# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 4 Discussion  
### Comparison of the Two MT-InSAR Techniques  
In the field of land subsidence monitoring, the MT-InSAR methods used in our research have significant advantages over traditional methods or standard D-InSAR. The establishment of a traditional ground monitoring system requires a lot of manpower and material resources. In Tianjin, the leveling survey monitoring system includes 2100 benchmarks, 12 continuous GPS stations and 12 groups of multilevel layer compression monitoring wells [29]. The coverage of the monitoring area continues to increase, and the leveling survey is conducted every year. Although traditional methods have high accuracy, they are still not suitable for large-scale investigations. The stranded D-InSAR method has its inherent shortcomings. For example, [PERSON] et al. [39] applied a geostatistical approach of combining D-InSAR with in situ leveling measurements to quantify land subsidence in Tianjin. However, because the D-InSAR method cannot address atmospheric effects, only by ensuring a sufficiently high coherence of the associated images can it be combined with a number of in situ leveling data to reduce the measurement errors. Considering the different performance in identifying the coherent points, using only one MT-InSAR method may not necessarily provide a comprehensive understanding of the subsidence in the study area. When studying ground deformation using the MT-InSAR technique,  
Figure 10: Time series of the surface subsidence from the two MT-InSAR methods at selected points. Points **(a1)** and **(a2)** fall on two plants in the emerging industrial parks. Point **(b1)** is located on a new high-rise building, and point **(b2)** on Jinba Railway. Points **(c1)** and **(c2)** are located in the old residential area in the suburbs. [PERSON] et al. [25] found that due to the sparse distribution of the coherent points acquired from PS, it is not easy to analyze the overall regional deformation and trends. The results of different MT-InSAR methods can be mutually confirmed and complement each other. The cross-comparison analysis used in our research further suggested that the subsidence velocities at the same location obtained by PS and SBAS are likely slightly different, which may indicate which areas are more unstable. Therefore, the application of two methods for remote sensing inversion and cross-comparison of their results is helpful for an in-depth understanding of the land subsidence range and its development trend. Similar to other studies, our research proves that the PS and SBAS methods are effective in monitoring land subsidence over a large spatial area [23; 40; 41]. By comparing the outcomes of the two approaches, they proved to be generally in good agreement in identifying the ground subsidence rate and the spatial distribution of the displacement in the Tianjin study area. The interpretation of the observed subsidence rates in Tianjin suggests the presence of deformation signals with different spatial extents and magnitudes, between 2017 and 2019. Due to different algorithm mechanism, the two MT-InSAR techniques could gain different coherent points [8; 11; 23]. The PS technique takes advantage of evaluating stable targets in the multi-temporal interferometric SAR images [13]. Combining the remote sensing images and the distribution data of main roads in the study area, we can see that the PS points are mainly distributed in urban/ suburban buildings, railways (such as G8936, Jinba Railway, Jingshan Railway, Jinpu Railway), expressways (Jinghu Expressway, Jinbao Expressway, for example), bridges and other man-made objects (Figure 11a,b). There are more PS points in the urban area than in the suburban area because of the existence of denser man-made structures. On the contrary, PS points are relatively sparsely distributed in vegetation coverage areas such as farmland mainly characterized by the cultivated land and crops, while there are almost no PS points in rivers and lakes (Figure 11b).