Spatiotemporal Variations in Sea Ice Albedo: A Study of the Dynamics of Sea Ice Albedo in the Sea of Okhotsk

This study utilizes a novel albedo retrieval framework combining radiative transfer modeling with scientific machine learning (RTM-SciML) to investigate sea ice dynamics in the Sea of Okhotsk. By validating albedo estimates derived from the MODIS sensor against in situ pyranometer measurements near the Hokkaido coast, we achieved a robust Pearson coefficient of 0.86 and an RMSE of 0.089 for all sea ice types, with even higher correlations for specific surfaces like snow-covered ice (Pearson-r = 0.89) and meltwater/open water (Pearson-r = 0.90). This confirms the framework’s efficacy across varying surface conditions. Cross-sensor comparisons between MODIS and the Second-Generation Global Imager (SGLI) further demonstrated its consistency, achieving an overall Pearson-r of 0.883 and RMSE of 0.036. Integrating these albedo estimates with sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR-2), we analyzed the complex role of the Sea of Okhotsk’s polynya systems and ice interactions in regional climate processes. Our results highlight the synergistic advantage of pairing optical sensors, like MODIS and SGLI, with microwave sensors, offering a more comprehensive understanding of evolving sea ice conditions and paving the way for future climate and cryosphere studies.