Value Added Products based on Nationwide Sentinel-1 Persistent Scatterer Interferometry Datasets in the Framework of the Ground Motion Service Germany

Advanced interferometric SAR processing techniques are able to detect and monitor various surface displacements caused by e.g. soil compaction, soluble and swelling rock formations, groundwater extraction, natural gas extraction, mining, cavern storage operation and landslides. These processes can cause damage to buildings and infrastructure, affect ecosystems and agriculture or affect the economic use of the geological underground e.g. by influencing the hydrogeological setting. Despite the maturity of the PSI technology it is rarely used in operational workflows of the German user community (e.g. from responsible authorities). Nevertheless, SAR data availability is a precondition for the operational use. From this perspective, Copernicus Sentinel-1 is a game changer, ensuring Synthetic Aperture Radar (SAR) data availability for almost the entire Earth, until at least 2030. In order to support the operational use of this technique the "Ground Motion Service Germany" is currently established by the Federal Institute for Geosciences and Natural Resources (BGR).

The service uses Persistent Scatterer Interferometry (PSI) datasets of the entire nation (approx. 360,000 km²). The PSI datasets are based on Sentinel-1 SAR data acquired from ascending and descending orbit. More than 200 continuous Global Navigation Satellite System (cGNSS) stations are used to calibrate and validate the PSI datasets. In order to cover the entire nation, the mapping coverage is increased by mosaicking PSI datasets from adjacent SAR image stacks. Numerous pilot studies are performed to verify the PSI datasets and to improve the usability. This presentation gives an overview of the Ground Motion Service Germany, shows the characteristics of the nationwide PSI datasets and finally highlights the main findings of selected case studies. Value added products are generated to support the usability of the original Line-of-Sight point cloud. For this reason 2D-motion decomposition to estimate the vertical and East-West velocity vectors and cluster analysis to automatically extract active displacement areas are applied. These products are demonstrated and discussed with respect to the case studies.