Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4189-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4189-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery
Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266071, China
Yuan Guo
Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266071, China
Xiaofeng Li
CORRESPONDING AUTHOR
Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266071, China
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Cited
16 citations as recorded by crossref.
- Advancing green tide monitoring: a spatiotemporal analysis in the Southern Yellow Sea using a novel deep learning and remote sensing fusion approach H. Kan et al. https://doi.org/10.1016/j.jag.2026.105398
- Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model M. Ji & C. Zhao https://doi.org/10.3390/rs17091636
- A fully automatic and label-free Sentinel-1 SAR framework for green-tide mapping P. Tang et al. https://doi.org/10.1016/j.jag.2025.105036
- Simulation Study of the Effect of Multi-Angle ATI-SAR on Sea Surface Current Retrieval Accuracy J. Chen et al. https://doi.org/10.3390/rs17193383
- AI-revealed long-term spatio-temporal characteristics of yellow sea green tides X. Pan et al. https://doi.org/10.1080/15481603.2026.2647380
- A knowledge-guided deep learning framework for Ulva prolifera green tide detection and quantification on MODIS imagery Z. Yin et al. https://doi.org/10.1080/15481603.2026.2666447
- Approaches, challenges and prospects for modeling macroalgal dynamics in the green tide: The case of Ulva prolifera H. Chang et al. https://doi.org/10.1016/j.marpolbul.2025.117897
- A Novel Green Tide Detection Method Based on Superpixel Fisher Vectors in Multispectral Remote Sensing Images Y. Zhou et al. https://doi.org/10.23919/cje.2025.00.157
- Deep Learning-Based Identification of Marine Mucilage in the Sea of Marmara from MODIS Images Q. Xu et al. https://doi.org/10.1109/JSTARS.2025.3648399
- Multi–scale drift characteristics of Ulva prolifera in the Yellow Sea derived from deep learning–based MODIS and Sentinel-1 observations X. Geng et al. https://doi.org/10.1016/j.marpolbul.2026.119516
- L- and C-Band SAR Backscattering Characteristics of Green Tide in the Yellow Sea Y. Guo et al. https://doi.org/10.1109/TGRS.2026.3694915
- Interpreting spatiotemporal dynamics of Ulva prolifera blooms in the southern yellow sea using an attention-enhanced transformer framework Y. Wang et al. https://doi.org/10.1016/j.envpol.2025.126999
- PAD: Phase–Amplitude Decoupling Fusion for Multimodal Land Cover Classification H. Zheng et al. https://doi.org/10.1109/TGRS.2025.3621902
- Exploring the potential of cold patches as an indicator for algal bloom occurrence and migration H. Han et al. https://doi.org/10.1016/j.marpolbul.2025.118588
- A decision-making framework by large language model for green tide salvage ship scheduling C. Feng et al. https://doi.org/10.1016/j.eswa.2025.130590
- DMDNet: Decoupled Multimodal Detection Network for Fine-Grained Ulva Prolifera Segmentation X. Lyu et al. https://doi.org/10.3390/rs18173052
16 citations as recorded by crossref.
- Advancing green tide monitoring: a spatiotemporal analysis in the Southern Yellow Sea using a novel deep learning and remote sensing fusion approach H. Kan et al. https://doi.org/10.1016/j.jag.2026.105398
- Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model M. Ji & C. Zhao https://doi.org/10.3390/rs17091636
- A fully automatic and label-free Sentinel-1 SAR framework for green-tide mapping P. Tang et al. https://doi.org/10.1016/j.jag.2025.105036
- Simulation Study of the Effect of Multi-Angle ATI-SAR on Sea Surface Current Retrieval Accuracy J. Chen et al. https://doi.org/10.3390/rs17193383
- AI-revealed long-term spatio-temporal characteristics of yellow sea green tides X. Pan et al. https://doi.org/10.1080/15481603.2026.2647380
- A knowledge-guided deep learning framework for Ulva prolifera green tide detection and quantification on MODIS imagery Z. Yin et al. https://doi.org/10.1080/15481603.2026.2666447
- Approaches, challenges and prospects for modeling macroalgal dynamics in the green tide: The case of Ulva prolifera H. Chang et al. https://doi.org/10.1016/j.marpolbul.2025.117897
- A Novel Green Tide Detection Method Based on Superpixel Fisher Vectors in Multispectral Remote Sensing Images Y. Zhou et al. https://doi.org/10.23919/cje.2025.00.157
- Deep Learning-Based Identification of Marine Mucilage in the Sea of Marmara from MODIS Images Q. Xu et al. https://doi.org/10.1109/JSTARS.2025.3648399
- Multi–scale drift characteristics of Ulva prolifera in the Yellow Sea derived from deep learning–based MODIS and Sentinel-1 observations X. Geng et al. https://doi.org/10.1016/j.marpolbul.2026.119516
- L- and C-Band SAR Backscattering Characteristics of Green Tide in the Yellow Sea Y. Guo et al. https://doi.org/10.1109/TGRS.2026.3694915
- Interpreting spatiotemporal dynamics of Ulva prolifera blooms in the southern yellow sea using an attention-enhanced transformer framework Y. Wang et al. https://doi.org/10.1016/j.envpol.2025.126999
- PAD: Phase–Amplitude Decoupling Fusion for Multimodal Land Cover Classification H. Zheng et al. https://doi.org/10.1109/TGRS.2025.3621902
- Exploring the potential of cold patches as an indicator for algal bloom occurrence and migration H. Han et al. https://doi.org/10.1016/j.marpolbul.2025.118588
- A decision-making framework by large language model for green tide salvage ship scheduling C. Feng et al. https://doi.org/10.1016/j.eswa.2025.130590
- DMDNet: Decoupled Multimodal Detection Network for Fine-Grained Ulva Prolifera Segmentation X. Lyu et al. https://doi.org/10.3390/rs18173052
Saved (final revised paper)
Latest update: 23 Sep 2026
Short summary
Since 2008, the Yellow Sea has faced a significant ecological issue, the green tide, which has become one of the world's largest marine disasters. Satellite remote sensing plays a pivotal role in detecting this phenomenon. This study uses AI-based models to extract the daily green tide from MODIS and SAR images and integrates these daily data to introduce a continuous weekly dataset, which aids research in disaster simulation, forecasting, and prevention.
Since 2008, the Yellow Sea has faced a significant ecological issue, the green tide, which has...
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