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A Submesoscale Eddy Identification Dataset Derived from GOCI I Chlorophyll-a Data based on Deep Learning
  • +2
  • Yan Wang,
  • Jie Yang,
  • Kai Wu,
  • Meng Hou,
  • Ge Chen
Yan Wang
School of Marine Technology, Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China

Corresponding Author:[email protected]

Author Profile
Jie Yang
Laboratory for Regional Oceanography and Numerical Modeling, Laoshan Laboratory, School of Marine Technology, Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China
Kai Wu
School of Marine Technology, Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China
Meng Hou
School of Marine Technology, Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China
Ge Chen
School of Marine Technology, Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Laboratory for Regional Oceanography and Numerical Modeling, Laoshan Laboratory

Abstract

This paper presents an observational dataset on submesoscale eddies, which obtains from high-resolution chlorophyll-a distribution images from GOCI I. We employed a combination of digital image processing, filtering, YOLOv7-X, and small object detection techniques, along with specific chlorophyll image enhancement processing, to extract information on submesoscale eddies, including their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score, which covers eight daily periods between 00:00 and 08:00 (UTC) from April 1, 2011, to March 31, 2021. We identified a total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies at a confidence threshold of 0.2. The mean radius of anticyclonic 15 eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). The unprecedented hourly resolution dataset on submesoscale eddies provides information on their distribution, morphology, and energy dissipation, making it a significant contribution to submesoscale eddies of study. The dataset is available at https://doi.org/10.5281/zenodo.7694115.
04 Apr 2023Submitted to ESS Open Archive
10 Apr 2023Published in ESS Open Archive