AI-revealed long-term spatio-temporal characteristics of yellow sea green tides
Year: 2026
Type: article
Abstract: Amid accelerating global environmental change over recent decades, floating macroalgae blooms have expanded worldwide. Notably, the annual Ulva prolifera bloom in the Yellow Sea has emerged as the world's largest green tide disaster, significantly impacting the marine environment and economy. However, the complex ocean–atmosphere environment presents challenges for high-accuracy automatic remote sensing detection. Consequently, a long-term, high-resolution daily dataset remains absent, which limits understanding of spatiotemporal characteristics and hinders disaster prevention. In this study, we designed a high-accuracy automated model, Comprehensive Algae Net (CANet), for green tide detection in satellite images using deep learning. The model enables high-accuracy green tide detection in cloud edges and turbid waters, with a Kappa coefficient of 0.875, and outperforms traditional models such as U-Net and FCN. Based on CANet, we constructed a daily 250-m-resolution multivariate dataset (2007–2024) capturing unmixed area and biomass dynamics. Key findings indicate that over the past 18 years, green tides have cumulatively affected 23% (70,914 km²) of the South Yellow Sea, spanning 526 km east–west and 682 km north–south, with an average annual duration of 82 ± 14 days. Despite interannual variations, the disaster scale can be predicted from the initial area (R² = 0.66, p < 0.05). The model and dataset provide a valuable basis for mechanism analysis and improved operational monitoring.
Source: GIScience & Remote Sensing
Authors: Xinliang Pan, Longxiao Zheng, Xiaofeng Li, Jiangtao Li, Mengqiu Wang, Yanfang Xiao, Rongjie Liu, Keunyong Kim, Song Gao, Xiaomin Ye, Maohua Guo, Ying Xu, Lie Sun, Ning Wang, Xiaoqing Cai, Joo-Hyung Ryu, Jue Huang, Yuhan Zhang, Huanhong Ji, Tingwei Cui
Institutions: Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Shandong University of Science and Technology, Chinese Academy of Sciences, Institute of Oceanology, Ocean University of China, University of Hong Kong, Ministry of Natural Resources, First Institute of Oceanography, Korea Institute of Ocean Science and Technology, National Satellite Ocean Application Service
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Awards: G4405291825, G7271035342
OpenAlex ID: W7140221026