用多源卫星数据实现200米分辨率北极海冰浓度高精度制图与不确定性评估
Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
- 设计融合全局与局部特征的高分辨率Transformer模型，增强对冰缝、融池等细微结构的识别能力
- 采用地理加权弱监督损失函数，在区域层面提升对开敞水域和冰盖区的建模精度，降低边缘带模糊影响
- 引入贝叶斯扩展框架，通过参数随机化有效量化模型不确定性，适合需要可信度评估的极地监测场景
尽管高分辨率全景北极海冰浓度（SIC）制图及其可靠不确定性估计对业务化海冰图绘制至关重要，但受冰特征微弱、标签不精确、模型不确定性和数据异质性等挑战制约，仍具难度。本研究提出一种新型贝叶斯高分辨率Transformer方法，利用哨兵-1、雷达星星座任务（RCM）和先进微波扫描辐射计2号（AMSR2）数据，实现200米分辨率全景北极SIC制图与不确定性量化。首先，为提升细小冰特征（如裂缝/冰隙、融池、冰块）提取能力，设计兼具全局与局部模块的高分辨率Transformer模型，更好区分海冰图案的细微差异。其次，针对低分辨率及不精确的SIC标签，设计地理加权弱监督损失函数，在区域层面进行监督，优先关注纯开敞水域与冰盖区特征，减轻边缘区（MIZ）模糊带来的影响。第三，为改善不确定性量化，构建所提Transformer的贝叶斯扩展，将参数视为随机变量，更有效捕捉不确定性。第四，通过决策层融合三种不同数据类型（哨兵-1、RCM、AMSR2），提升海冰浓度制图与不确定性估计性能。该方法在2021年和2025年北极最小海冰覆盖条件下进行评估，结果表明：使用哨兵-1数据时，整体特征检测准确率达0.70，同时保持全景海冰浓度模式一致性（相对于ARTIST海冰产品，哨兵-1的R² = 0.90）。
原文摘要 · Abstract (English)
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).
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