A Deep Learning Model for Eddy Tracking Based on Multi-Source Remote Sensing Imagery

Mesoscale eddies are swirling water exiting ubiquitously in the global ocean. They are significant for material and energy transit and global ocean circulation. Using satellite sea surface height anomaly (SSHA) and sea surface temperature (SST) images, this study proposes a new automated eddy tracking model based on deep learning (DL) technology. Compared with existing eddy tracking algorithms, the DL-based model fuses SSHA and SST data for eddy tracking. We aim to solve problems as eddy splits, mergers, and transient ‘disappearance’ that occurred using SSHA data. The DL-based eddy tracking model is applied in the Kuroshio Extension, and the result verified the model's tracking accuracy and efficiency.