## 3 Methodology  
The methodology of this study is depicted in Figure 2. Initially, SAR images, precise orbit data, and digital elevation model (DEM) data for the LT-1 and Sentinel-1 satellites are gathered. This is followed by preprocessing to read the data (Figure 2a). DInSAR technology is then utilized to process the data, yielding results in the interferometric phase and coherence (Figure 2b). Subsequently, a comparative analysis examines four aspects: subsidence results, interferometric phase, backscattering intensity, and coherence (Figure 2c). This analysis aims to give a comprehensive evaluation of the mining-induced subsidence monitoring capabilities of the LT-1 satellite data.  
In this study, the multi-looking process was performed by setting the number of looks for LT-1 SAR data as 4 in the range direction and 4 in the azimuth direction, and for Sentinel-1A SAR data as 1 in the range direction and 4 in the azimuth direction. This multi-looking process increases the SNR of the interferograms, thereby improving the quality of coherence estimation. Interferograms were then generated after satisfying the criteria for temporal, geometric baseline, and Doppler differences. The resulting interferograms are considered Stripmap-like interferograms, thus phase filtering and unwrapping were applied. Although several methods exist for unwrapping interferograms, this study utilized the Minimum Cost Flow (MCF) method for both LT-1 and Sentinel-1A data. Other methods were tested and visually compared but were found to be less suited for the study area than the MCF method. To maximize the use of coherent pixels, a compromise value of 0.2 was adopted when employing the MCF method for the unwrapping stage. Consequently, all areas with coherence values lower than the 0.2 threshold were eliminated, as low coherence values can introduce significant noise and reduce the reliability of the final results. Additionally, coherence maps for each interferogram were generated, and Goldstein filters were applied to the interferograms to minimize noise. Goldstein filters are a type of adaptive filter used in the phase-unwrapping process of DInSAR analysis. Their primary purpose is to reduce noise and improve the accuracy of the unwrapped phase by suppressing the effects of atmospheric disturbances and other sources of interference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite** & **Interference Pair** & **Time Baseline (d)** & **Spatial Baseline (m)** \\ \hline LT-1 & 20230623–20230725 & 32 & 262.9 \\ Sentinel-1 & 20230621–20230715 & 24 & 196.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of two types of SAR imagery.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **LT-1** & **Sentinel-1** \\ \hline Imaging Mode & STRIP1 & IW \\ Resolution (Azimuth by range m) & 1.7 / 1.9 & 2.3 / 13.9 \\ Imaging Width (km) & 50 & 250 \\ Polarization & HH & VV \\ Absolute Orbit & \(\backslash\) & 49,083 \\ Incidence Angle (\({}^{\circ}\)) & 35.8 & 39.2 \\ Wavelength (cm) & 23.6 & 5.6 \\ Frequency Band & L & C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic information on different satellite data.  
Figure 2: Technical workflow chart.