13 citations as recorded by crossref.

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2. Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model M. Ji & C. Zhao https://doi.org/10.3390/rs17091636
3. 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
4. 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
5. AI-revealed long-term spatio-temporal characteristics of yellow sea green tides X. Pan et al. https://doi.org/10.1080/15481603.2026.2647380
6. 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
7. 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
8. 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
9. 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
10. 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
11. PAD: Phase–Amplitude Decoupling Fusion for Multimodal Land Cover Classification H. Zheng et al. https://doi.org/10.1109/TGRS.2025.3621902
12. 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
13. 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