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We present a tool for the automatic generation of co-seismic Differential Synthetic Aperture Radar Interferometry (DInSAR) products by using space-borne SAR data. In particular, the implemented tool relies on the large availability of Sentinel-1 SAR data and on-line earthquake catalogues (e.g. USGS, INGV) to generate co-seismic Line Of Sight (LOS) interferograms and displacement maps. The processing is triggered by the occurrence of a main seismic event, according to the accessible earthquake catalogues. The tool automatically retrieves all the needed SAR acquisitions that cover a defined area across the epicentre and generates the DInSAR products that will be then openly available through the European Plate Observing System (EPOS) portal. Moreover, the possibility to implement the presented tool into the upcoming Copernicus Data and Information Access Services (DIAS) will significantly reduce the product processing time, thus implying a faster product generation and delivery. Accordingly, such a tool not only will contribute to expand the use of DInSAR products in the geoscience field, but also will play a key role on the support of the Civil Protection authorities during the management of seismic crisis.

We present in this work a methodology for computing surface deformation time series and mean velocity maps of large areas. Our approach relies on the availability of a multi-temporal set of synthetic aperture radar (SAR) data collected from ascending and descending orbits over an area of interest, and also permits us to estimate the vertical and horizontal (East-West) components of the Earth's surface deformation. The adopted methodology 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 unsupervised processing of large SAR data volumes, from the raw data (level-0) imagery up to the generation of the corresponding DInSAR time series and maps. The solution presented, which is highly scalable, has been tested on ascending and descending ENVISAT SAR archives comprising approximately 400 GB of data, which have been acquired over a large area of southern California (US) that extends over about 90,000 km 2 . Such an input dataset has been processed in parallel by exploiting 280 computing nodes of the Amazon Web Services Cloud environment. The overall processing lasted about 8 h and cost approximately $1900 USD. Moreover, to produce the final mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area, we also took advantage of the information available from external GPS measurements that permit us to account for possible regional trends not easily detectable by DInSAR and to refer the P-SBAS measurements to an external geodetic datum. The results presented clearly demonstrate the effectiveness of the proposed approach that paves the way to the extensive use of the available ERS-1/2 and ENVISAT SAR data archives. Furthermore, the proposed methodology can be particularly suitable to deal with the very large data flow provided by the Sentinel-1 constellation, thus permitting the extension of the DInSAR analyses at a nearly global scale.