Fusing ice surface temperature with the AI4Arctic dataset for improved deep learning-based sea ice mapping

Arctic sea ice mapping is vital for supporting marine navigation, climate monitoring, and efforts by northern communities to adapt to variable ice conditions. Automated mapping approaches can leverage freely accessible satellite data to supplement navigational ice charts, improve operational forecasting, and produce high-resolution sea ice parameter estimates. The AI4Arctic dataset enables deep learning-based mapping using synthetic aperture radar (SAR), passive microwave (PM), and reanalysis data. However, SAR and PM can struggle to resolve ice features due to ambiguous textures, atmospheric effects, and sensor limitations. To provide complementary data, an 84-scene VIIRS dataset is co-registered with AI4Arctic to evaluate whether ice surface temperature (IST) measurements can improve estimation of sea ice concentration (SIC), stage of development, and floe size. Input- and feature-level fusion methods, based on the U-Net architecture, are explored. Models are evaluated using the SIC R2 coefficient and SOD/FLOE F1-score, as well as predicted sea ice maps. Additionally, an alternative SIC accuracy score is introduced to assist with evaluating marginal ice predictions. Incorporating IST improves performance across all models compared to the AI4Arctic baseline; this includes single-encoder, dual-encoder, and multi-decoder U-Nets. Results highlight significant improvements in the prediction of open water under conditions with low-incidence angle, high atmospheric moisture, and wind roughening. Overall, the best performing dual-encoder model, DUE-Net-V, improves predictions by 2.18- 5.01% across all metrics, relative to the baseline. These results support integrating IST in deep learning workflows and highlight the potential for next-generation thermal-infrared sensors to improve automated sea ice mapping.