## 4 Results and Discussion
### Discussion  
In many glacial lake mapping methods, the lack of high-frequency mapping results is a problem for some hazardous lakes that have high seasonal variations, making it difficult for timely and reliable GLOF risk evaluation. The use of a glacial lake inventory dataset with a coarse temporal resolution may lead to the incorrect analysis of the evolution patterns of glacial lakes, which may also affect decision-making regarding hazard management. This study devised a method that was devoted to increasing the frequency maps of glacial lakes by utilizing the time series of Sentinel-1A/1B SAR data  
Figure 7: The spatial distribution of glacial lakes (shown with red) extracted using the developed method over the southeastern Tibetan Plateau (indicated as pink and blue rectangles). Three magnified maps (**a–c**) in the bottom show the local details in the lake outlines overlaid on the Sentinel-1A/1B median composite image.  
with consecutive 6-day periods. In addition to the high-frequency characteristics, our method also showed an improvement in accuracy when compared with SVM and ISODATA methods in classifying complex-shaped glacial lakes with various surroundings. It should be noted that in our study, to reduce the influence of data noise and uncertainty from a single image, all the accessible images acquired during the observation period were used to compute the median value for each pixel for the precise delimitation of the lake extent. In classifying images that did not have a sufficient enough image number or high data quality, the derived median values may differ slightly and affect the extraction results of glacial lakes from what is mapped in this study.  
As for the selection of features for segmentation, in this paper, we used the SAR intensity images to extract the glacial lake outlines, which have extensive applications for water delineation due to its low backscattering values at all microwave wavelengths and distinct separation from the snow, glacier, and debris cover around the glacial lakes [27; 37]. This allows for the fairly accurate classification of glacial lakes in intensity images. Given the fact that the relative instability of glacial lake water will significantly decrease the degree of interferometric synthetic aperture radar (InSAR) coherence, the capability of coherence has been recently explored to characterize and map the unconnected glacial lakes in the Himalayas [37], which has proved to be a promising feature for deriving lake boundaries and may be used as an input feature for future study. Furthermore, the application of the proposed method is not limited to SAR data since it can be extended for the extraction of glacial lake outlines by incorporating spectral features from optical images, such as single-band reflectance features, water indices, image transformation features, etc.  
Nevertheless, in our test cases, we did not consider the influence of different types of glacial lakes, such as proglacial lakes and supra-glacial lakes. Therefore, the robustness of the new method needs to be tested in different regions under various climatic and cryospheric backgrounds. More sites over the whole Tibetan Plateau may need to be included for a thorough evaluation of the performance of the method.