11 citations as recorded by crossref.

1. Consistent long-term observations of surface phytoplankton functional types from space H. Xi et al. https://doi.org/10.5194/sp-6-osr9-7-2025
2. A Global, Gap-Free daily Ocean-Colour dataset (2000–2024) for robust marine ecological monitoring Y. Zhang et al. https://doi.org/10.1016/j.jag.2026.105532
3. Responses of phytoplankton functional types to marine heatwaves in China’s marginal seas and adjacent waters F. Li et al. https://doi.org/10.3389/fmars.2026.1828780
4. AI in Satellite Remote Sensing of the Ocean X. Li et al. https://doi.org/10.1109/JPROC.2026.3664121
5. Spatiotemporal Variations of Phytoplankton Groups and Their Relationships with Mesoscale Eddies in the Northwest Pacific J. Wen et al. https://doi.org/10.3390/jmse14090789
6. Coupling ecological concepts with an ocean-colour model: inversion modelling X. Sun et al. https://doi.org/10.3389/frsen.2025.1692306
7. Southern Ocean regime shift: divergent trends in surface phytoplankton community composition using a diagnostic pigment framework N. Adroli et al. https://doi.org/10.1038/s41467-026-75303-2
8. 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
9. Global estimation of phytoplankton functional types using PACE OCI hyperspectral data and a novel deep ensemble learning approach Y. Zhang et al. https://doi.org/10.1016/j.jag.2026.105225
10. Hyperspectral Satellite-Derived Phytoplankton Diagnostic Pigments in the East China Sea: Spatiotemporal Patterns and Ecological Response to Coastal Fronts H. Wang et al. https://doi.org/10.1109/JSTARS.2026.3723474
11. CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space M. Zoffoli et al. https://doi.org/10.1016/j.srs.2025.100212