Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series

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.