Towards a wide-scale land subsidence product in Eastern states of Australia

The implementation of a wide-land deformation monitoring program in eastern states of Australia, characterized by an extensive landmass and extensive coastlines, necessitates the generation of deformation maps with a significantly larger spatial extent. A pragmatic approach for achieving such coverage involves the integration of multiple interferometric synthetic aperture radar (InSAR) time series results encompassing various tracks and frames. The Sentinel-1 satellite constellation is facilitating the generation of expansive ground surface deformation, while the initial challenge in obtaining such a product arises from the varying spatial coverage of the Sentinel-1 data. To overcome this obstacle, we proposed a novel Sentinel-1 image definition to facilitate consistent interferometric processing. Before the formation of any large displacement map, the inconsistency between different synthetic aperture radar (SAR) image scenes must, nevertheless, be addressed. Several factors contribute to these inconsistencies in observations, such as differences in the angle of observation at the overlapping regions of adjacent image tracks and imprecise estimations within the dataset itself (e.g., burst or swath discontinuities). This study delves into solutions for these challenges, specifically: 1) introducing a computer-vision-based algorithm for InSAR dataset quality assessment and 2) proposing a global least square mosaicking procedure for the amalgamation of tiles from multitracks and frames. Statistical analyses show that better accuracy can be achieved. The mosaicking InSAR product demonstrates the capacity to quantify ground surface changes, which also exhibits a correlation with other geological layers, and the subsidence range of [−7, 10] mm/year shows a strong association with clay content levels ranging from 10% to 30% along the Darling River. Citation: Z. Du et al., "Toward a Wide-Scale Land Subsidence Product in Eastern States of Australia," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-12, 2023, Art no. 5213312, doi: 10.1109/TGRS.2023.3299928.