Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.19565-19574
• Main Authors: Gu, Haoqi, Zhang, Lianchong, Qin, Mengjiao, Wu, Sensen, Du, Zhenhong
• Format: Article
• Language: English
• Subjects: Arctic; Atmospheric measurements; Climate change; Deep learning; Dynamic models; Global warming; Heterogeneity; Patchiness; Predictions; Predictive models; Satellite images; Sea ice; sea ice concentration (SIC) prediction; spatial attention; Spatial discrimination learning; Spatial heterogeneity; Spatial resolution; Spatiotemporal phenomena
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3486187

With the accelerating impact of global warming, the changes of Arctic sea ice has become a focal point of research. Due to the spatial heterogeneity and the complexity of its evolution, long-term prediction of Arctic sea ice remains a challenge. In this article, a spatial attention U-Net (SAU-Net) method integrated with a gated spatial attention mechanism is proposed. Extracting and enhancing the spatial features from the historical atmospheric and SIC data, this method improves the accuracy of Arctic sea ice prediction. During the test periods (2018-2020), our method can skillfully predict the Arctic sea ice up to 12 months, outperforming the naive U-Net, linear trend models, and dynamical models, especially in extreme sea ice scenarios. The importance of different atmospheric factors affecting sea ice prediction are also analyzed for further exploration.