Sea Ice

Sea ice research focuses on understanding its dynamics, distribution, and impact on climate, with a primary objective of improving prediction capabilities for navigation and environmental monitoring. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), U-Nets, and transformers to analyze satellite imagery (SAR and optical) for sea ice classification and forecasting, often incorporating multi-temporal and multi-spectral data fusion. These advanced models aim to improve accuracy and efficiency compared to traditional physical models, particularly for short-term operational forecasting in key regions. Improved sea ice prediction is crucial for maritime safety, resource management, and a more comprehensive understanding of Arctic climate change.