Abstract
Although high-resolution mapping of pan-Arctic sea ice with reliable corresponding uncertainty is essential for operational sea ice concentration (SIC) charting, it is a difficult task due to key challenges, such as the subtle nature of ice signature features, inexact SIC labels, model uncertainty, and data heterogeneity. This study presents a novel Bayesian High-Resolution Transformer approach for 200 meter resolution pan-Arctic SIC mapping and uncertainty quantification using Sentinel-1, RADARSAT Constellation Mission (RCM), and Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, to improve small and subtle sea ice feature (e.g., cracks/leads, ponds, and ice floes) extraction, we design a novel high-resolution Transformer model with both global and local modules that can better discern the subtle differences in sea ice patterns. Second, to address low-resolution and inexact SIC labels, we design a geographically-weighted weakly supervised loss function to supervise the model at region level instead of pixel level, and to prioritize pure open water and ice pack signatures while mitigating the impact of ambiguity in the marginal ice zone (MIZ). Third, to improve uncertainty quantification, we design a Bayesian extension of the proposed Transformer model, treating its parameters as random variables to more effectively capture uncertainties. Fourth, to address data heterogeneity, we fuse three different data types (Sentinel-1, RCM, and AMSR2) at decision-level to improve both SIC mapping and uncertainty quantification. The proposed approach is evaluated under pan-Arctic minimum-extent conditions in 2021 and 2025. Results demonstrate that the proposed model achieves 0.70 overall feature detection accuracy using Sentinel-1 data, while also preserving pan-Arctic SIC patterns (Sentinel-1 R\textsuperscript{2} = 0.90 relative to the ARTIST Sea Ice product).
Abstract (translated)
尽管高分辨率的泛北极海冰制图并可靠地量化相应的不确定性对于操作性海冰浓度（SIC）绘图至关重要，但由于一些关键挑战——如冰特征细微、SIC标签不准确、模型不确定性以及数据异质性的原因，这一任务十分艰难。本研究提出了一种新颖的贝叶斯高分辨率变换器方法，用于利用Sentinel-1、RADARSAT星座使命（RCM）和高级微波扫描辐射计2号（AMSR2）的数据进行泛北极海冰浓度的200米分辨率制图及不确定性量化。具体而言： 第一，为了改进小而微妙的海冰特征（如裂缝/缝隙、池塘、浮冰）的提取，我们设计了一种新颖的高分辨率变换器模型，该模型同时具备全局和局部模块，能够更好地识别海冰模式中的细微差异。 第二，为了解决低分辨率和不准确的SIC标签问题，我们设计了一个地理加权弱监督损失函数，在区域级别而非像素级别上监督模型，并优先考虑纯净开放水域及密集冰区的特征，同时缓解边际冰带（MIZ）中模糊性的不利影响。 第三，为了改进不确定性量化，我们将贝叶斯扩展应用于提出的变换器模型，将其参数视为随机变量，从而更有效地捕捉到不确定因素。 第四，为了解决数据异质性问题，我们融合了三种不同类型的数据（Sentinel-1、RCM和AMSR2），在决策层面提高SIC制图及不确定性量化的准确性。 所提出的方法已在2021年和2025年的泛北极最小范围条件下进行了评估。结果显示，在使用Sentinel-1数据的情况下，该模型实现了0.70的整体特征检测准确率，并且保留了泛北极的海冰浓度模式（相对于ARTIST Sea Ice产品，Sentinel-1 R² = 0.90）。
URL
https://arxiv.org/abs/2603.03503
