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Global climate warming is rapidly reducing Arctic sea ice volume and extent. The associated perennial sea ice loss has economic and global security implications associated with Arctic Ocean navigability, since sea ice cover dictates whether an Arctic route is open to shipping. Thus, understanding changes in sea ice thickness, concentration and drift is essential for operation planning and routing. However, changes in sea ice cover on scales up to a few days and kilometers are challenging to detect and forecast; current sea ice models may not capture quickly-changing conditions on short timescales needed for navigation. Assimilating these predictive models requires frequent, high-resolution morphological information about the pack, which is operationally difficult. We suggest an approach to mitigate this challenge by using machine learning (ML) to interpret satellite-based synthetic aperture radar (SAR) imagery. In this study, we derive ML models for the analysis of SAR data to improve short-term local sea ice monitoring at high spatial resolutions, enabling more accurate analysis of Arctic navigability. We develop an algorithm/classifier that can analyze Sentinel-1 SAR imagery with the potential to inform operational sea ice forecasting models. We focus on detecting two sea ice features of interest to Arctic navigability: ridges and leads (fractures in the ice shelf). These can be considered local extremes in terms of ice thickness, a crucial parameter for navigation. We build models to detect these ice features using machine learning techniques. Both our ridge and lead detection models perform as well as, if not better than, state-of-the- art methods. These models demonstrate Sentinel-1's ability to capture sea ice conditions, suggesting the potential for Sentinel-1 global coverage imagery to inform sea ice forecasting models.
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