par Tollenaar, Veronica 
;Zekollari, Harry ;Pattyn, Frank ;Rußwurm, Marc;Kellenberger, Benjamin;Lhermitte, Stef;Izeboud, Maaike;Tuia, Devis
Référence Geophysical research letters, 51, 3
Publication Publié, 2024-02
;Zekollari, Harry ;Pattyn, Frank ;Rußwurm, Marc;Kellenberger, Benjamin;Lhermitte, Stef;Izeboud, Maaike;Tuia, DevisRéférence Geophysical research letters, 51, 3
Publication Publié, 2024-02
Article révisé par les pairs
| Résumé : | Abstract 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 km 2 (∼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. |
