## 5 Discussion and Suggestion
In the research [65] conducted in Groningen/Netherlands, the standard deviation is ~0.1 mm/year per \(\sqrt{\text{km}}\) when 24 interferograms are used. However, in a subsequent analysis using 74 interferograms in the same location, the standard deviation decreased to ~0.04 mm/year per \(\sqrt{\text{km}}\). Hence, adjusting the amount of data when dealing with the X-band dataset might be presented as a guideline in this study. Moreover, the lack of data collected from the descending orbit for the CSK dataset might be considered a limiting factor in this research. Similarly, the lack of diagnostic train data for the third study region is viewed as a drawback in comparative analyses. However, the absence of train data in regions like these has once again emphasized the crucial significance of InSAR remote sensing for monitoring structural deformation. While the subsidence phenomena in the whole study area are effectively demonstrated by our results across the datasets, they should still be improved via in situ analysis using techniques like GNSS and levelling as independent ground-truth data, which can be seen as another limitation of this study. Furthermore, due to the fact that railway lines typically pass both urban and rural regions, it is necessary to develop specific methods for monitoring deformation events in rural areas. This is where the existence of C-band observations, which can be obtained without any cost, becomes significant [13]. Artificial reflectors, such as corner reflectors, can be placed in the areas lacking PSs but which need to be monitored for deformation [68]. Corner reflectors offer a cost-effective alternative to on-site and ground-based monitoring equipment. They can be strategically installed in key sections of the structure to generate effective reflections, facilitating more efficient identification of these targets as PSs during the processing phases [69]. The analysis results are crucial for properly placing GNSS receivers at certain locations identified as critical, such as the first and third places mentioned in this study. Performing further geodetic observations in these crucial locations will enhance the level of detail and quality of the observations, enabling more precise decision-making. As an additional suggestion, if a more detailed geophysical study is desired for such areas, a Ground Penetrating Radar (GPR) device can be easily mounted on the diagnostic train. GPR is a well-recognized Non-Destructive Testing (NDT) method that causes minimum disruption to local traffic and is very efficient in examining the internal structure of the ballast layer, subgrade and embankment [3]. In [69], researchers review recent advancements and applications in satellite remote sensing and ground-based NDT methods for monitoring transport infrastructures in detail. This proposal appears to be a feasible approach for the Asagipinarbasi-Yukaripinarbasi region, which is referred to as the first research area in this study and has been identified as a critical area. Another important limitation of any InSAR technique is that tunnels cannot be measured due to SAR's nature. Such structures will continue to be precisely and regularly monitored using diagnostic trains and/or terrestrial measurement techniques and various sensors. The Workflow Chart is designed specifically for addressing railway-related issues and providing specialized solutions, as shown in Figure 13. It is anticipated that this chart will offer a straightforward and practical method for analysts or researchers who need to monitor subsidence events in and around railway areas utilizing an advanced InSAR technique as a complementary surveying and monitoring tool.