## 3 Methodology
### Flow Velocity Monitoring of POT Glacier Considering Noise Suppression  
Optimization Selection of Matching Window  
Because POT technology calculates the pixel offset by establishing the template window and search window in turn in the map, the large amount of calculation leads to its low efficiency in the wide-area glacier flow velocity monitoring. Therefore, before implementing wide-area monitoring, first, the Sentinel-1 data of local glaciers (Table 2) in the study area, as shown in Figure 2, are selected for a pre-test to analyze the error sources and noise suppression. According to the principle of the POT technique, the cross-correlation coefficient of the center pixel of the template window is related to the size of the window and the intensity value of each pixel in the window. Therefore, it is necessary to compare and optimize the window size, and then balance the monitoring accuracy and noise suppression of the surface flow velocity of the POT glacier. Three sizes of cross-correlation windows of 1024 \(\times\) 512, 512 \(\times\) 256, and 256 \(\times\) 128 are selected for offset calculation, and we obtained the offsets of the two image pairs in Table 2 in the range and azimuth directions, as shown in Figure 2. It can be seen that the offset of the non-glacier area in the result is near the value of 0, indicating that the image pair registration effect is good and is less affected by errors such as orbit, topography, and ionosphere. Detailed information of the glacier displacement is well preserved in the results solved by the 256 \(\times\) 128 window, but the overall noise level is high. The noise is obviously suppressed in the results of the 512 \(\times\) 256 window solution, especially near the glacier area, and the monitoring effect of the main glacier deformation area is maintained. The noise is significantly suppressed in the 1024 \(\times\) 512 window solution, but the monitoring degree of the glacier displacement area is greatly weakened by the large matching window. Therefore, as the cross-correlation window size of the POT technique \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Image Pair** & **Spatial Baseline/m** & **Time Baseline/Day** \\ \hline
1 & 20200120–20200201 & \(-\)28.123 & 12 \\
2 & 20200120–20200213 & \(-\)45.562 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sentinel-1 image parameters of Monacobreen glacier, Svalbard, selected from the pre-experiment. Figure 2: Sentinel-1 image intensity of Monacobreen glacier in Svalbard selected from the pre-experiment. increases in glacier flow velocity monitoring, and the noise suppression level gradually increases, but the description efficiency of deformation gradually decreases. Compared with the results of 12-day time interval in Figure 3(a-1-a-3,b-1-b-3), the overall noise level in the results of Figure 3(c-1-c-3,d-1-d-3) with a longer time interval (24 days) is relatively high, and the use of a larger cross-correlation window can no longer play a useful role in suppressing the noise. This is because due to the long time interval, the physical texture characteristics of surface features change greatly, which is does not allow us to carry out pixel offset calculation based on the cross-correlation strategy. Therefore, the selection of a time baseline needs to be reasonably made according to the specific conditions of glacier flow velocity in the monitoring area. In Table 3, the statistics for the root mean square error of the non-glacier location shift near the glacier and the signal-to-noise ratio under the cross-correlation window of three sizes are shown. It can be seen that in the non-glacial region, the RMS error gradually decreases as the cross-correlation window increases. Therefore, when there is noise between the primary and secondary images, choosing a large cross-correlation window can suppress the noise to a certain extent.