Articles | Volume 10, issue 11
https://doi.org/10.5194/bg-10-7553-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-10-7553-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Nov 2013
Research article |
| 22 Nov 2013
Distribution of phytoplankton functional types in high-nitrate, low-chlorophyll waters in a new diagnostic ecological indicator model
A. P. Palacz
National Institute of Aquatic Resources, Denmark Technical University, Jægersborg Allé 1, 2920 Charlottenlund, Denmark
M. A. St. John
National Institute of Aquatic Resources, Denmark Technical University, Jægersborg Allé 1, 2920 Charlottenlund, Denmark
R. J. W. Brewin
Plymouth Marine Laboratory, Plymouth, UK
T. Hirata
Faculty of Environmental Earth Science, Hokkaido University, N10W5, Sapporo, 060-0810 Hokkaido, Japan
W. W. Gregg
NASA Global Modeling and Assimilation Office, Greenbelt, MD, USA
Viewed
Total article views: 7,400 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 May 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,394 | 3,792 | 214 | 7,400 | 197 | 182 |
- HTML: 3,394
- PDF: 3,792
- XML: 214
- Total: 7,400
- BibTeX: 197
- EndNote: 182
Total article views: 5,697 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Nov 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,386 | 3,135 | 176 | 5,697 | 180 | 178 |
- HTML: 2,386
- PDF: 3,135
- XML: 176
- Total: 5,697
- BibTeX: 180
- EndNote: 178
Total article views: 1,703 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 May 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,008 | 657 | 38 | 1,703 | 17 | 4 |
- HTML: 1,008
- PDF: 657
- XML: 38
- Total: 1,703
- BibTeX: 17
- EndNote: 4
Cited
26 citations as recorded by crossref.
- Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier K. Yala et al. https://doi.org/10.5194/os-16-513-2020
- Plankton Imagery Data Inform Satellite‐Based Estimates of Diatom Carbon A. Chase et al. https://doi.org/10.1029/2022GL098076
- Phenology of Phytoplankton Size Classes in the Arabian Sea R. Shunmugapandi et al. https://doi.org/10.1109/LGRS.2021.3132660
- Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020) Y. Zhang et al. https://doi.org/10.1016/j.rse.2023.113596
- A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean C. Mouw et al. https://doi.org/10.3389/fmars.2017.00041
- Modelling mussel (Mytilus spp.) microplastic accumulation N. Stamataki et al. https://doi.org/10.5194/os-16-927-2020
- Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective A. Stock & A. Subramaniam https://doi.org/10.3389/fmars.2020.00599
- A neural network‐based method for merging ocean color and Argo data to extend surface bio‐optical properties to depth: Retrieval of the particulate backscattering coefficient R. Sauzède et al. https://doi.org/10.1002/2015JC011408
- Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development A. Bracher et al. https://doi.org/10.3389/fmars.2017.00055
- Ecological niches of open ocean phytoplankton taxa P. Brun et al. https://doi.org/10.1002/lno.10074
- Application of neural networks to model changes in fish community biomass in relation to pressure indicators and comparison with a linear approach D. Dempsey et al. https://doi.org/10.1139/cjfas-2018-0411
- Retrieving the vertical distribution of chlorophyll a concentration and phytoplankton community composition from in situ fluorescence profiles: A method based on a neural network with potential for global‐scale applications R. Sauzède et al. https://doi.org/10.1002/2014JC010355
- Optical characterization of marine phytoplankton assemblages within surface waters of the western Arctic Ocean R. Reynolds & D. Stramski https://doi.org/10.1002/lno.11199
- Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class R. Brewin et al. https://doi.org/10.1016/j.rse.2015.07.004
- Retrieval of global surface phytoplankton community structure using a minimal set of predictors X. Li et al. https://doi.org/10.1016/j.rse.2025.115174
- Estimation of phytoplankton community composition from satellite data using a fuzzy and probabilistic combination model in mountainous reservoirs: A case of Huating Lake in spring and summer Y. Qiu et al. https://doi.org/10.1016/j.ecoinf.2025.103153
- Struktur Komunitas, Cadangan Karbon, dan Estimasi Nilai Ekonomi Mangrove di Muara Sungai Musi H. Farahisah et al. https://doi.org/10.18343/jipi.26.2.228
- Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning Q. Zhu et al. https://doi.org/10.3390/rs11172001
- Incorporating environmental data in abundance-based algorithms for deriving phytoplankton size classes in the Atlantic Ocean T. Moore & C. Brown https://doi.org/10.1016/j.rse.2020.111689
- Relative contributions of photophysiology and chlorophyll-a abundance to phytoplankton group-specific primary production in the Kuroshio region as inferred by satellite ocean color remote sensing T. Hirata & K. Suzuki https://doi.org/10.1007/s10872-022-00638-5
- Sensing the ocean biological carbon pump from space: A review of capabilities, concepts, research gaps and future developments R. Brewin et al. https://doi.org/10.1016/j.earscirev.2021.103604
- Introduction to the BASIN Special Issue: State of art, past present a view to the future M. St. John et al. https://doi.org/10.1016/j.pocean.2014.11.007
- Ocean Biology Studied from Space S. Sathyendranath et al. https://doi.org/10.1007/s10712-023-09805-9
- Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data H. Xi et al. https://doi.org/10.1016/j.rse.2020.111704
- Phytoplankton composition from sPACE: Requirements, opportunities, and challenges I. Cetinić et al. https://doi.org/10.1016/j.rse.2023.113964
- Optimizing an Abundance-Based Model for Satellite Remote Sensing of Phytoplankton Size Classes in the Bohai and Yellow Seas of China Y. Wang et al. https://doi.org/10.1109/TGRS.2024.3383391
26 citations as recorded by crossref.
- Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier K. Yala et al. https://doi.org/10.5194/os-16-513-2020
- Plankton Imagery Data Inform Satellite‐Based Estimates of Diatom Carbon A. Chase et al. https://doi.org/10.1029/2022GL098076
- Phenology of Phytoplankton Size Classes in the Arabian Sea R. Shunmugapandi et al. https://doi.org/10.1109/LGRS.2021.3132660
- Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020) Y. Zhang et al. https://doi.org/10.1016/j.rse.2023.113596
- A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean C. Mouw et al. https://doi.org/10.3389/fmars.2017.00041
- Modelling mussel (Mytilus spp.) microplastic accumulation N. Stamataki et al. https://doi.org/10.5194/os-16-927-2020
- Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective A. Stock & A. Subramaniam https://doi.org/10.3389/fmars.2020.00599
- A neural network‐based method for merging ocean color and Argo data to extend surface bio‐optical properties to depth: Retrieval of the particulate backscattering coefficient R. Sauzède et al. https://doi.org/10.1002/2015JC011408
- Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development A. Bracher et al. https://doi.org/10.3389/fmars.2017.00055
- Ecological niches of open ocean phytoplankton taxa P. Brun et al. https://doi.org/10.1002/lno.10074
- Application of neural networks to model changes in fish community biomass in relation to pressure indicators and comparison with a linear approach D. Dempsey et al. https://doi.org/10.1139/cjfas-2018-0411
- Retrieving the vertical distribution of chlorophyll a concentration and phytoplankton community composition from in situ fluorescence profiles: A method based on a neural network with potential for global‐scale applications R. Sauzède et al. https://doi.org/10.1002/2014JC010355
- Optical characterization of marine phytoplankton assemblages within surface waters of the western Arctic Ocean R. Reynolds & D. Stramski https://doi.org/10.1002/lno.11199
- Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class R. Brewin et al. https://doi.org/10.1016/j.rse.2015.07.004
- Retrieval of global surface phytoplankton community structure using a minimal set of predictors X. Li et al. https://doi.org/10.1016/j.rse.2025.115174
- Estimation of phytoplankton community composition from satellite data using a fuzzy and probabilistic combination model in mountainous reservoirs: A case of Huating Lake in spring and summer Y. Qiu et al. https://doi.org/10.1016/j.ecoinf.2025.103153
- Struktur Komunitas, Cadangan Karbon, dan Estimasi Nilai Ekonomi Mangrove di Muara Sungai Musi H. Farahisah et al. https://doi.org/10.18343/jipi.26.2.228
- Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning Q. Zhu et al. https://doi.org/10.3390/rs11172001
- Incorporating environmental data in abundance-based algorithms for deriving phytoplankton size classes in the Atlantic Ocean T. Moore & C. Brown https://doi.org/10.1016/j.rse.2020.111689
- Relative contributions of photophysiology and chlorophyll-a abundance to phytoplankton group-specific primary production in the Kuroshio region as inferred by satellite ocean color remote sensing T. Hirata & K. Suzuki https://doi.org/10.1007/s10872-022-00638-5
- Sensing the ocean biological carbon pump from space: A review of capabilities, concepts, research gaps and future developments R. Brewin et al. https://doi.org/10.1016/j.earscirev.2021.103604
- Introduction to the BASIN Special Issue: State of art, past present a view to the future M. St. John et al. https://doi.org/10.1016/j.pocean.2014.11.007
- Ocean Biology Studied from Space S. Sathyendranath et al. https://doi.org/10.1007/s10712-023-09805-9
- Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data H. Xi et al. https://doi.org/10.1016/j.rse.2020.111704
- Phytoplankton composition from sPACE: Requirements, opportunities, and challenges I. Cetinić et al. https://doi.org/10.1016/j.rse.2023.113964
- Optimizing an Abundance-Based Model for Satellite Remote Sensing of Phytoplankton Size Classes in the Bohai and Yellow Seas of China Y. Wang et al. https://doi.org/10.1109/TGRS.2024.3383391
Saved (final revised paper)
Latest update: 06 Jun 2026
Altmetrics
Final-revised paper
Preprint