## 4 Results and Discussion  
### Accuracy Assessment  
The validation was conducted on a total of 283 glacial lakes that were covered by the area of GF-2 imagery. As this study area was large and glacial lakes were abundant at a small size, it was difficult to show all the lake boundaries and classification errors clearly. For a better display and visualization of the extraction results and their classification accuracy, Figure 4 presents a complete example of the accuracy assessment that was performed on the mapping results of some test glacial lakes. As shown in Figure 4a, the extracted glacial lake outlines from the Sentinel-1A/1B image (red contours) were almost coincident with the manually digitized glacial lake outlines (yellow contours); these clearly illustrate that the proposed method worked well for detecting glacial lakes from the SAR data. To show more details about the accuracy assessment results, a relatively large glacial lake was  
Figure 3: Conceptual graph of the basic steps in the process of mapping time series of glacial lakes. selected as the example shown in Figure 4b. From Figure 4b, it can be seen that the misclassification mainly occurred along the lake outline, and the number of commission and omission pixels were low. This can be further confirmed using the enlarged distance buffer zone around the outline of this glacial lake (Figure 4c), where the distance between the extracted and manually digitized glacial lake outline was less than five pixels. To show the advantages of the proposed method, we also utilized the supervised SVM and unsupervised ISODATA methods for comparison. The comparison was also conducted for the glacial lakes in the area covered by the GF-2 PMS images. Figure 5 illustrates the differences between the glacial lake outlines extracted by the three methods. Here, three glacial lakes that were characterized by a complex geometrical shape and various surroundings were selected as typical examples to show the detailed comparative results. For the glacial lake (a) that had an irregular shape and stream outlets in the lower reaches of the lake (indicated as a red ellipse in Figure 5a), the SVM overestimated this water area but it was completely ignored by the ISODATA, reflecting their poor ability in the delineation of glacial lakes with complex curved shapes [30]. However, the proposed method took the median composite of the backscattering coefficient values as the regularization term to restrain the lake boundary and could accurately extract the water bodies in the lower reaches region of the glacial lake. For some glacial lakes that had a similar backscattering with the background, as is shown in the eastern part of the glacial lake (b) (Figure 5b), this mainly referred to the local low areas and mud patches characterized by a high moisture content, such that there would still be a tendency to overestimate the glacial lake area using SVM from a single image. The ISODATA method extracted a rough water boundary, which was because it applied global statistics to generate a clustered center,  
Figure 4: Extraction results for some test glacial lakes. (**a**) Comparison of manually digitized glacial lake outlines from GF-2 PMS images and extracted outlines from Sentinel-1A/1B images using the proposed method. (**b**) Commission errors (indicated as yellow color pixels) and omission errors (indicated as red color pixels) of the typical glacial lake. (**c**) Enlarged distance buffer zone (in pixels) image around the outline for the glacial lake in (b). which is often hindered in highly complex images, especially for complex water conditions [31; 32]. On the contrary, the proposed method exhibited the best performance due to its superiority in terms of spectral and backscattering variability. The last glacial lake (c) could be accurately identified using all three methods (Figure 5c), but our method outperformed the SVM and ISODATA since it could capture more local details and obtain a sharper border along the edge of the lake.