Rapid Arctic warming has accelerated sea ice drift, deformation and fragmentation, reshaping the Arctic ice environment and creating an urgent need for dynamic ice floe monitoring to ensure safe Arctic navigation. Marine radar (MR), with its all-weather, high-temporal-resolution capabilities, uniquely captures rapid small-scale ice deformation in fragmented ice fields. However, blurred floe boundaries and weak surface texture in MR imagery impede the accurate extraction of ice dynamic parameters. To address this challenge, we developed a Floe Motion and Deformation (FMD) framework specifically for MR data. It treats individual ice floes as discrete objects and employs point-set registration to estimate their displacement vectors and rotation angles. Hence, it enables a quantitative characterization of both motion and deformation at the floe scale. We first evaluated FMD on synthetic datasets, where it shows a high accuracy in retrieving kinematic and deformation parameters. Next, we applied it to MR image sequences acquired during the R/V Zhong Shan Da Xue Ji Di FACE2024 (Following Arctic/Antarctic iCE 2024) expedition, where it successfully tracked Lagrangian trajectories of 2425 individual ice floes. Validation against GPS buoys deployed on ice floes shows that FMD achieves a linear velocity error of 0.25 cm s−1 and an angular velocity error of 9.5 × 10−6 rad s−1 over 10-minute intervals. The FMD framework thus provides a high-precision, minute-scale solution for monitoring large floe populations, directly enhancing a vessel's ability to perceive dynamic ice environments and supporting the development and application of autonomous navigation systems in ice-covered waters.
Li et al. (Thu,) studied this question.