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

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).

Figures (13)

• Figure 1: Overview of our key innovations for pan-Arctic SIC mapping with corresponding uncertainty quantification. The proposed model utilizes a (a) High-Resolution Transformer architecture, $f^{\omega}(.)$, with an among-token Transformer block for modeling global context (GloFormer) and a within-token Transformer block for modeling local detail (LoFormer). The high-resolution Transformer becomes a (b) Bayesian Neural Network (BNN) by assuming the model parameters in the attention mechanisms are probabilistic, as highlighted in red. The primary training strategy is a (c) geographically-weighted weakly supervised $\mathcal{L}_{L1-GW}$ loss, where the difference between the predicted SIC and NASA Team ground truth is evaluated at the cluster level rather than the high-resolution pixel level. Geographical weights dynamically scale the importance of each sample according to the NIC ice chart. Individual Bayesian Transformer models are trained using Sentinel-1, RCM, and AMSR2 89.0GHz in parallel. After variational inference, (d) decision-level data fusion is used to combine the SIC and corresponding uncertainty maps.
• Figure 2: Theoretical Comparison of Epistemic Uncertainty Quantification Approaches. Figure (a) illustrates the Bayesian Neural Network (BNN) where the model parameters are assumed to be probabilistic. Figure (b) illustrates MC dropout where red denotes masked nodes and green denotes masked connections. With each variational inference the dropout configuration changes, introducing randomness into the neural network. Figure (c) illustrates Epoch ensemble generation where model parameters at each epoch are treated as independent ensemble members.
• Figure 3: Overview of data downloaded and processed for September 4th, 2021. Includes Sentinel-1 (a) HH, (b) HV, and (c) HH and HV cross-polarization, RCM (f) HH, (g) HV, and (h) HH and HV cross-polarization, AMSR2 (d) 89.0 GHz H and (e) 89.0 GHz V data, as well as (i) NIC Ice Chart and (j) NASA Team SIC products.
• Figure 4: Comparison of predicted SIC (left), the ice/water binary ground truth mask derived using adaptive thresholding of the NIC ice chart (middle), and corresponding SAR HV (right) for one 256 x 256 validation sample.
• Figure 5: Pan-Arctic SIC from the Bayesian High-Resolution Transformer for validation data on September 4th, 18th, and 27th, 2021. SIC derived from (a) Sentinel-1, (b) RCM, and (c) AMSR2, and (d) is the final fused map of all data sources with Sentinel-1 layered on top, followed by RCM and AMSR2. SIC estimates are visually compared to (e) NASA Team SIC and (f) NIC Ice Chart.