06 May 2024
 | 06 May 2024
Status: this preprint is currently under review for the journal ESSD.

Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Le Gao, Yuan Guo, and Xiaofeng Li

Abstract. Since 2008, the Yellow Sea has experienced a world's largest-scale marine disasters, known as the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced AI models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 Synthetic Aperture Radar (SAR) images. Most importantly, this study presents a continuous and seamless weekly average green tide coverage dataset with the resolution of 500 m, by integrating high precise daily optical and SAR data during each week during the green tide breakout. The uncertainty assessment of this weekly product shows it is completely consistent with the overall direct average of the daily product (R2=1 and RMSE=0). Additionally, the individual case verification in 2019 also shows that the weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic curve-like characteristics, with an low uncertainty (R2=0.89 and RMSE=275 km2).This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at (Gao et al., 2024).

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Le Gao, Yuan Guo, and Xiaofeng Li

Status: open (until 12 Jun 2024)

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Le Gao, Yuan Guo, and Xiaofeng Li

Data sets

The green tide coverage product in the Yellow Sea during 2008-2022 Le Gao, Yuan Guo, and Xiaofeng Li

Le Gao, Yuan Guo, and Xiaofeng Li


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Short summary
Since 2008, the Yellow Sea has faced a concerning ecological issue called the green tide, becoming one of the world's largest marine disasters. Satellite remote sensing, bolstered by artificial intelligence (AI), plays a pivotal role in detecting this phenomenon. Our study utilizes AI models to extract green tide from MODIS and SAR images, achieving promising results. We introduce a continuous weekly dataset spanning 15 years, aiding research in forecasting, and disaster prevention.