The Arctic is highly sensitive to changes in the global climate. One of the most prominent examples in this context is the shrinking sea ice cover over the past decades. The impact of the change in ice coverage on the global climate requires a thorough understanding of ice dynamics, as drifting sea ice transports salt and heat and in this way influences ocean dynamics. Sea ice motion is mainly driven by wind, ocean currents and internal ice stress. Convergent ice motion causes features such as ridges and rubble fields, which change the momentum exchange between atmosphere, ice and ocean. Openings in the ice due to divergent motion increase the exchange of heat and matter between ocean and atmosphere. On time scales of days to weeks, linear kinematic features, such as leads and ridges, evolve on spatial scales ranging from meters to tens and hundreds of kilometers. These features also emerge in numerical sea ice models when the resolution of the simulations is increased to a few kilometers. While plausible, their realism in the simulations is yet unclear and requires a detailed evaluation, with the help of (in our case satellite-based) observations. We use Arctic-wide MITgcm simulations for 2006 at a spatial resolution of approximately 4km for a regional comparison with microwave satellite observations, e.g. from Synthetic Aperture Radar (SAR). We derive sea ice displacement from a sequence of satellite images by measuring the offset between matching patterns in different images. Discontinuities in the resulting velocity field indicate regions of instantaneous deformation that occur at some point in the time interval between the acquisition of the two SAR images used for displacement retrieval. The obtained quantities of deformation by divergence, shear and vorticity are scale-dependent. As a consequence, they depend on the spatial resolution of the SAR images and differ from the quantities calculated by the model. Hence, the comparison between model simulations and results of retrievals from remote sensing data is not straightforward. Therefore, we pay special attention to the spatial and temporal scales of the observed / modelled processes and introduce appropriate statistical tools. By evaluating the kinematic features in the results of high-resolution sea ice models based on the microwave remote sensing data we expect to be able to assess and improve the ice rheology in these sea ice models.