Monitoring nonlinear and fast deformation caused by underground mining exploitation using multi-temporal Sentinel-1 radar interferometry and corner reflectors: application, validation and processing obstacles

In this study, Differential Interferometric Synthetic Aperture Radar Interferometry (DInSAR) of artificial Corner Reflectors (CRs) were validated in the area of fast and nonlinear deformation gradient caused by active coal longwall exploitation. Three Sentinel-1 datasets were processed using conventional DInSAR, Persistent Scatterer Interferometry (PSI), and Small BAseline Subset methods implemented in ENVI SARscape (TM). For evaluation, leveling and Global Navigation Satellite System (GNSS) measurements were used. Considering the challenge of snow cover, the removal of all winter images was not a successful strategy due to the long temporal baseline and strong movement, which cause phase unwrapping problems and underestimate the real deformation. The results indicate that only conventional DInSAR and SBAS with low network redundancy allow us to capture maximal deformation gradient and the root mean square error calculated between the CRs and the ground truth is on the level of 2-3 cm for the vertical and easting deformation component, respectively. For the small deformation gradient represented by the permanent GNSS station (4 cm/year), all SBAS techniques appeared to be more accurate than DInSAR, which corresponds to higher redundancy and better removal of the atmospheric signal. In contrast, DInSAR results allowed to capture information about two subsidence basins, which was not possible with SBAS and PSI approaches.

Automated summary

This study validated the use of Differential Interferometric Synthetic Aperture Radar Interferometry (DInSAR) with artificial Corner Reflectors (CRs) in an area of fast and nonlinear deformation gradient caused by active coal longwall exploitation. Three Sentinel-1 datasets were analyzed using conventional DInSAR, Persistent Scatterer Interferometry (PSI), and Small Baseline Subset (SBAS) methods. Results showed that only conventional DInSAR and SBAS with low network redundancy were able to capture the maximal deformation gradient with an error rate of 2-3 cm in the vertical and easting deformation component. SBAS techniques were found to be more accurate than DInSAR for small deformation gradients, while DInSAR provided information about subsidence basins that SBAS and PSI approaches were unable to capture.