinel-1 constellations, has been pushing toward the present Earth Observation (EO) scenario into a "golden age", which is rapidly moving towards a real Big Data scenario. The widespread use of satellite SAR data have fostered the development of several SAR applications, one of those referred to as Differential SAR Interferometry (DInSAR) technology, which has deeply demonstrated to profitably detect the surface deformations over a wide spatial extent in both natural and anthropic hazard scenarios, through the generation of spatially dense deformation maps with millimetric accuracies. In particular, the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach allows retrieving the temporal and spatial characteristics of the detected displacements at two spatial resolution scales, referred to as regional (spatial resolution in the 30-90 m range) and local (spatial resolution in the 3-10 m range) scales, suitable for a large variety of application fields, from natural hazards (volcano eruptions, seismic events, landslides) to anthropic contexts (urban areas, archaeological sites, oil-gas extraction, structures and transport infrastructures).However, the interferometric processing performed at local scale needs to deal with hundreds of SAR acquisitions at full spatial resolution, i.e. to manage several hundreds of million points; consequently, the processing of such a data amount is particularly heavy from a computational point of view and can not be carried out in reasonable time frames through the traditional (sequential) implementation of the full resolution DInSAR processing chains.Accordingly, to profitably benefit from the current SAR scenario, it is crucial to develop innovative solutions to automatically and efficiently handle large DInSAR data stacks. These solutions are principally based on the exploitation of advanced distributed computing environments, to achieve high efficiency in terms of scalability performances, as well as on the development of much more advanced DInSAR methodologies (and codes) able to effectively maximize the information related to these huge amount of DInSAR data.This work is aimed at describing an innovative DInSAR solution, based on the exploitation of distributed HPC and Cloud Computing environments, which benefits from parallel programming techniques (multi-node, multi-threads, GPUs) implemented within an automatic full resolution P-SBAS processing pipeline. Starting from large SAR datasets acquired by the COSMO-SkyMed constellation, the developed parallel full resolution P-SBAS processing chain allows retrieving in short time frame (less than 24 hours) displacement time series and deformation maps, at the single buildings/infrastructure level, relevant to extended urban areas. The presented experimental results and the related performance analyses are achieved by applying the developed parallel P-SBAS pipeline to a number of large full resolution COSMO-SkyMed datasets acquired over some important Italian cities (e.g. Rome and Naples urban areas).

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.

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.

The North Anatolian Fault (NAF) is one of the most active faults worldwide, extending approximately 1,200 km from Eastern Turkey to the Northern Aegean Sea. During the 20th century series of damaging earthquakes occurred along the NAF, generally propagated westward towards Istanbul; the last one occurred in 1999 at Izmit, a city 80 km away from Istanbul. Within this scenario, the FP7 MARsite project (New Directions in Seismic Hazard assessment through Focused Earth Observation in Marmara Supersite), supported by EU, intends to collect, share and integrate multidisciplinary data (seismologic, geochemical, surveying, satellite, etc.) in order to carry out assessment, mitigation and management of seismic risk in the region of the Sea of Marmara. In the framework of the MARsite project, we performed the analysis and monitoring of the surface deformation affecting the Istanbul mega city by exploiting the large archives of X-band satellite SAR data, made available through the Supersites Initiatives, and by processing them via the advanced multi-temporal and multi-scale InSAR technique, known as the Small BAseline Subset (SBAS) approach. In particular, we applied the SBAS technique to a dataset of 101 SAR images acquired by the TerraSAR-X constellation of the German Space Agency (DLR) over descending orbits and spanning the November 2010 - August 2014 time interval. From,these images, we generated 312 differential interferograms with a maximum spatial separation (perpendicular baseline) between the acquisition orbits of about 500 m., that were used to generate, via the SBAS approach, mean deformation velocity map and corresponding ground time series of the investigated area. The performed InSAR analysis reveals a generalized stability over the Istanbul area, except for some localized displacements, related to subsidence and slope instability phenomena. In particular, we identified: (i) a displacement pattern related to the Istanbul airport, showing a mostly linear deformation trend with a velocity of about 1 cm/yr, although a slowdown effect is observed starting from early 2014, and (ii) a subsidence phenomenon in correspondence to Miniaturk park with a mean velocity value of about 1.5 cm/yr. Moreover, by benefiting from the recent launch of the C-band Sentinel-1A (S1A) satellite (April 2014), developed within the European Commission Copernicus Programme, we performed a more recent InSAR analysis of the Istanbul mega city and its surroundings by exploiting datasets collected during the October 2014 - December 2015 time interval. In such a way, we are able to investigate possible changes in the spatial and temporal deformation rates of the detected displacements over time with the aim to improve the comprehension of the deformation processes already occurred and/or occurring in this area. The obtained results, generated within the MARsite project, are expected to be available to scientific community through the Geohazards Exploitation Platform (GEP), an ESA platform aimed at supporting the exploitation of satellite EO data for geohazards, to be fully compliant with some of the major issues of the Supersites Initiative, as well as to foster the InSAR data sharing within a wider scientific community.