arXiv:2611.08533v1 Announce Type: new Abstract: Current sea ice mapping benchmarks are mostly built around single-sensor SAR products, limiting the evaluation of cross-sensor variability and the effectiveness of combining observations from different SAR platforms. Models trained on such datasets may also be sensitive to sensor-specific artifacts, radiometric characteristics, and acquisition conditions, which can reduce robustness and limit generalization. To address these limitations, this study introduces Arctic-SAR, a cross-sensor SAR dataset for Arctic sea ice mapping, constructed by matching RADARSAT Constellation Mission (RCM) dual-polarization imagery with Sentinel-1 scenes from the AI4Arctic Sea Ice Dataset. The dataset consists of 221 co-registered scenes and supports multi-task prediction of sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). In addition to the dataset, we establish a benchmark for cross-sensor SAR sea ice mapping by quantifying the contribution of RCM imagery and evaluating single-sensor baselines alongside representative input-, feature-, and decision-level fusion strategies within a consistent multi-task U-Net framework. Across three independent runs, the test results show that the effectiveness of cross-sensor fusion is strongly dependent on the fusion strategy and prediction task. Decision-level fusion achieves the highest mean combined score of $77.28\pm0.29\%$, essentially matching the Sentinel-1-only baseline at $77.26\pm0.13\%$, while achieving the highest SIC \(R^2\) score. These findings show that Sentinel-1 and RCM provide task- and class-dependent information, but that simple joint cross-sensor fusion does not consistently improve generalization over a strong single-sensor baseline. Arctic-SAR therefore provides a benchmark for cross-sensor SAR fusion research in automated Arctic sea ice mapping.