A High-Resolution Sea Ice Concentration Retrieval from Ice-WaterNet Using Sentinel-1 SAR Imagery in Fram Strait, Arctic

Abstract

What are the main findings?

We propose Ice-WaterNet, a novel superpixel-based deep learning framework that effectively reduces classification uncertainty in complex melt season conditions by integrating CRF and a dual-attention U-Net mechanism.

The model is validated on 2735 Sentinel-1 SAR images from 2021–2023 in the Fram Strait, demonstrating superior performance over state-of-the-art methods in both winter and summer seasons across multiple evaluation metrics.

What is the implication of the main finding?

This study indicates the critical need to develop high-resolution SAR-based products, which can more accurately capture fine-grained spatiotemporal melt characteristics and provide reliable data for climate change research and sea ice trend analysis.

By revealing the limitations of passive microwave sensors in assessing melt conditions, this study emphasizes that high-resolution SIC retrieval is essential to reduce underestimation errors and support operational applications such as maritime navigation and polar environment monitoring with improved spatial and temporal precision.

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research.