*5.3. Development of InSAR Deformation Monitoring in a Wide Area*

At present, most wide-area InSAR deformation monitoring projects use the TS–InSAR algorithm to resolve the deformation time series of all highly coherent monitoring points in each frame [10,11,13,17]. Even though different multi-looking ratios for the WSA and the ROI were used to improve the efficiency of data processing by controlling the spatial resolution of the results [14], these methods are still not out of the scope of time-series deformation calculation. Moreover, if the strategy of reducing spatial resolution is not optimized, it will cause repeated calculations and reduce monitoring efficiency. The deformation rate is usually used to detect potential geohazards in a wide area [38]. Therefore, the deformation

time series of some points is unnecessary, especially for stable areas. Hence, calculating the deformation time series at all monitoring points wastes computing resources and labor costs, and produces lots of redundant results. For example, deformation areas account for about 2.4‰ of the total monitoring area in the Turpan–Hami basin. WAVS–InSAR only calculates the deformation rate at each monitoring point in the WSA. Reducing the time dimension of the wide-area deformation results can greatly improve the efficiency of the multitemporal InSAR solution, especially for a lot of InSAR frames in the WSA. Spatial distribution and area of deformation are detected by an adaptive deformation detection method combined with the obtained wide-area deformation rate. After that, high-precision time-series monitoring is only done in the ROI to obtain effective fine deformation results.

For variable-scale deformation results, the WAVS–InSAR strategy proposes a novel variable-scale deformation product organization structure, i.e., it shows the deformation information at the stable surface with low-spatial-resolution deformation rate, while the ROI has a high-spatio-temporal-resolution deformation time series. This structure reduces the amount of deformation results in the stable regions of the WSA, locates the ROI efficiently, and improves the spatial and temporal dimensions of the deformation in the ROI, which is convenient for the calculation, storage, display, and interpretation of the deformation results.

As the SAR satellites and InSAR data increase, InSAR deformation monitoring projects will produce many monitoring results. In the future, wide-area InSAR deformation monitoring projects should be object-oriented, integrating different deformation monitoring to obtain deformation results of multidimensional and high spatio-temporal resolution, and ultimately form a set of universal deformation products. The data-processing strategy and deformation product organization structure proposed in WAVS–InSAR will greatly improve deformation monitoring efficiency and reduce the storage space of massive In-SAR monitoring data, which may become a standardized data-processing procedure and data-storage format for future wide-area InSAR deformation products.

#### **6. Conclusions**

In this study, we proposed a variable-scale InSAR ground-deformation detection strategy and a deformation product organization structure for wide-area monitoring, namely WAVS–InSAR. This strategy efficiently obtains the deformation rate in the WSA, and uses an adaptive deformation detection method to process the wide-area deformation rate and obtain the spatial distribution and area of the deformation areas (ROI). High-precision time-series monitoring is then only done in the ROI, to obtain effective fine deformation results. Therefore, we can produce variable-scale deformation products in the WSA that consist of low-spatial-resolution deformation rates in stable regions, and fine monitoring results in the ROI.

The proposed WAVS–InSAR was used to monitor wide-area deformation in the Turpan–Hami basin, which has an area of 277,000 km2. The results show that there are 32 deformation regions with an area of more than 1 km2 and a deformation magnitude of more than 2 cm/year. The detected deformation areas account for about 2.4‰ of the total monitoring area. The SFM–def region is selected as an application demonstration area of the ROI to carry out fine monitoring of the deformation time series. We obtain the long-term and multidimensional deformation of this area from 2007 to 2010 and from 2015 to 2020 using improved IPTA and MSBAS technologies.

The subsidence funnel center in the SFM–def region moved from northwest to southeast during 2007 to 2020. Based on the variable-scale deformation products and the information regarding hydrogeology, land cover and human activities, we analyze the causes of ground subsidence. Tectonic faults have blocked the water supply in the SFM–def region. The rapid development of facility agriculture has increased the water demand for irrigation. To solve this problem, groundwater has been overexploited. The aquifers in the oasis plain in the SFM–def region are in a state of net deficit. Increased demand for water in the upper reaches of Aydingkol Lake has reduced the lake's water supply. Aydingkol Lake has

shrunk dramatically. In addition, there are several deformation areas related to mining in the Turpan–Hami basin.

**Author Contributions:** Conceptualization, Y.W., G.F. and Z.L.; Formal analysis, H.W. and Z.X.; Funding acquisition, Y.W., G.F. and Z.L.; Investigation, J.Z. and J.H.; Methodology, Y.W. and G.F.; Resources, G.F. and Z.L.; Software, Y.W.; Supervision, Z.L., J.Z. and J.H.; Validation, Y.W., S.L. and H.W.; Writing—original draft, Y.W. and G.F.; Writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Natural Science Foundation of Hunan Province (2021JJ30807), the National Natural Science Foundation of China (Nos. 42174039), the National Science Fund for Distinguished Young Scholars (41925016), the Scientific Research Innovation Project for Graduate Students in Hunan Province (CX20200111), and the Fundamental Research Funds for the Central Universities of Central South University (2020zzts168).

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.

**Acknowledgments:** The authors would like to thank the European Space Agency (ESA) for providing free and open Sentinel-1 data, the Japan Aerospace Exploration Agency (JAXA) for providing the ALOS-1/PALSAR images (No: PER2A2N038), and the National Basic Geographic Information Center of China for providing the free and open global Land Cover Data Product (http://www. globallandcover.com/, accessed on 10 July 2022).

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

