Fusing Ice Surface Temperature with the AI4Arctic Dataset for Enhanced Sea Ice Concentration Estimation: A Preliminary Assessment

While Arctic sea ice mapping supports several key applications (e.g., navigation, climate monitoring), its accuracy is impacted by remote sensing uncertainties and data limitations. The recent AI4Arctic dataset combines Sentinel-1 Synthetic Aperture Radar (SAR) imagery, AMSR2 brightness temperature (TB) measurements, ERA-5 reanalysis data, and ice charts to improve deep learning-based mapping approaches. Nevertheless, AI4Arctic excludes thermal infrared data and it is critical to explore the use of these products, which may improve predictions where SAR and passive microwave measurements are challenging to interpret. This study investigates the use of VIIRS ice surface temperature (IST) for improving SIC predictions. Our work builds on a competitive U-Net architecture, which estimates three parameters for automated sea ice mapping: SIC, stage of development, and floe size. A 30-scene subset of the AI4Arctic dataset is selected based on established criteria, and co-registered with VIIRS IST data. The impacts of fusing IST with other remote sensing data at the input- and feature-levels are explored using two fusion architectures. Experimental trials are conducted using these models, spanning six input channel combinations. Predictions are compared using evaluation metrics and SIC maps. When using IST measurements in combination with the original input channels, both the input- and feature-level approaches outperform the baseline model. This preliminary study suggests that IST data, in combination with TB measurements, improves predictions where ambiguous textures are present in SAR imagery or PM data is not able to contribute.