## 5 Discussion and Suggestion  
A great performance of the advanced PS-InSAR remote sensing technique for monitoring the railway's subsidence deformation has been shown, which had not been conducted before in the area of interest. By considering the results, C-band data, with its free access opportunity, would be a great choice for evaluating hundreds of kilometers of railway lines. However, for obtaining and focusing detailed information about displacements and representing it with more PSs, X-band data seem more suitable. In [67], the researchers mention that the increased PSI measurements' reliability in relation to the infrastructures is a result of the PSs' higher density. While the density of PSs on the railway line is sensitive to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Study Area-3**} \\ \hline \multirow{3}{*}{(a)} & \multirow{3}{*}{CSK} & Joint Location\#1 & \multirow{3}{*}{(b)} & \multirow{3}{*}{CSK} & Joint Location\#2 & \multirow{3}{*}{CSK} \\  & & S1B/T65 & S1B/T160 & & & & S1B/T65 \\ \cline{1-1} \cline{5-6} \cline{7-  
the orientation of the railways, observations from ascending and descending orbits would be helpful [21; 22], as performed in this study. It can be seen from Figure 11 that in the second area, which is the right half parts of each graph, the subsidence values are close to or around zero, while the behavior of the PSs in this stable area is spatially homogeneous, representing minimal variations in the deformation rates. As a suggestion, the complicated structure of SAR image types and characteristics necessitates careful consideration when assessing appropriate data sources for monitoring railway subsidence. In addition to data selection, it is crucial to perform image processing meticulously to ensure precise computation of deformation and deformation velocities. Phase residual plots, correlation graphs, elevation charts, error diagrams, standard deviation values, etc., should be checked during the analysis to ensure that phase residual errors and atmospheric effects are eliminated or minimized in the process, and if necessary, parameters and thresholds used should be changed, and after that, the analysis should be repeated. It should be noted that this is a relative technique, i.e., it is based on the behavior of the reference point, and the process goes on according to the filter values and parameters used. In addition, the whole structure of the study region, the number of images used, and the band characteristics also play an effective role in the analyses. Although the high correlation between the results of SAR datasets is important for validation, it is unrealistic to expect exactly the same result. The reason for this is that the X-band and C-band characteristics are different, which leads to the formation of different numbers of PSs by giving different responses to deformation analyses over the region. This causes a slight difference in the averages and accuracy of the results. Moreover, as seen in Figure 8, it should be noted that factors such as differences in the start and end dates of the datasets within the time series for a few days, the total number of images used, and different orbit baseline intervals will also cause minor differences in the outputs of the X-band and C-band datasets. When observed in rural areas, the chosen X-band's temporal consistency decreases, making it difficult to acquire consistent subsidence monitoring findings. This is due to the changing types and lengths of agricultural crops in this area over time. This study utilized an X-band CSK dataset comprising 20 SAR data collected over a span of two years. However, due to the long base distance in the repetitive passes of the satellite set and the temporal differences of two or three months between images (Figure 8), the desired efficiency could not be achieved. While a large amount of images does not necessarily guarantee more precise results, research explains a correlation between the number of images and the standard deviation as follows.