Analysis of Groundwater Depletion/Inflation and Freeze–Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

Abstract

This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement.

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

“…In this section, the effectiveness of the proposed method is demonstrated using real spaceborne data acquired by the TerraSAR-X and Sentinel-1A satellites for the Beijing International Capital Airport area. The results are compared with those of the threshold method [23] and the graph theory method [29].…”

“…In the threshold method [23], the spatial and temporal baseline thresholds are set to 275 m and 500 days, respectively. In the graph theory method [29], the corridor bridge area of the terminal is chosen to fit the coherence proxy, and the optimization criterion is set to 4, as in [29].…”

“…The buildings in the white box-marked area of Fig. 11 are rigid structures with similar deformations, so the evaluation strategy proposed in [23] could be adopted for calculating the average high-pass (HP) deformation time-series standard deviation. The evaluation results are listed in Table I.…”

“…Interferometric pairs, at three times the number of SAR SLC images, were selected based on the coherence of high signal-tonoise ratio (SNR) pixels in the featured region [22]. In [23], candidate interferometric pairs were chosen considering a spatial-temporal baseline threshold. Their average coherence was utilized to estimate a coherence threshold by minimizing the relative deformation error.…”

Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation

“…From appropriate interferometric SAR data pairs strategies, selecting appropriate SB interferograms can mitigate the effects of decorrelation in what is known as small baseline subset (SBAS)-InSAR processing. Some approaches for selecting optimal SB interferograms have been previously explored, including the minimum spanning tree [18], simulated annealing [17], semi-automatic [19], and error bound algorithms [20], as well as the graph theory and variance-covariance matrix [21]. In fact, each pixel has different appropriate SB interferograms.…”