A Framework for Fine-Resolution and Spatially Continuous Arctic Sea Ice Drift Retrieval Using Multisensor Data

Monitoring Arctic sea ice drift is essential for understanding climate change. Currently, large-scale observed sea ice drift datasets primarily rely on single-sensor remote sensing data, which suffer from low spatial resolution or poor spatial continuity. To address these limitations, this study proposes a sea ice drift retrieval framework based on multisensor data, utilizing the complementary sea ice drift information derived from passive microwave (PM) radiometers and medium-resolution optical sensors. The proposed framework employs the maximum cross correlation (MCC)-based pattern matching method to obtain sea ice drift fields from coarse-resolution Fengyun-3D (FY-3D) Microwave Radiation Imager (MWRI) data, and an accelerated-KAZE (A-KAZE)-based feature tracking method to extract sea ice motion vectors from FY-3D Medium-Resolution Spectral Imager II (MERSI-II) data. Finally, the sea ice drift vectors from different sensors are merged using the Co-Kriging algorithm to obtain the final sea ice drift result. The effectiveness of the proposed framework was assessed by comparing displacements from 166 buoys with the retrieved vectors derived from FY-3D single-sensor and multisensor data, as well as an existing sea ice drift product [Ifremer-advanced microwave scanning radiometer (AMSR2)] collected in the Beaufort Sea, the East Siberian Sea, and the Fram Strait. The results demonstrate the proposed framework’s ability to retrieve fine-resolution (i.e., 1 km) and spatially continuous sea ice drift fields in areas where vectors from fine-resolution data can be obtained. The overall mean absolute errors (MAEs) of the merged sea ice motion vectors are 0.76 km/day for speed and 4.53° for angle, exhibiting superior drift accuracy to Ifremer-AMSR2 in areas covered by MERSI-II vectors.