Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica

• Published in: Geophysical research letters 2024-02, Vol.51 (3), p.381-n/a
• Main Authors: Tollenaar, Veronica, Zekollari, Harry, Pattyn, Frank, Rußwurm, Marc, Kellenberger, Benjamin, Lhermitte, Stef, Izeboud, Maaike, Tuia, Devis
• Format: Article
• Language: English
• Subjects: Albedo; Algorithms; Antarctica; Artificial intelligence; Balance studies; Bare ice; Bias; Blue ice; Colour; Deep learning; Environmental Sciences; Exposure; Field study; Fieldwork; Glaciation; Ice; Ice sheets; Image processing; Image segmentation; Labels; Machine learning; Mapping; Mass balance; Meteorites; Meteors & meteorites; Missions; MODIS; Neural networks; noisy labels; Performance measurement; Remote sensing; Remote sensors; Satellite observation; Sea level changes; Sea level rise
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2023GL106285

In some areas of Antarctica, blue‐colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi‐sensor remote sensing data (MODIS, RADARSAT‐2, and TanDEM‐X) in a deep learning framework, we map blue ice across the continent at 200‐m resolution. We use a novel methodology for image segmentation with “noisy” labels to learn an underlying “clean” pattern with a neural network. In total, BIAs cover ca. 140,000 km2 (∼1%) of Antarctica, of which nearly 50% located within 20 km of the grounding line. There, the low albedo of blue ice enhances melt‐water production and its mapping is crucial for mass balance studies that determine the stability of the ice sheet. Moreover, the map provides input for fieldwork missions and can act as constraint for other geophysical mapping efforts. Plain Language Summary While most of the continent of Antarctica is covered by snow, in some areas, ice is exposed at the surface, with a typical blue color. At lower elevations, blue ice enhances melt‐water production, which is important for studying the future of the ice sheet. Moreover, scientific teams frequently visit blue ice areas (BIAs) as they act as traps for meteorites and very old ice. In this study, we map the extent and the exact location of BIAs using various satellite observations. These diverse observations are efficiently combined in an artificial intelligence algorithm. We develop the algorithm so that it can learn to map blue ice even though existing training labels, which teach the algorithm what blue ice looks like, are imperfect. We quantify that the new map scores better on various performance metrics compared to the current most‐used blue ice map. Moreover, for the first time, we estimate uncertainties of the detection of blue ice. The map indicates that ca. 1% of the surface of Antarctica exposes blue ice and will be important for fieldwork missions and understanding surface processes leading to melt and potential sea level rise. Key Points We map blue ice areas in Antarctica by combining multi‐sensor satellite observations in a convolutional neural network Blue ice covers ca. 140,000 km2 (∼1%) of Antarctica, of which ca. 50% located in the grounding zone Our map will improve mass balance estimates and studies on ice‐shelf stability, and will support searches for meteorites or old ice