Cryo2S1: Mapping sea ice radar freeboard in Sentinel-1 SAR imagery from CryoSat-2 using deep learning

Sea ice is critical to map for safe and efficient maritime navigation, to mitigate ship trapping and capsizing. Sea ice is also vital to monitor to assess the state of the changing climate and a critical component in climate and weather models, reflecting sunlight towards space and acting as an insulating material between the ocean surface and the atmosphere.

Professional sea ice analysts at national ice services map sea ice based on Synthetic Aperture Radar (SAR) images acquired by satellites, such as the Sentinel-1 (S1) satellite constellation. The ice analysts manually interpret the SAR images using their in-depth knowledge and experience to create sea ice charts with information on the sea ice conditions.

A challenge for the S1 5.4 GHz SAR measurements is that the radar wave does not penetrate deep into the sea ice and is scattered/reflected by the surface. Therefore, the SAR images provide information primarily about the sea ice surface, useful for identifying and classify sea ice conditions. The charts describe, among others, the sea ice’s stage of development - the type of sea ice - an indicator of its thickness. The manual charting process apply sea ice classes, defined by the International Ice Charting Working Group (IICWG) on behalf of the World Meteorological Organization (WMO). Considerable uncertainties are associated with the ice classes that can vary from, e.g. 30-200cm or 70-120cm in thickness. Deep-learning models that produce stage-of-development information from S1 radar images exist but has the same inherent limitations of the sea ice charts in the model outputs.

Current state-of-the-art sea ice thickness retrieval methods relies on altimeter satellites, such as the CryoSat-2 (CS2) satellite. The distance between the ocean and the sea ice is measured, known as the sea ice freeboard. For a Ku-band radar altimeter like CS2, it is assumed that the radar response penetrates the snow and returns from the sea ice surface. As the true penetration is unknown, and the radar wave propagation is delayed when the signal passes through snow, the measured quantity is known as the radar freeboard.

The sea ice thickness can be estimated with an accuracy of 20-40% using the radar freeboard by calculating the sea ice's buoyancy based on snow and ice density estimates, and auxiliary snow depth information. However, CS2 only measures 1600m across the orbit and can thus only monitor sea ice thickness in the Arctic monthly - insufficient for many applications, such as maritime navigation, and leaves data record gaps. S1 SAR on the other hand, cover 400km in Extra Wide mode across the orbit with repeating coverage every week.

Here, we present our preliminary results of circumventing the limitations of CS2 and S1 by training supervised deep-learning convolutional neural network (CNN) models to recognise sea ice textures in S1 SAR images and assign sea ice radar freeboard estimates acquired by CS2. This approach transfers information acquired by CS2 to S1, which we call Cryo2S1. A Cryo2S1 dataset is curated, containing several thousand collocated S1 SAR images and along-track CS2 measurements during 2020-2021.