*Article* **A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China**

**Yuedong Wang 1,2, Guangcai Feng 1,2,\*, Zhiwei Li 1,2, Shuran Luo 1, Haiyan Wang 3, Zhiqiang Xiong 1, Jianjun Zhu 1,2 and Jun Hu 1,2**


**Abstract:** In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for widescale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the *WAVS–InSAR strategy*). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of <sup>≥</sup>1 km2 and a deformation magnitude of greater than <sup>±</sup>2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.

**Keywords:** wide-area deformation; deformation detection; time-series InSAR; stacking; Turpan–Hami basin

#### **1. Introduction**

Advanced microwave remote sensing technology can precisely monitor deformation over wide areas, which helps geohazard surveys of phenomena such as underground fluid development, mineral mining, and landslide. In recent years, fast-developing interferometric synthetic aperture radar (InSAR) technology and abundant available synthetic aperture radar (SAR) data [1–4] has laid the foundation for high-precision and wide-scale InSAR ground-deformation monitoring. InSAR technology has been successfully used to monitor ground deformation at a regional [5–9] and national scale [10–13]. Large-scale geodetic technology, such as InSAR, usually describes the spatial characteristics of ground deformation by deformation rate, and shows deformation development over time using a time series of deformation. The deformation region usually accounts for a small part of the monitoring area [11], so the ground deformation we are interested in only accounts for a small part of

**Citation:** Wang, Y.; Feng, G.; Li, Z.; Luo, S.; Wang, H.; Xiong, Z.; Zhu, J.; Hu, J. A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China. *Remote Sens.* **2022**, *14*, 3832. https://doi.org/ 10.3390/rs14153832

Academic Editors: Paolo Mazzanti and Saverio Romeo

Received: 11 July 2022 Accepted: 6 August 2022 Published: 8 August 2022

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the wide-area monitoring results. At present, the engineering projects to obtain ground deformation in a wide study area (WSA) usually calculate the deformation time series using InSAR datasets covering the whole WSA, using time-series InSAR (TS–InSAR) technology. Even with multiple spatial resolutions, such schemes require a lot of computing resources and storage space, and even then require repeated calculations and provide redundant results, especially in the non-deformation region [14]. Therefore, it is necessary to develop a set of efficient monitoring methods and procedures for wide-area InSAR deformation and a more feasible multi-scale deformation product organization structure in the WSA.

One way to improve the computational efficiency of the TS–InSAR method is to introduce a parallel processing method, which can be realized using high-performance computers (HPC) [12,15–19]. However, the high cost of HPC equipment hinders the popularization of this strategy. Another way is to improve the TS–InSAR method itself, by introducing sequential adjustment theory for real-time InSAR data processing [20–22], introducing a geological model or time-series filtering algorithm for high-dimensional deformation calculation [23–26], or realizing a high-precision TS–InSAR deformation calculation using block solutions [27,28]. These strategies can improve the efficiency of the TS–InSAR solution to a certain extent. However, for wide-area InSAR deformation monitoring, high-precision independent calculation of all InSAR datasets in the WSA will provide many useless time-series results, especially in the non-deformation area. Therefore, it is necessary to develop a demand-oriented multiple spatio-temporal-scale deformation monitoring method, considering the universality of monitoring strategies, computing resources, measurement accuracy, and the efficiency of deformation calculation and interpretation.

The averaging of multiple interferograms (stacking) method was proposed by the authors in [29], which can obtain the ground-deformation rate by averaging the phase of the multitemporal differential InSAR (DInSAR) dataset. Compared with conventional TS– InSAR technologies, such as persistent scatterer (PS) [30], small-baseline subset (SBAS) [31], and interferometric point target analysis (IPTA) [32], stacking only obtains the deformation rate with lower technical requirements and higher computational efficiency. Stacking has been widely used for deformation identification [33–37]. A wide-area deformation monitoring project usually identifies deformation regions based on the ground-deformation rate [38]. For the deformation region, the corresponding deformation time series is extracted to analyze the spatio-temporal evolution of deformation. The deformation time series in stable zones has less information. Therefore, combining stacking and TS–InSAR may contribute to efficient variable-scale deformation monitoring.

In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (WAVS–InSAR). WAVS–InSAR uses stacking technology to quickly calculate the low-spatial-resolution ground-deformation rate over the WSA. Then, an adaptive intelligent recognition algorithm is used to identify the location and area of the deformation regions and determine the regions of interest (ROI). Advanced TS–InSAR technologies are then used to obtain the high-spatio-temporal-resolution deformation time series in the ROI. Finally, the variable-scale InSAR deformation product in the WSA is obtained by a novel variable-scale deformation product organization structure. To verify the proposed WAVS– InSAR strategy, we applied it to the Turpan–Hami basin (about 277,000 km2) in Xinjiang, China. The Turpan–Hami basin is the driest place in China, and has the least rainfall in China. Many tectonic faults, as well as agricultural and mining areas, are scattered across the basin. It is of great significance to obtain the spatio-temporal distribution characteristics of ground subsidence and to investigate the surface deformation related to the active agricultural economy and mineral exploitation in the basin.

The remainder of the paper is organized as follows. We introduce the WAVS–InSAR strategy in Section 2. In Section 3, the general situation of the Turpan–Hami basin, InSAR data, and the data-processing details are briefly described. The variable-scale deformation product in the Turpan–Hami basin is shown in Section 4, followed by the discussion in Section 5. Section 6 presents the conclusions.

#### **2. Methodology**

We first collect all available InSAR datasets covering the WSA, and preprocess all datasets through registration and DInSAR, to generate the multitemporal DInSAR datasets with the same spatial reference data. Then, we apply the WAVS–InSAR strategy to process the multitemporal DInSAR data to obtain variable-scale deformation products in a wide area. The WAVS–InSAR includes the following four modules (Figure 1).

