Articles | Volume 18, issue 11
https://doi.org/10.5194/essd-18-8255-2026
© Author(s) 2026. 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-18-8255-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| Highlight paper
| 
11 Nov 2026
Data description article | Highlight paper |
| 11 Nov 2026
| 11 Nov 2026
A new global dataset of photosynthesis parameters
Žarko Kovač
Faculty of Science, University of Split, Rudera Boškovića 33, 21000 Split, Croatia
Marija Bačeković Koloper
CORRESPONDING AUTHOR
Faculty of Science, University of Split, Rudera Boškovića 33, 21000 Split, Croatia
University of Split, Faculty of Science, Doctoral study of Biophysics, Rudera Boškovića 33, 21000 Split, Croatia
Shubha Sathyendranath
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
National Centre for Earth Observations, Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
Gemma Kulk
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
National Centre for Earth Observations, Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
Heather Bouman
Department of Earth Sciences, University of Oxford, Oxford, OX1 3AN, UK
Leon Ćatipović
Faculty of Science, University of Split, Rudera Boškovića 33, 21000 Split, Croatia
Related authors
Jozef Skákala, Shubha Sathyendranath, Yuri Artioli, Deep S. Banerjee, Heather Bouman, Robert J. W. Brewin, Momme Butenschön, Stefano Ciavatta, Stephanie Dutkiewicz, Yanna Fidai, David Ford, Grinson George, Karen Guihou, Bror Jönsson, Marija Bačeković Koloper, Žarko Kovač, Lekshmi Krishnakumary, Gemma Kulk, Charlotte Laufkötter, Gennadi Lessin, Jann Paul Mattern, Angélique Melet, Alexandre Mignot, David Moffat, Fanny Monteiro, Mayra Rodriguez Bennadji, Cécile S. Rousseaux, Ranjini Swaminathan, Osvaldo Ulloa, and Jerry Tjiputra
Ocean Sci., 22, 1457–1481, https://doi.org/10.5194/os-22-1457-2026, https://doi.org/10.5194/os-22-1457-2026, 2026
Short summary
Short summary
Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Qi Zheng, Johannes J. Viljoen, Xuerong Sun, Žarko Kovač, Shubha Sathyendranath, and Robert J. W. Brewin
Biogeosciences, 22, 3253–3278, https://doi.org/10.5194/bg-22-3253-2025, https://doi.org/10.5194/bg-22-3253-2025, 2025
Short summary
Short summary
Phytoplankton contribute to half of Earth’s primary production, but not a lot is known about subsurface phytoplankton, living at the base of the sunlit ocean. We develop a two-layered box model to simulate phytoplankton seasonal and interannual variations in different depth layers of the ocean. Our model captures seasonal and long-term trends of the two layers, explaining how they respond to a warming ocean, furthering our understanding of how phytoplankton are responding to climate change.
Alexandre Mignot, Thomas J. Ryan-Keogh, Gemma Kulk, David Antoine, Marine Bretagnon, Stefano Ciavatta, Hervé Claustre, Marin Cornec, Zhongping Lee, Patrick Lehodey, Antoine Mangin, Raphaëlle Sauzède, Shubha Sathyendranath, Greg M. Silsbe, Julia Uitz, and Toby Westberry
State Planet Discuss., https://doi.org/10.5194/sp-2026-34, https://doi.org/10.5194/sp-2026-34, 2026
Preprint under review for SP
Short summary
Short summary
Phytoplankton account for about half of Earth's primary production, sustaining marine food webs and fisheries and supporting carbon storage. Satellite estimates disagree on how this production is changing. We combined eight estimates from 1998–2025, weighted by performance and independence. The combined estimate indicates a decline of about 2% per decade in global mean marine net primary production, providing a new indicator to monitor changes in ocean ecosystems and the carbon cycle.
Salem Ibrahim Salem, Andre Valente, Shubha Sathyendranath, Andrei Chuprin, Théo Renard, Jaime Pitarch, Ruth Airs, Moreno Ana, Maria-Manuel Angelico, David Antoine, Robert Arnone, Florinda Artuso, Paulo O. Oliveira, William Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Marco Bellacicco, Jean-François Berthon, Şükrü Beşiktepe, Francesco Bignami, Yngve Borsheim, Astrid Bracher, Federica Braga, Vittorio Brando, Robert J. W. Brewin, Vanda Brotas, Elisabetta Canuti, Francisco P. Chavez, Andrés Cianca, Hervé Claustre, Lesley Clementson, Simone Colella, Raul Costa, Cristina Nunes, Richard Crout, Emmanuel Devred, Annalisa Di Cicco, Federico Falcini, Ivan Farace, Afonso Ferreira, Scott Freeman, Robert Frouin, Carlos García-Soto, Isabella Giardina, Stuart W. Gibb, Arthur Gleason, Ralf Goericke, Richard Gould, Michael Grant, Nathalie Guillocheau, Hiroto Higa, Stanford B. Hooker, Chuamin Hu, Joji Ishizaka, Thomas Jackson, Mati Kahru, Milton Kampel, Bokyung Kim, Holger Klein, Piotr Kowalczuk, Susanne Kratzer, Raphael Kudela, Antonia Lai, Rosario Lavezza, Jesus Ledesma, Huiwon Lee, Steven Lohrenz, Hubert Loisel, Vivian Lutz, Antonio Mannino, Francesca Margiotta, Victor Martinez-Vicente, Salvatore Marullo, Luca Massi, Patricia Matrai, Atsushi Matsuoka, David McKee, Chiara Melillo, Carlos B. Mendes, Brian G. Mitchell, Tiffany Moisan, Enrique Montes, Frank Muller-Karger, Aimee Neeley, Michael Novak, Leonie O'Dowd, Michael Ondrusek, Emanuele Organelli, Heeyoon Park, Trevor Platt, Francesca Polonelli, Alex Poulton, Mariella Ragni, Michel Repecaud, Maurizio Ribera d’Alcalà, Tamara Rodriguez-Ramos, Rüdiger Röttgers, Manuel Ruiz-Villarreal, Michela Sammartino, Rosalia Santoleri, Gian Marco Scarpa, Thomas Schroeder, Valeria Segura, Timothy Smyth, Denise Smythe-Wright, Heidi M. Sosik, Joanna Stoń-Egiert, Malcolm Taberner, Jing Tan, Rob Thomas, Crystal Thomas, Gavin Tilstone, Andreia Tracana, Michael Twardowski, Vincenzo Vellucci, Gianluca Volpe, Kenneth Voss, Jeremy Werdell, Marcel Wernand, Bozena Wojtasiewicz, Simon Wright, Sunmi Yang, Giuseppe Zibordi, Maria Laura Zoffoli, and Steve Groom
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-539, https://doi.org/10.5194/essd-2026-539, 2026
Preprint under review for ESSD
Short summary
Short summary
This paper presents the fourth version of the ESA Ocean Colour Climate Change Initiative global in situ bio-optical database. The updated compilation combines observations from many international data sources collected between 1996 and 2025 and includes major expansions in coverage and variables. The dataset provides a consistent reference for validating satellite ocean-colour products and supports algorithm development and long-term climate studies.
Jozef Skákala, Shubha Sathyendranath, Yuri Artioli, Deep S. Banerjee, Heather Bouman, Robert J. W. Brewin, Momme Butenschön, Stefano Ciavatta, Stephanie Dutkiewicz, Yanna Fidai, David Ford, Grinson George, Karen Guihou, Bror Jönsson, Marija Bačeković Koloper, Žarko Kovač, Lekshmi Krishnakumary, Gemma Kulk, Charlotte Laufkötter, Gennadi Lessin, Jann Paul Mattern, Angélique Melet, Alexandre Mignot, David Moffat, Fanny Monteiro, Mayra Rodriguez Bennadji, Cécile S. Rousseaux, Ranjini Swaminathan, Osvaldo Ulloa, and Jerry Tjiputra
Ocean Sci., 22, 1457–1481, https://doi.org/10.5194/os-22-1457-2026, https://doi.org/10.5194/os-22-1457-2026, 2026
Short summary
Short summary
Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Daniel J. Ford, Gemma Kulk, Shubha Sathyendranath, and Jamie D. Shutler
Earth Syst. Sci. Data, 18, 569–584, https://doi.org/10.5194/essd-18-569-2026, https://doi.org/10.5194/essd-18-569-2026, 2026
Short summary
Short summary
Chlorophyll a is routinely monitored using ocean colour satellites, however, these data records have gaps. Here we present a methodology to provide a spatially and temporally complete chlorophyll-a record, using Biogeochemical Argo floats as a constraint on wintertime chlorophyll-a, and a statistical kriging approach to fill cloud gaps. Thereby, providing a complete record at monthly 0.25° resolution between 1997 and 2024, consistent to the underlying climate data record.
Qi Zheng, Johannes J. Viljoen, Xuerong Sun, Žarko Kovač, Shubha Sathyendranath, and Robert J. W. Brewin
Biogeosciences, 22, 3253–3278, https://doi.org/10.5194/bg-22-3253-2025, https://doi.org/10.5194/bg-22-3253-2025, 2025
Short summary
Short summary
Phytoplankton contribute to half of Earth’s primary production, but not a lot is known about subsurface phytoplankton, living at the base of the sunlit ocean. We develop a two-layered box model to simulate phytoplankton seasonal and interannual variations in different depth layers of the ocean. Our model captures seasonal and long-term trends of the two layers, explaining how they respond to a warming ocean, furthering our understanding of how phytoplankton are responding to climate change.
Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath
Geosci. Model Dev., 16, 4639–4657, https://doi.org/10.5194/gmd-16-4639-2023, https://doi.org/10.5194/gmd-16-4639-2023, 2023
Short summary
Short summary
While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Tihomir S. Kostadinov, Lisl Robertson Lain, Christina Eunjin Kong, Xiaodong Zhang, Stéphane Maritorena, Stewart Bernard, Hubert Loisel, Daniel S. F. Jorge, Ekaterina Kochetkova, Shovonlal Roy, Bror Jonsson, Victor Martinez-Vicente, and Shubha Sathyendranath
Ocean Sci., 19, 703–727, https://doi.org/10.5194/os-19-703-2023, https://doi.org/10.5194/os-19-703-2023, 2023
Short summary
Short summary
We present a remote sensing algorithm to estimate the size distribution of particles suspended in natural near-surface ocean water using ocean color data. The algorithm can be used to estimate the abundance and carbon content of phytoplankton, photosynthesizing microorganisms that are at the basis of the marine food web and play an important role in Earth’s carbon cycle and climate. A merged, multi-sensor satellite data set and the model scientific code are provided.
André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Thomas Jackson, Andrei Chuprin, Malcolm Taberner, Ruth Airs, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Yngve Borsheim, Astrid Bracher, Vittorio Brando, Robert J. W. Brewin, Elisabetta Canuti, Francisco P. Chavez, Andrés Cianca, Hervé Claustre, Lesley Clementson, Richard Crout, Afonso Ferreira, Scott Freeman, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Ralf Goericke, Richard Gould, Nathalie Guillocheau, Stanford B. Hooker, Chuamin Hu, Mati Kahru, Milton Kampel, Holger Klein, Susanne Kratzer, Raphael Kudela, Jesus Ledesma, Steven Lohrenz, Hubert Loisel, Antonio Mannino, Victor Martinez-Vicente, Patricia Matrai, David McKee, Brian G. Mitchell, Tiffany Moisan, Enrique Montes, Frank Muller-Karger, Aimee Neeley, Michael Novak, Leonie O'Dowd, Michael Ondrusek, Trevor Platt, Alex J. Poulton, Michel Repecaud, Rüdiger Röttgers, Thomas Schroeder, Timothy Smyth, Denise Smythe-Wright, Heidi M. Sosik, Crystal Thomas, Rob Thomas, Gavin Tilstone, Andreia Tracana, Michael Twardowski, Vincenzo Vellucci, Kenneth Voss, Jeremy Werdell, Marcel Wernand, Bozena Wojtasiewicz, Simon Wright, and Giuseppe Zibordi
Earth Syst. Sci. Data, 14, 5737–5770, https://doi.org/10.5194/essd-14-5737-2022, https://doi.org/10.5194/essd-14-5737-2022, 2022
Short summary
Short summary
A compiled set of in situ data is vital to evaluate the quality of ocean-colour satellite data records. Here we describe the global compilation of bio-optical in situ data (spanning from 1997 to 2021) used for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The compilation merges and harmonizes several in situ data sources into a simple format that could be used directly for the evaluation of satellite-derived ocean-colour data.
Cited articles
Amirian, M. M., Finkel, Z. V., Devred, E., and Irwin, A. J.: Parameterization of photoinhibition for phytoplankton, Commun. Earth Environ., 6, 1–9, https://doi.org/10.1038/s43247-025-02686-3, 2025. a, b, c
Antoine, D., Andre, J. M., and Morel, A.: Oceanic primary production. 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll, Global Biogeochem. Cy., 10, 57–69, https://doi.org/10.1029/95GB02832, 1996. a
Babin, M., Morel, A., Claustre, H., Bricaud, A., Kolber, Z., and Falkowski, P. J.: Nitrogen- and irradiance-dependent variations of the maximum quantum yield of carbon fixation in eutrophic, mesotrophic and oligotrophic marine systems, Deep-Sea Res. Pt. I, 43, 1241–1272, https://doi.org/10.1016/0967-0637(96)00058-1, 1996. a
Balch, W., Evans, R., Brown, J., Feldman, G., McClain, C., and Esalas, W.: The Remote Sensing of Ocean Primary Productivity: Use of a New Data Compilation to Test Satellite Algorithms, J. Geophys. Res., 97, 2279–2293, https://doi.org/10.1029/91JC02843, 1992. a
Behrenfeld, M. J. and Falkowski, P. G.: A consumer’s guide to phytoplankton primary productivity models, Limnol. Oceanogr., 42, 1479–1491, https://doi.org/10.4319/lo.1997.42.7.1479, 1997b. a, b
Behrenfeld, M. J., Prasil, O., Babin, M., and Bruyant, F.: In search of a physiological basis for covariations in light-limited and light-saturated photosynthesis, J. Phycol., 40, 4–25, https://doi.org/10.1046/j.1529-8817.2004.03083.x, 2004. a
Bouman, H. A., Platt, T., Doblin, M., Figueiras, F. G., Gudmundsson, K., Gudfinnsson, H. G., Huang, B., Hickman, A., Hiscock, M., Jackson, T., Lutz, V. A., Mélin, F., Rey, F., Pepin, P., Segura, V., Tilstone, G. H., van Dongen-Vogels, V., and Sathyendranath, S.: Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set, Earth Syst. Sci. Data, 10, 251–266, https://doi.org/10.5194/essd-10-251-2018, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Brewin, R., Sathyendranath, S., Kulk, G., Rio, M.-H., Concha, J., Bell, T., Bracher, A., Fichot, C., Frölicher, T., Galí, M., Hansell, D., Kostadinov, T., Mitchell, C., Neeley, A., Organelli, E., Richardson, K., Rousseaux, C., Shen, F., Stramski, D., Tzortziou, M., Watson, A., Addey, C., Bellacicco, M., Bouman, H., Carroll, D., Cetinić, I., Dall’Olmo, G., Frouin, R., Hauck, J., Hieronymi, M., Hu, C., Ibello, V., Jönsson, B., Kong, C., Kovač, v., Laine, M., Lauderdale, J., Lavender, S., Livanou, E., Llort, J., Lorinczi, L., Nowicki, M., Pradisty, N., Psarra, S., Raitsos, D., Ruescas, A., Russell, J., Salisbury, J., Sanders, R., Shutler, J., Sun, X., Taboada, F., Tilstone, G., Wei, X., and Woolf, D.: Ocean carbon from space: Current status and priorities for the next decade, Earth-Sci. Rev., 240, 104386, https://doi.org/10.1016/j.earscirev.2023.104386, 2023. a
Britten, G. L., Jonsson, B., Kul, G., Bouman, H. A., Follows, M. J., and Sathyendranath, S.: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations, Limnol. Oceanogr. Lett., 10: 967–976, https://doi.org/10.1002/lol2.70062, 2025. a
Bruyant, F., Babin, M., Genty, B., Prasil, O., Berhrenfeld, M. J., Claustre, H., Bricaud, A., Garczarek, L., Holtzendorff, J., Koblizek, M., Dousova, H., and Partensky, F.: Diel variations in the photosynthetic parameters of Prochlorococcus strain PCC 9511: combined effects of light and cell cycle, Limnol. Oceanogr., 50, 850–863, https://doi.org/10.4319/lo.2005.50.3.0850, 2005. a
Buitenhuis, E. T., Hashioka, T., and Quéré, L.: Combined constraints on global ocean primary production using observations and models, Global Biogeochem. Cy., 27, 847–858, https://doi.org/10.1002/gbc.20074, 2013. a, b
Campbell, J., Antoine, D., Armstrong, R., Arrigo, K., Balch, W., Barber, R., Behrenfeld, M., Bidigare, R., Bishop, J., Carr, M.-E., Esaias, W., Falkowski, P., Hoepffner, N., Iverson, R., Kiefer, D., Lohrenz, S., Marra, J., Morel, A., Ryan, J., Vedernikov, V., Waters, K., Yentsch, C., and Yoder, J.: Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature, and irradiance, Global Biogeochem. Cy., 16, 9–15, https://doi.org/10.1029/2001GB001444, 2002. a
Devred, E., Sathyendranath, S., and Platt, T.: Delineation of ecological provinces using ocean colour radiometry, Mar. Ecol. Prog. Ser., 346, 1–13, https://doi.org/10.3354/meps07149, 2007. a
Falkowski, P. G.: Light-shade adaptation and assimilation numbers, J. Plankton Res., 3, 203–216, https://doi.org/10.1093/plankt/3.2.203, 1981. a
Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W.: Emergent biogeography of microbial communities in a model ocean, Science, 315, 1843–1846, https://doi.org/10.1126/science.1138544, 2007. a, b
Frenette, J., Demers, S., and Legendre, L.: Lack of agreement among models for estimating the photosynthetic parameters, Limnol. Oceanogr., 38, 679–687, https://doi.org/10.4319/lo.1993.38.3.0679, 1993. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Landschützer, P., Le Quéré, C., Li, H., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Arneth, A., Arora, V., Bates, N. R., Becker, M., Bellouin, N., Berghoff, C. F., Bittig, H. C., Bopp, L., Cadule, P., Campbell, K., Chamberlain, M. A., Chandra, N., Chevallier, F., Chini, L. P., Colligan, T., Decayeux, J., Djeutchouang, L. M., Dou, X., Duran Rojas, C., Enyo, K., Evans, W., Fay, A. R., Feely, R. A., Ford, D. J., Foster, A., Gasser, T., Gehlen, M., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A. K., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Kato, E., Keeling, R. F., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Lan, X., Lauvset, S. K., Lefèvre, N., Liu, Z., Liu, J., Ma, L., Maksyutov, S., Marland, G., Mayot, N., McGuire, P. C., Metzl, N., Monacci, N. M., Morgan, E. J., Nakaoka, S.-I., Neill, C., Niwa, Y., Nützel, T., Olivier, L., Ono, T., Palmer, P. I., Pierrot, D., Qin, Z., Resplandy, L., Roobaert, A., Rosan, T. M., Rödenbeck, C., Schwinger, J., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Steinhoff, T., Sun, Q., Sutton, A. J., Séférian, R., Takao, S., Tatebe, H., Tian, H., Tilbrook, B., Torres, O., Tourigny, E., Tsujino, H., Tubiello, F., van der Werf, G., Wanninkhof, R., Wang, X., Yang, D., Yang, X., Yu, Z., Yuan, W., Yue, X., Zaehle, S., Zeng, N., and Zeng, J.: Global Carbon Budget 2024, Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, 2025. a
Gallegos, C. L. and Platt, T.: Photosynthesis Measurements on Natural Populations of Phytoplankton: Numerical Analysis, in: Physiological bases of phytoplankton ecology, Can. B. Fish. Aquat. Sci., 103–112, 1981. a
Gordon, H. R., Clark, D. K., Brown, J. W., Brown, O. B., Evans, R. H., and Broenkow, W. W.: Phytoplankton pigment concentrations in the Middle Atlantic Bight: comparison of ship determinations and CZCS estimates, Appl. Optics, 22, 20–36, https://doi.org/10.1364/ao.22.000020, 1983. a
Harding, L. W., Meeson, B. W., and Tyler, M. A.: Photoadaptation and diel periodicity of photosynthesis in the dinoflagellate Prorocentrum mariaelebouriae, Mar. Ecol. Prog. Ser., 13, 73–85, https://doi.org/10.3354/meps013073, 1983. a
Jassby, A. D. and Platt, T.: Mathematical formulation of the relationship between photosynthesis and light for phytoplankton, Limnol. Oceanogr., 21, 540–547, https://doi.org/10.4319/lo.1976.21.4.0540, 1976. a, b, c
Jones, C. T., Craig, S. E., Barnett, A. B., MacIntyre, H. L., and Cullen, J. J.: Curvature in models of the photosynthesis-irradiance response, J. Phycol., 50, 341–355, https://doi.org/10.1111/jpy.12164, 2014. a, b
Kirk, J. T. O.: Light and photosynthesis in aquatic ecosystems, Cambridge University Press, 3rd Edn., https://doi.org/10.1017/CBO9781139168212, 2010. a, b
Kovač, Z., Platt, T., Antunović, S., Sathyendranath, S., Morović, M., and Gallegos, C.: Extended Formulations and Analytic Solutions for Watercolumn Production Integrals, Front. Mar. Sci., 4, 163, https://doi.org/10.3389/fmars.2017.00163, 2017a. a, b
Kovač, Z., Platt, T., Sathyendranath, S., and Lomas, M. W.: Extraction of Photosynthesis Parameters from Time Series Measurements of In Situ Production: Bermuda Atlantic Time-Series Study, Remote Sens., 10, 915, https://doi.org/10.3390/rs10060915, 2018. a, b
Kovač, Z., Bačeković Koloper, M., Sathyendranath, S., Kulk, G., Bouman, H., and Ćatipović, L.: Global marine photosynthesis parameters dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.17973417, 2025. a, b, c, d
Kovač, Z., Bačeković Koloper, M., Sathyendranath, S., Kulk, G., Bouman, H., and Ćatipović, L.: Global marine photosynthesis parameters codes, Zenodo [code], https://doi.org/10.5281/zenodo.22645060, 2026a. a
Kovač, Ž., Kovač, A., Vojković, M., Ćatipović, L., Koloper, M. B., and Sathyendranath, S.: Inverse model inferred time series of photosynthesis parameters from three decades of primary production measurements, J. Geophys. Res.-Oceans, 131, e2026JC023997, https://doi.org/10.1029/2026JC023997, 2026b. a
Kulk, G., Platt, T., Dingle, J., Jackson, T., Johnsson, B., Bouman, H. A., Doblin, M., Estrada, M., Figueiras, F. G., Furuya, K., Gonzalez, N., Huang, B., Isada, T. Kovac, Z. L. V., Maranon, E., Raman, M., Richardson, K., Rozema, P. D., van de Poll, W. H., Segura, V., Tilstone, G. H., and Sathyendranath, S.: Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades, Remote Sens., 12, 826, https://doi.org/10.3390/rs12050826, 2020. a, b, c, d, e
Kulk, G., Platt, T., Dingle, J., Jackson, T., Johnsson, B., Bouman, H. A., Doblin, M., Estrada, M., Figueiras, F. G., Furuya, K., Gonzalez, N., Huang, B., Isada, T. Kovac, Z. L. V., Maranon, E., Raman, M., Richardson, K., Rozema, P. D., van de Poll, W. H., Segura, V., Tilstone, G. H., and Sathyendranath, S.: Correction: Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades, Remote Sens., 13, 3462, https://doi.org/10.3390/rs13173462, 2021. a, b, c, d, e
Kyewalyanga, M., Platt, T., and Sathyendranath, S.: Ocean primary production calculated by spectral and broad-band models, Mar. Ecol. Prog. Ser., 85, 171–185, https://doi.org/10.3354/meps085171, 1992. a
Kyewalyanga, M. N., Platt, T., and Sathyendranath, S.: Estimation of the photosynthetic action spectrum: implication for primary production models, Mar. Ecol. Prog. Ser., 146, 207–223, https://doi.org/10.3354/meps146207, 1997. a
Kyewalyanga, M. N., Platt, T., and Satyhendranath, S.: Seasonal variations in physiological parameters of phytoplankton across the North Atlantic, J. Plankton Res., 20, 17–42, https://doi.org/10.1093/plankt/20.1.17, 1998. a
Lewis, M. R., Warnock, R. E., Irwin, B., and Platt, T.: Measuring photosynthetic action spectra of natural phytoplankton populations, J. Phycol., 21, 310–315, https://doi.org/10.1111/j.0022-3646.1985.00310.x, 1985. a
Li, Z., Matsushita, B., and Konohod, A.: Remote estimation of phytoplankton primary production in clear to turbid waters by integrating a semi-analytical model with a machine learning algorithm, Remote Sens. Environ., 275, 113027, https://doi.org/10.1016/j.rse.2022.113027, 2022. a, b, c
Longhurst, A., Sathyendranath, S., and Platt, T.: An estimate of global primary production in the ocean from satellite radiometer data, J. Plankton Res., 17, 1245–1271, https://doi.org/10.1093/plankt/17.6.1245, 1995. a, b, c
MacCaull, W. A. and Platt, T.: Diel variations in the photosynthetipca rameters of coastal marine phytoplankton, Limnol. Oceanogr., 22, 723–731, https://doi.org/10.4319/lo.1977.22.4.0723, 1977. a
MacIntyre, H. L., Kana, T. M., Anning, T., and Geider, R.: Photoacclimation of photosynthesis irradiance response curves and photosynthetic pigments in microalge and cyanobacteria, J. Phycol., 38, 17–38, https://doi.org/10.1046/j.1529-8817.2002.00094.x, 2002. a
Marra, J.: Approaches to the Measurements of Plankton Production, in: Phytoplankton productivity – Carbon assimilation in marine and freshwater ecosystems, Blackwell Science, 78–108, https://doi.org/10.1002/9780470995204, 2002. a
Marra, J.: Net and gross productivity: weighing in with 14C, Aquat. Microb. Ecol., 56, 123–131, https://doi.org/10.3354/ame01306, 2009. a
Marshall, H. L., Geider, R. J., and Flynn, K. J.: A mechanistic model of photoinhibition, New Phytol., 145, 347–359, https://doi.org/10.1046/j.1469-8137.2000.00575.x, 2000. a
Mattei, F. and Scardi, M.: Global marine phytoplankton production dataset, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.932417, 2021b. a
Milligan, A., Halsey, K. H., and Behrenfeld, M. J.: Advancing interpretations of 14C-uptake measurements in the context of phytoplankton physiology and ecology, J. Plankton Res., 37, 692–698, https://doi.org/10.1093/plankt/fbv051, 2015. a
Morel, A. and Smith, R. C.: Relation between total quanta and total energy for aquatic photosynthesis, Limnol. Oceanogr., 19, 591–600, https://doi.org/10.4319/lo.1974.19.4.0591, 1974. a
Nelder, J. A. and Mead, R.: A simplex method for function minimization, Comput. J., 7, 308–313, https://doi.org/10.1093/comjnl/7.4.308, 1965. a, b
Peterson, B. J.: Aquatic primary productivity and the 14C-CO2 method: A History of the productivity problem, Annu. Rev. Ecol. Evol. Syst., 11, 359–385, https://doi.org/10.1146/annurev.es.11.110180.002043, 1980. a
Picart, S. S., Sathyendranath, S., Dowell, M., Moore, T., and Platt, T.: Remote sensing of assimilation number for marine phytoplankton, Remote Sens. Environ., 146, 87–96, https://doi.org/10.1016/j.rse.2013.10.032, 2014. a
Platt, T. and Jassby, A.: The relationship between photosynthesis and light for natural assemblages of coastal marine phytoplankton, J. Phycol., 12, 421–430, https://doi.org/10.1111/j.1529-8817.1976.tb02866.x, 1976. a, b, c
Platt, T. and Sathyendranath, S.: Oceanic primary production: Estimation by remote sensing at local and regional scales, Science, 241, 1613–1620, https://doi.org/10.1126/science.241.4873.1613, 1988. a
Platt, T. and Sathyendranath, S.: Biological production models as elements of coupled, atmosphere-ocean models for climate research, J. Geophys. Res., 96, 2585–2592, https://doi.org/10.1029/90JC02305, 1991. a, b
Platt, T. and Sathyendranath, S.: Estimators of Primary Production for Interpretation of Remotely Sensed Data on Ocean Color, J. Geophys. Res., 98, 14561–14576, https://doi.org/10.1029/93JC01001, 1993. a
Platt, T. and Sathyendranath, S.: Spatial structure of pelagic ecosystem processes in the global ocean, Ecosystems, 2, 384–394, https://doi.org/10.1007/s100219900088, 1999. a
Platt, T., Sathyendranath, S., Caverhill, C. M., and Lewis, M. R.: Ocean primary production and available light: Further algorithms for remote sensing, Deep-Sea Res., 35, 855–879, https://doi.org/10.1016/0198-0149(88)90064-7, 1988. a, b
Platt, T., Caverhill, C., and Sathyendranath, S.: Basin-scale estimates of oceanic primary production by remote sensing: The North Atlantic, J. Geophys. Res., 96, 15147–15159, https://doi.org/10.1029/91JC01118, 1991. a
Platt, T., Sathyendranath, S., Forget, M., White, G. N., Caverhill, C., Bouman, H., Devred, E., and Son, S.: Operational estimation of primary production at large geographical scales, Remote Sens. Environ., 112, 3437–3448, https://doi.org/10.1016/j.rse.2007.11.018, 2008. a, b
Platt, T., Sathyendranath, S., White, G. N., Jackson, T., Saux Picart, S., and Bouman, H.: Primary Production: Sensitivity to Surface Irradiance and Implications for Archiving Data, Front. Mar. Sci., 4, 387, https://doi.org/10.3389/fmars.2017.00387, 2017. a, b, c, d
Prézelin, B. B., Putt, M., and Glover, H. E.: Diurnal patterns in photosynthetic capacity and depth-dependent photosynthesis–irradiance relationships in Synechococcus spp. and larger phytoplankton in three water masses in the Northwest Atlantic Ocean, Mar. Biol., 91, 205–217, https://doi.org/10.1007/BF00569436, 1986. a
Reynolds, C. S.: The Ecology of Phytoplankton, Cambridge University Press, 1st Edn., https://doi.org/10.1017/CBO9780511542145, 2006. a, b
Richardson, K., Bendtsen, J., Kragh, T., and Mousing, E. A.: Constraining the Distribution of Photosynthetic Parameters in the Global Ocean, Front. Mar. Sci., 3, 1–13, https://doi.org/10.3389/fmars.2016.00269, 2016. a
Ross, O. N., Geider, R. J., Berdalet, E., Artigas, M. L., and Piera, J.: Modelling the effect of vertical mixing on bottle incubations for determining in situ phytoplankton dynamics. I. Growth rates, Mar. Ecol. Prog. Ser., 435, 13–31, https://doi.org/10.3354/meps09193, 2011a. a
Ross, O. N., Geider, R. J., and Piera, J.: Modelling the effect of vertical mixing on bottle incubations for determining in situ phytoplankton dynamics. II. Primary production, Mar. Ecol. Prog. Ser., 435, 33–45, https://doi.org/10.3354/meps09194, 2011b. a, b
Sakshaug, E., Bricaud, A., Dandonneau, Y., Falkowski, P. G., Kiefer, D. A., Legendre, L., Morel, A., Parslow, J., and Takahashi, M.: Parameters of photosynthesis: definitions, theory and interpretation of results, J. Plankton Res., 19, 1637–1670, https://doi.org/10.1093/plankt/19.11.1637, 1997. a
Sathyendranath, S. and Platt, T.: Computation of aquatic primary production: Extended formalism to include the effect of angular and spectral distribution of light, Limnol. Oceanogr., 34, 188–198, https://doi.org/10.4319/lo.1989.34.1.0188, 1989a. a, b
Sathyendranath, S. and Platt, T.: Remote sensing of ocean chlorophyll: consequence of nonuniform pigment profile, Appl. Optics, 28, 490–495, https://doi.org/10.1364/AO.28.000490, 1989b. a
Sathyendranath, S. and Platt, T.: Spectral effects in bio-optical control on the ocean system, Oceanologia, 49, 5–39, 2007. a
Schofield, O., Prezelin, B. B., Smith, R. C., Stegmann, P. M., Nelson, N. B., Lewiss, M., and Baker, K. S.: Variability in spectral and nonspectral measurements of photosynthetic light utilization efficiencies, Mar. Ecol. Prog. Ser., 78, 253–271, https://doi.org/10.3354/meps078253, 1991. a
Strickland, J. D. H. and Parsons, T. R.: A practical handbook of seawater analysis, Fisheries Research Board of Canada Bulletin 167, 2nd Edn., https://publications.gc.ca/site/eng/9.581564/publication.html (last access: 6 November 2026), 1972. a
Topliss, B. J. and Platt, T.: Passive fluorescence and photosynthesis in the ocean: implications for remote sensing, Deep-Sea Res., 33, 849–864, https://doi.org/10.1016/0198-0149(86)90001-4, 1986. a
Westberry, T. K., Silsbe, G. M., and Behrenfeld, M. J.: Gross and net primary production in the global ocean: An ocean color remote sensing perspective, Earth-Sci. Rev., 104322, 1–15, https://doi.org/10.1016/j.earscirev.2023.104322, 2023. a, b
Wu, J., Lee, Z., Goes, J., do Rosario Gomes, H., and Wei, J.: Evaluation of three contrasting models in estimating primary production from ocean color remote sensing using long-term time-series data at oceanic and coastal sites, Remote Sens. Environ., 302, 113983, https://doi.org/10.1016/j.rse.2023.113983, 2024. a
Zheng, Q., Viljoen, J. J., Sun, X., Kovač, Ž., Sathyendranath, S., and Brewin, R. J. W.: Simulating vertical phytoplankton dynamics in a stratified ocean using a two-layered ecosystem model, Biogeosciences, 22, 3253–3278, https://doi.org/10.5194/bg-22-3253-2025, 2025. a
Zonneveld, C.: Modeling effects of photoadaptation on the photosynthesis-irradiance curve, J. Theor. Biol., 186, 381–388, https://doi.org/10.1006/jtbi.1997.0400, 1997. a
Zonneveld, C.: Light-limited microalgal growth: a comparison of modelling approaches, Ecol. Model., 113, 41–54, https://doi.org/10.1016/S0304-3800(98)00133-1, 1998. a
Editorial statement
The importance of this work lies in the fact that it enriches the global dataset with primary production data, specifically photosynthetic parameters, thereby doubling the global dataset on photosynthetic parameters, which may help resolve a bottleneck in the use of satellite-based primary production models in the future. The work thoroughly explains the different mathematical equations used to calculate the parameters through inverse modeling. The authors were able to retrieve and process more than 6,000 historical primary production profiles, transforming them into thousands of parameter pairs. The true potential of this work lies in providing useful information from the data to create new maps, use new satellite algorithms or improve existing ones, as well as construct time series spanning more than 30 years to assess climate change in phytoplankton, among other applications.
The importance of this work lies in the fact that it enriches the global dataset with primary...
Short summary
Marine life depends on photosynthesis, but models of ocean productivity require measurements that are scarce and costly. We developed a method to estimate how microscopic marine plants use light from existing measurements of production at different depths. Applying it to global ocean observations, we recovered 3776 sets of photosynthesis parameters and created a publicly available dataset. This resource can improve estimates of ocean productivity and its role in the global carbon cycle.
Marine life depends on photosynthesis, but models of ocean productivity require measurements...
Altmetrics
Final-revised paper
Preprint