A Deep Learning Model for Oceanic Mesoscale Eddy Detection Based on Multi-source Remote Sensing Imagery

Mesoscale eddies are circular flowing currents that can retain and transport salt, heat, and nutrients all around the ocean. Mesoscale eddies can be detected on remote sensing images, i.e., sea surface height (SSH) images, sea surface temperature (SST) images, chlorophyll concentration images, etc. Most existing automatic eddy detection algorithms are developed based on one kind of remote sensing data. There is a lack of an automatic eddy detection algorithm that can make full use of multi-source remote sensing data to ensure the accuracy and efficiency of eddy detection. The paper proposes a multi-modal U-Net model, a deep neural network-based framework for eddy detection from multi-source remote sensing images. Compared with the previous eddy detection methods, the newly proposed method improves the accuracy and efficiency of eddy detection by using fusion data of SSH and SST.

1. Introduction

Mesoscale eddies are circular currents of water bodies that widely exist in the global oceans. They play a significant role in the transport of momentum, mass, heat, nutrients, salt, and other seawater chemical elements across the ocean basins. They also impact the global ocean circulation, large-scale water distribution, air-sea coupling, and biological activities [1]–[5]. Cyclonic (anticyclonic) eddies drive local upwelling (downwelling), leading to negative (positive) sea surface height (SSH) and sea surface temperature (SST) anomalies. The satellite-measured SSH and SST images can, therefore, be used to obtain information on the characteristics of oceanic eddies.