A parallel multi-temporal InSAR method for Sentinel-1 large scale deformation monitoring

Inhalt:
InSAR technique has made great achievements in various geodetic areas on the regional scale, its ability to monitor surface deformation with wide areas has been paid more and more attentions in recent years. And benefited from Sentinel- 1 dataset, its wide coverage and abundant acquisitions have made Senitnel-1 very suitable for large scale deformation monitoring. As traditional algorithms meet up with many challenges of time and memory cost, many new parallel algorithms have been developed based on high performance computing environment (HPC), and have been applied in many national scale tasks. In this work, we propose a novel parallel time series Sentinel-1 InSAR framework, which has improved the efficiency of Sentinel-1 data processing greatly, by making use of the unique data structure of Sentinel-1 TOPS mode and by separating different blocks in time series processing. Through tested on the Sentinel-1 IWS data in Beijing region, the proposed method has showed very good performance, and its accuracy can be well certified by comparison with GPS measurements. This automatic and parallel multi-temporal InSAR framework will be very helpful in wide areas subsidence monitoring in North China Plain, with high efficiency and less human cost.