31 citations as recorded by crossref.

1. Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model M. Ji & C. Zhao https://doi.org/10.3390/rs17091636
2. A Coastal Zone Imager-Based Model for Assessing the Distribution of Large Green Algae in the Northern Coastal Waters of China T. Mao et al. https://doi.org/10.3390/jmse14020140
3. Environmental factors affecting Ulva prolifera blooms in the South Yellow Sea: Insights from deep learning models with attention mechanisms Y. Wang et al. https://doi.org/10.1016/j.rsma.2025.104304
4. Biophysical interactions control the progression of harmful algal blooms in Chesapeake Bay: A novel Lagrangian particle tracking model with mixotrophic growth and vertical migration J. Xiong et al. https://doi.org/10.1002/lol2.10308
5. 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
6. Revealing the anomalous 2025 green tide trajectory in the Southern Yellow Sea using multi-source satellite imagery and VB-FAH H. Kan et al. https://doi.org/10.1016/j.marpolbul.2026.119356
7. Characterizing distribution patterns of small algae patches across the northern and southern sides of the Yellow Sea front using synchronous CZI-MODIS images Z. Yin et al. https://doi.org/10.1080/01431161.2025.2492410
8. Estimating windage coefficient of floating marine green tide of Ulva prolifera using UAV optical images X. Zheng et al. https://doi.org/10.1016/j.marpolbul.2025.117834
9. 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
10. Monitoring, modeling and projection of harmful algal blooms in China W. Guan et al. https://doi.org/10.1016/j.hal.2021.102164
11. Storm-induced coastward expansion of Margalefidinium polykrikoides bloom in Chesapeake Bay J. Xiong et al. https://doi.org/10.1016/j.marpolbul.2022.114187
12. Identification and assessment of the drift velocity of green tides using the maximum cross-correlation method in the Yellow Sea M. Ji et al. https://doi.org/10.1016/j.marpolbul.2023.115420
13. Predicting massive floating macroalgal blooms in a regional sea F. Zhou et al. https://doi.org/10.1016/j.envsoft.2024.106310
14. Numerical Modeling of a Green Tide Migration Process with Multiple Artificial Structures in the Western Bohai Sea, China X. Han et al. https://doi.org/10.3390/app12063017
15. Modeling of algal blooms: Advances, applications and prospects Y. Wang et al. https://doi.org/10.1016/j.ocecoaman.2024.107250
16. A deep-learning framework to detect green tide from MODIS images W. Zhu et al. https://doi.org/10.3389/frsen.2025.1578841
17. Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery S. Xu et al. https://doi.org/10.3390/rs15082196
18. Sedimentation and drag in drifting macrophytes and plastic objects: a model F. Gronwald et al. https://doi.org/10.1038/s41598-025-28893-8
19. Temporal and spatial distribution of floating marine macro litter in the offshore waters of the Bohai Sea and Yellow Sea (BYS) G. Teng et al. https://doi.org/10.1016/j.marpolbul.2024.117232
20. Shifting Dynamics for Coastal Upwelling and Surface Cold Water Patch: A Synergy of HF-Radar, In-Site Observation, Satellite, and Numerical Model Y. Wang et al. https://doi.org/10.1109/TGRS.2025.3559935
21. Forecasting green tide events in a semi-closed tidal flat using artificial intelligence and environmental big data E. Yauchi et al. https://doi.org/10.1080/24705357.2023.2204107
22. 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
23. Distribution characteristics of green tides and its impact on environment in the Yellow Sea S. Wang et al. https://doi.org/10.1016/j.marenvres.2022.105756
24. Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea Y. Men et al. https://doi.org/10.3390/jmse11122212
25. Multi-scenario modeling of the Ulva growth supports risk management for oyster farming on a coastal flat in western central Taiwan T. Lu et al. https://doi.org/10.1016/j.marpolbul.2025.118402
26. The regulatory effect of environmental factors on green tide blooms in the Yellow Sea: A spatio-temporal downscaling numerical reconstruction method L. Fan et al. https://doi.org/10.1016/j.marenvres.2025.107689
27. Exploring the Green Tide Transport Mechanisms and Evaluating Leeway Coefficient Estimation via Moderate-Resolution Geostationary Images M. Ji et al. https://doi.org/10.3390/rs16162934
28. Research on the ensemble forecasting method for green tide paths in the Yellow Sea based on parameter perturbation Y. Zhu et al. https://doi.org/10.1016/j.marpolbul.2025.117717
29. Strong interannual variation of green tides in the southern Yellow Sea: Crucial factors and implications on management strategies Y. Li et al. https://doi.org/10.1016/j.marpolbul.2025.117989
30. Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery L. Gao et al. https://doi.org/10.5194/essd-16-4189-2024
31. A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides X. Geng et al. https://doi.org/10.3389/fmars.2025.1546289