Presented on February 14, 2024, by

Yannik Glasser
PhD Candidate, Information & Computer Science
Justin Stopa, Ph.D.
Associate Professor Department of Ocean & Resources Engineering


Synthetic aperture radars (SAR) aboard space-borne satellites measure sub-mesoscale oceanic and atmospheric phenomena at a global scale. This work uses the European Space Agency (ESA) Sentinel-1 (S-1) SAR mission’s sea surface roughness to study sub-mesoscale phenomena such as turbulence in the atmosphere, rain, and slicks. To utilize the large S-1 archive (>1Pb), we develop automatic image detection methods using deep learning. We developed a foundational contrastive model trained solely on millions of S-1 images. This model, called WVNET, exceeds the performance of other models such as ImageNet in a variety of tasks including regression and classification. With the improved model performance, we have more confidence in estimating the climatology of the sea surface roughness morphology. We find the atmosphere dominates the SAR imagery. The time-space mapping of WVNet’s predictions is relevant for the study of air-sea interactions.