## 3 Status and Application
### Repeat-Pass Interference
#### 3.1.1 D-InSAR  
In 1989, [PERSON] et al. [21] first proposed the theory of combining interferometry with differential methods to measure surface deformation. They successfully applied this technique to obtain surface deformation data from an irrigation area in California, demonstrating the capability of D-InSAR for high-precision surface deformation monitoring. In 1993, [PERSON] and colleagues [22] conducted a study on the Landers earthquake in California, where they used D-InSAR for the first time to extract co-seismic deformation fields, marking the beginning of InSAR's application in the geosciences. Their results were published in _Nature_, highlighting InSAR's potential and leading to its widespread use in surface deformation monitoring. However, InSAR is highly sensitive to factors such as Atmospheric Phase Screen (APS) and temporal/spatial decorrelation. To overcome these limitations and enable the high-precision, long-term monitoring of surface deformations, researchers began focusing on these challenges after 2001. These efforts ultimately led to the development of the various Multi-Temporal InSAR (MT-InSAR) techniques. The data processing procedure for extracting InSAR deformation information generally includes steps such as the registration of primary and secondary SAR images, generation and filtering of the interferogram, removal of the flat-earth phase, topographic phase removal (i.e., differential processing), phase unwrapping, geometric transformation, and geocoding [23; 24]. The following will focus on the application of D-InSAR in monitoring and analyzing surface deformation caused by seismic activities, volcanic movements,  
Figure 2: Spaceborne SAR system. glacier drift, ground subsidence, landslides, etc. For the description and classification of different types of surface deformation, Table 1 presents typical geophysical phenomena and their corresponding surface deformation characteristics. It can be seen that the deformation characteristics presented by different geophysical phenomena are quite different. Before implementing D-InSAR surface deformation detection, investigating and understanding the temporal and spatial characteristics of the monitoring object can guide the selection of appropriate SAR data and the setting of appropriate processing parameters in the data processing process, and help with the correct interpretation of the deformation field. In fact, during the SAR interferometric modeling process, the SAR interferometric phase can be regarded as the comprehensive contribution of three factors: the reference ellipsoid, topographic undulation, and surface deformation. On the one hand, this is because the low-frequency high-correlation feature of the atmospheric delay phase in spatial distribution can be eliminated by high-pass filtering in a limited research area [4], while the noise phase can be attributed to systematic errors such as sensor thermal noise and can be suppressed by low-pass filtering, densified ephemerides, etc. [25]. Therefore, D-InSAR to measure ground surface deformation is used to model the interferometric phase of SAR images and sequentially remove the flat-earth phase and topographic phase. Of these, the flat-earth phase can be calculated and removed based on the SAR satellite orbit parameters and imaging geometric parameters, and the topographic phase can be modeled and removed based on external DEM data or the topographic data generated by another SAR interferometric pair, according to SAR imaging parameters. Generating differential interferograms is the primary goal of D-InSAR for monitoring ground surface deformation. Figure 3 shows the basic strategy for generating differential interferograms, corresponding to the co-seismic deformation field of the 1995 Sakhalin Island earthquake in Russia [4], which is composed of interferograms formed by JERS-1 SAR data.