High Resolution Sea Ice Concentration Using a Sentinel-1 U-Net Ice-Water Classifier

Abstract

Accurate and high-resolution sea ice concentration (SIC) mapping is essential for polar navigation, environmental monitoring, and assimilation into forecast models. Traditional passive microwave sensors, such as AMSR2, provide reliable SIC estimates but are limited by coarse (5 km) resolution, particularly near coastlines and regions with mixed ice and open water, where finer spatial detail is critical. Synthetic aperture radar (SAR) imagery offers a high-resolution alternative. This study applies a U-Net convolutional neural network to Sentinel-1 SAR data, utilizing pixelwise ice-water labels to enhance SIC mapping. To address SAR noise challenges, we incorporate multilooking, adaptive noise correction, and overlapping patches at inference to improve SIC accuracy while preserving fine-scale features. We trained the U-Net across multilooking levels to balance resolution, noise reduction, and computational efficiency, allowing the model to handle noise artefacts effectively. Our results identify an optimal 7 × 7 multilooking level, achieving 280 m ice-water labels and a 2.5 km SIC field when an additional 9 × 9 SIC window is applied. This configuration enhances traditional SIC products by improving the representation of the ice edge, leads, and near-coastal features, which are critical for operational applications. SAR-derived SIC addresses the limitations of passive microwave products by providing superior spatial detail and ice edge resolution. Incorporating additional information from AMSR2 or wind features could strengthen SIC robustness and minimize the misclassification of open water, which is present in the results. These advancements would establish SAR-based SIC as a valuable tool for operational sea ice monitoring and integration into high-resolution models.