INTENSIVE AND SYSTEMATIC SENTINEL-1 SBAS-DINSAR PROCESSING FOR DEFORMATION TIME-SERIES GENERATION

Sentinel-1A (S1A) is the first of a family of satellites designed to provide a satellite data stream for the European environmental monitoring program Copernicus, previously known as GMES (Global Monitoring for Environment and Security). The S1A satellite, which has been launched on April 3, 2014, provides scientific community with C-Band SAR data collected in continuity with the first generation ERS-1/2 and ENVISAT missions, guaranteeing further enhancements in terms of revisit time, coverage, timeliness and reliability of service. In particular, the SAR Instrument is designed to operate over land with the innovative acquisition mode referred to as Terrain Observation with Progressive Scans (TOPS), by means of which S1A Interferometric Wide Swath (IWS) scenes are being collected. TOPS mode is quite similar to the ScanSAR one, since during the acquisition time the antenna beam is switched cyclically among different sub-swaths, allowing a significant improvement of the range coverage at expenses of the azimuth resolution. The IWS acquisitions are specifically collected to carry out interferometric analyses, through Differential SAR Interferometry (DInSAR) technique, to analyze and investigate Earth's surface displacements. This work is aimed at describing the development of an advanced and efficient interferometric processing chain, based on the well-known DInSAR algorithm referred to as Small BAseline Subset (SBAS), for the generation of S1A IWS deformation time-series. In particular, the high data stream expected by S1A and the upcoming twin system Sentinel-1B, together with the big size of the data (around 10 times greater than ERS and ENVISAT scenes), make increasingly important the computing efficiency of the DInSAR processing chains. In this framework, the pursued strategy strongly takes into account the data acquisition characteristics of the TOPS mode. Indeed, IWS scenes consist of series of bursts that can be considered as separate acquisitions. This makes a large part of the processing inherently parallel at burst granularity level, thus implying that the processing time can significantly benefit from the availability of large computing resources. The last aspect is of high importance/relevance in the use of SBAS-DInSAR processing chain in operational contexts, where dealing with large amounts of data represents a challenging task. However, one of the main issues occurring when processing large DInSAR datasets, characterized by several hundreds of SAR acquisitions and interferograms, is related to the very high network and Input/Output (I/O) capabilities required to overcome the saturation of the storage resource. Such a bottleneck becomes particularly critical when a high number of processing nodes concurrently work and the access to storage resource has to be shared among tens or hundreds of parallel jobs. The presented SBAS-DInSAR processing chain, properly designed to process large IWS Sentinel-1 datasets, allows us to minimize the above-mentioned limitations and achieve good scalability without the mandatory need of high performance computing resources, by benefiting from a proper distribution of data storage among different nodes. The proposed SBAS-DInSAR processing chain has been properly designed to massively process IWS Sentinel-1 data on a continuous basis, and can represent the starting point of an advanced Sentinel-1 service able to provide users with continuously and systematically updated deformation time-series of very large areas on the Earth's surface.