Deep learning and physics-based approaches for ice-ocean monitoring

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Sea ice cover in the Arctic is undergoing significant change. Decreasing ice extent and thickness, and an increasing open water season, are driving increased pressures on this ecologically sensitive region. Ways to monitor these changes are desperately needed by ice service operations worldwide as well as governmental decision makers. Sea ice concentration is a key variable that indicates the fraction of a specified area of the ocean that is covered by ice. It is typically monitored using remote sensing data acquired in the low-frequency microwave portion of the electromagnetic spectrum because data at these frequencies are not sensitive to cloud cover or sunlight. Both passive and active microwave sensors are used for sea ice concentration monitoring. Passive microwave sensors measure the energy naturally emitted by the earth, while active microwave sensors send a signal to the earth and measure the backscatter. Passive microwave sensors are used to monitor sea ice concentration at large scales (e.g., 30 km), while synthetic aperture radar (SAR) sensors provide higher resolution data (e.g. 50 m). SAR sensors are well suited to monitoring the marginal ice zone, a realm where ice eddies, floes and waves significantly modulate the sea ice cover. The recent launch of multiple SAR constellation systems is providing a rapidly growing data volume from SAR. This has subsequently spurred strong interest in automated methods to extract information from these data. Deep learning is a data-driven approach that can learn patterns from data, and is well suited to this task. However, these methods are widely viewed as a `black box', which hinders widespread acceptance of deep learning in downstream applications. Erroneous information provided by a deep learning algorithm could endanger those operating on the ice. For example, fishing boats that lack ice-strengthening need an accurate ice edge location. While there are several 'opening the box' methods proposed, these are designed for the problem space of everyday images that have distinct objects and colours. SAR sea ice images are greyscale images of multi-scale phenomena. Additionally, sea ice is a physical system that can be modelled using differential equations. The proposed research program will exploit these aspects to develop tractable, scale-aware, physically guided approaches. Both real data and simulated data will be used to develop the deep learning approaches, which is different from past work in this problem domain. This will allow a thorough investigation of the proposed methodologies. Expected outcomes are improved estimates of sea ice concentration in the marginal ice zone from satellite sensors benchmarked quantitatively against current leading products; and a novel deep learning framework for multiscale physical systems.