Scientists at the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences, and their international collaborators, proposed a state-of-the-art deep-learning network, named EOLO, aiming to improve the detection of ocean eddies observed in C-band spaceborne synthetic aperture radar (SAR) imagery. They combined advanced artificial intelligence (AI) algorithms with high-resolution spaceborne SAR data, providing a methodological basis for further studies of sub-mesoscale eddies. The study was published in Remote Sensing of Environment.

Ocean eddies are rotary currents of water, which play a crucial role in the global energy cycle and significantly influence the transport of heat, salt, and nutrients in the global ocean. Eddies with diameters larger than the first baroclinic Rossby radius are known as mesoscale eddies, and those with diameters smaller than this radius are called sub-mesoscale eddies. Radar altimeters (RAs) have become powerful enough to help scientists investigate mesoscale eddies, while the investigation on sub-mesoscale ones is challenging due to their small size and short lifetimes.

SAR, with its high spatial resolution and independence of daytime and weather conditions, has shown unique capability in sub-mesoscale eddy observation. In this study, scientists developed an algorithm for automatic detection of ocean eddies observed in SAR imagery and the extraction of their spatial features, called Eddy detection based on the YOLO algorithm (EOLO).

Based on the ocean eddy dataset (named EddyDataset) established from Sentinel-1 (S1) SAR data, a series of improvements, e.g., channel attention mechanism and new feature fusion method, were made and a high-quality eddy detection network with 91.5% average precision was obtained. EOLO was applied to a large number of randomly selected SAR data acquired over the Red Sea, the Baltic Sea, and the Western Mediterranean Sea, achieving 96.6%, 98.8% and 98.9% precision, respectively, which suggests EOLO's good generalization ability.

Based on the S1 data acquired over the Western Mediterranean Sea in 2021, 8056 SAR eddies were detected by the EOLO network. Comparing these eddies with those identified by RA during the same period, it was found that there is a remarkable difference in the scale of the ocean eddies. Typical diameters of SAR eddies range from 2 km to 20 km while those of RA eddies range from 50 km to 170 km. Quantitative analysis showed that about 46% of the ocean eddies, mainly sub-mesoscale eddies, cannot be observed by existing radar altimeters, providing insights into further research on the dynamics of sub-mesoscale and mesoscale ocean eddies.

With its high precision and efficiency, EOLO is expected to advance the understanding of ocean dynamics, climate patterns, and marine ecosystems, ultimately contributing to more accurate environmental predictions and informed decision-making for sustainable ocean management.

"In the next step, the team will continue to improve the generalization capability of the model, and realize the automatic detection of ocean eddies on multi-band spaceborne SAR imagery. On this basis, the team will try to overcome the difficulty of the three-dimensional perspective of sub-mesoscale eddies,” said Prof. LI Xiaoming, the correspondence author of this study.