Advancing Supraglacial Lake Mapping in Antarctica: A Deep Learning Approach Using Sentinel-2 Imagery

With the ongoing effects of global warming, Arctic and Antarctic supraglacial meltwater plays a critical role in the instability of ice sheets. Spaceborne remote sensing provides a viable solution to detect surface melt in these vast and remote regions. Innovative deep learning image segmentation methods have the potential to improve supraglacial lake (SGL) mapping by adding spatial awareness to the mapping process as currently established methods only consider spectral variety of multispectral satellite data (Sentinel-1, Landsat-8) on a per-pixel basis. In this study, we adapt a U-net image segmentation method to detect SGL in Sentinel-2 data and quantify its performance in comparison to established mapping approaches in Antarctica. At a 90% probability threshold, the proposed workflow achieves a producer’s accuracy, user’s accuracy and F1 score of 0.915, 0.912, and 0.913, respectively. We compare the classification results to those of an established Random Forests classifier, and find that the U-net achieves better results in 13 of the 16 validation scenes. In a relative comparison with five established SGL products, the U-net approach maps the most complete lake area, although there is disagreement between the datasets due to different data sources, cloud cover, and misclassification of some clouds as lakes. The main advantages of the developed approach are the capabilities to map lakes under thin clouds and floating ice, resulting in less patchy lake areas than conventional approaches, and a reduced number of input features required, which will be advantageous in future large-scale SGL mapping efforts.