Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics.
Keywords: Sentinel-1; Tibetan Plateau; glacial lake mapping; median composite; time series.