High-Resolution Sea Ice Drift Modelling Using Sentinel-2 and Discrete Element Method: Towards Subgrid Parameterization for Large-Scale Models

Large-scale sea ice models still struggle to reproduce observed drift and deformation patterns, underscoring the need for physically based representations of unresolved floe-scale processes. Advances in high-resolution modelling and satellite imagery now offer new opportunities to investigate floe-scale dynamics and their influence on large-scale ice behaviour. These developments are essential not only for improving model fidelity but also for Arctic navigation and offshore safety.

As part of the ESA-funded HIRLOMAP project, we present a workflow for generating high-resolution sea ice products by integrating satellite observations, image processing, and numerical modelling. The study domain is a 110 × 110 km southeast of Svalbard. Two Sentinel-2 images (10 m resolution) acquired on 16^th and 17^th of April 2025 are used. Ice drift vectors were derived from the satellite images through feature tracking, enabling estimation of local displacement and deformation rates (divergence and shear).

To simulate ice dynamics, we employ a Discrete Element Model (DEM) with boundary conditions informed by satellite-derived drift. Environmental forcing includes wind fields from NORA3 and ocean currents from Barents-2.5, while ice thickness is obtained from CryoSat-2/SMOS products. The initial ice field is digitized from Sentinel-2 imagery, yielding around 40 000 floes. Computational efficiency is improved through hierarchical clustering and area-based filtering, reducing floe count to around 1 800 while conserving total ice concentration. Model calibration focuses on air drag coefficients to reproduce observed deformation patterns. Simulated drift and strain rates are compared against Sentinel-2 observations and Barents-2.5 outputs, demonstrating the capability of DEM to capture local-scale variability beyond continuum models.

Future work will address large-scale fracture processes (e.g., ridging), wave–ice interactions, strategies to enhance computational performance, and the integration of machine learning approaches to further advance modelling capabilities. Even though these processes are not yet included, the results presented here already demonstrate strong potential for delivering next-generation Arctic sea ice services that combine high-resolution satellite data and DEM-based modelling. Beyond its engineering applications, this approach demonstrates how DEM can act as a subgrid parameterization tool for continuum models, enabling large-scale systems to represent floe-scale processes such as deformation and fracture.