MAPPING THE SURFACE DEFORMATION AT NATIONAL SCALE THROUGH THE AWS CLOUD IMPLEMENTATION OF THE S1 P-SBAS PROCESSING CHAIN

This work is aimed at showing that an effective integration of advanced remote sensing methods and new ICT technologies allows the full exploitation of large volumes of Earth Observation (EO) data, contributing to deeply investigate the Earth System processes and to address new challenges within the Big Data scenario. In particular, 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.