Articles | Volume 22, issue 18
https://doi.org/10.5194/bg-22-4705-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-4705-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the challenges of retrieving phytoplankton properties from remote-sensing observations
Ocean Sciences Department, University of California, Santa Cruz, USA
Department of Astronomy & Astrophysics, University of California, Santa Cruz, USA
Kavli Institute for the Physics and Mathematics of the Universe, The University of Tokyo, Tokyo, Japan
Scripps Institution of Oceanography, University of California, San Diego, USA
Robert J. Frouin
Scripps Institution of Oceanography, University of California, San Diego, USA
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Cited articles
Behrenfeld, M. J., Boss, E., Siegel, D. A., and Shea, D. M.: Carbon-based ocean productivity and phytoplankton physiology from space, Global Biogeochem. Cy., 19, GB1006, https://doi.org/10.1029/2004GB002299, 2005. a
Behrenfeld, M. J., O'Malley, R. T., Boss, E. S., Westberry, T. K., Graff, J. R., Halsey, K. H., Milligan, A. J., Siegel, D. A., and Brown, M. B.: Revaluating ocean warming impacts on global phytoplankton, Nat. Clim. Change, 6, 323–330, https://doi.org/10.1038/nclimate2838, 2016. a
Bentler, P. M. and Bonett, D. G.: Significance tests and goodness of fit in the analysis of covariance structures, Psychol. Bull., 88, 588–606, https://doi.org/10.1037/0033-2909.88.3.588, 1980. a
Boss, E., Waite, A. M., Karstensen, J., Trull, T., Muller-Karger, F., Sosik, H. M., Uitz, J., Acin as, S. G., Fennel, K., Berman-Frank, I., Thomalla, S., Yamazaki, H., Batten, S., Gregori, G., Richardson, A. J., and Wanninkhof, R.: Recommendations for Plankton Measurements on OceanSITES Moorings With Re levance to Other Observing Sites, Front. Mar. Sci., 9, 929436, https://doi.org/10.3389/fmars.2022.929436, 2022. a
Bricaud, A., Babin, M., Morel, A., and Claustre, H.: Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization, J. Geophys. Res., 100, 13321–13332, https://doi.org/10.1029/95JC00463, 1995. a
Bricaud, A., Morel, A., Babin, M., Allali, K., and Claustre, H.: Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio-optical models, J. Geophys. Res., 103, 31033–31044, https://doi.org/10.1029/98JC02712, 1998. a, b
Cael, B. B., Bisson, K., Boss, E., and Erickson, Z. r. K.: How many independent quantities can be extracted from ocean color?, Limnol. Oceanogr. Lett., 8, 603–610, https://doi.org/10.1002/lol2.10319, 2023. a, b, c, d
Cetinić, I., McClain, C. R., and Werdell, P. J.: PACE Technical Report Series, Volume 6: Data Product Requirements and Error Budgets, NASA Technical Memorandum NASA/TM-2018 – 2018-219027/Vol. 6, NASA Goddard Space Flight Center, Greenbelt, MD, https://ntrs.nasa.gov/citations/20190001726 (last access: 15 January 2025), 2018. a
Cetinić, I., Rousseaux, C. S., Carroll, I. T., Chase, A. P., Kramer, S. J., Werdell, P. J., Siegel, D. A., Dierssen, H. M., Catlett, D., Neeley, A., Soto Ramos, I. M., nnifer L. Wolny, J., Sadoff, N., Urquhart, E., Westberry, T. K., Stramski, D., Pahlevan, N., Seegers, B. N., Sirk, E., Lange, P. K., Vandermeulen, R. A., Graff, J. R., Allen, J. G. ., Gaube, P., McKinna, L. I., McKibben, S. M., aren E. Binding, C., Calzado, V. S., and Sayers, M.: Phytoplankton composition from sPACE: Requirements, opportunities, and challenges, Remote Sens. Environ., 302, 113964, https://doi.org/10.1016/j.rse.2023.113964, 2024. a
Cloern, J. E. and Jassby, A. D.: Patterns and Scales of Phytoplankton Variability in Estuarine–Coastal Ecosystems, Estuar. Coast., 33, 230–241, https://doi.org/10.1007/s12237-009-9195-3, 2010. a
Defoin-Platel, M. and Chami, M.: How ambiguous is the inverse problem of ocean color in coastal waters?, J. Geophys. Res.-Oceans, 112, C03004, https://doi.org/10.1029/2006JC003847, 2007. a
Erickson, Z. K., Werdell, P. J., and Cetinić, I.: Bayesian retrieval of optically relevant properties from hyperspectral water-leaving reflectances, Appl. Optics, 59, 6902, https://doi.org/10.1364/AO.398043, 2020. a, b
Erickson, Z. K., McKinna, L., Werdell, P. J., and Cetinić, I.: Bayesian approach to a generalized inherent optical property model, Optics Express, 31, 22790, https://doi.org/10.1364/OE.486581, 2023. a, b
Flombaum, P., Wang, W.-L., Primeau, F. W., and Martiny, A. C.: Global picophytoplankton niche partitioning predicts overall positive response to ocean warming, Nat. Geosci., 13, 116–120, https://doi.org/10.1038/s41561-019-0524-2, 2020. a
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J.: emcee: The MCMC Hammer, Publ. Astron. Soc. Pac., 125, 306–312, https://doi.org/10.1086/670067, 2013. a
Fox, J., Kramer, S. J., Graff, J. R., Behrenfeld, M. c. J., Boss, E., Tilstone, G., and Halsey, K. H.: An absorption-based approach to improved estimates of phytoplankton bio mass and net primary production, Limnol. Oceanogr. Lett., 7, 419–426, https://doi.org/10.1002/lol2.10275, 2022. a
Frouin, R. and Pelletier, B.: Bayesian methodology for inverting satellite ocean-color data, Remote Sens. Environ., 159, 332–360, https://doi.org/10.1016/j.rse.2014.12.001, 2015. a
Frouin, R., Tan, J., Compiègne, M., Ramon, D. r., Sutton, M., Murakami, H., Antoine, D., Send, U. w., Sevadjian, J., and Vellucci, V.: The NASA EPIC/DSCOVR Ocean PAR Product, Frontiers in Remote Sensing, 3, 833340, https://doi.org/10.3389/frsen.2022.833340, 2022. a
Garver, S. A., Siegel, D. A., and B. Greg, M.: Variability in near-surface particulate absorption spectra: What can a satellite ocean color imager see?, Limnol. Oceanogr., 39, 1349–1367, https://doi.org/10.4319/lo.1994.39.6.1349, 1994. a
Gordon, H. R.: Simple Calculation of the Diffuse Reflectance of the Ocean, Appl. Optics, 12, 2803, https://doi.org/10.1364/AO.12.002803, 1973. a
Gordon, H. R.: Distribution Of Irradiance On The Sea Surface Resulting From A Point Source Imbedded In The Ocean, in: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 637 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 66–71, https://doi.org/10.1117/12.964216, 1986. a
Gordon, H. R. and Morel, A. Y.: Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery, vol. 4 of Lecture Notes on Coastal and Estuarine Studies, Springer-Verlag, ISBN 9780387909233, 9781118663707, https://doi.org/10.1029/LN004, 1983. a
Gray, P. C., Boss, E., Prochaska, J. X., Kerner, H., Begouen Demeaux, C., and Lehahn, Y.: The Promise and Pitfalls of Machine Learning in Ocean Remote Sensing, Oceanography, 37, 52–63, https://doi.org/10.5670/oceanog.2024.511, 2024. a
Hansen, J. E.: Multiple Scattering of Polarized Light in Planetary Atmospheres Part II. Sunlight Reflected by Terrestrial Water Clouds., J. Atmos. Sci., 28, 1400–1426, https://doi.org/10.1175/1520-0469(1971)028<1400:MSOPLI>2.0.CO;2, 1971. a
Hooker, S. B., Matsuoka, A., Kudela, R. M., Yamashita, Y., Suzuki, K., and Houskeeper, H. F.: A global end-member approach to derive aCDOM(440) from near-surface optical measurements, Biogeosciences, 17, 475–497, https://doi.org/10.5194/bg-17-475-2020, 2020. a
Houskeeper, H. F. and Hooker, S. B.: The primacy of dissolved organic matter to aquatic light variability, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-4163, 2025. a, b
Hovis, W. A., Clark, D. K., Anderson, F., Austin, R. W., Wilson, W. H., Baker, E. T., Ball, D., Gordon, H. R., Mueller, J. L., El-Sayed, S. Z., Sturm, B., Wrigley, R. C., and Yentsch, C. S.: Nimbus-7 Coastal Zone Color Scanner: System Description and Initial Imagery, Science, 210, 60–63, https://doi.org/10.1126/science.210.4465.60, 1980. a
IOCCG: Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex Waters, no. 3 in Reports of the International Ocean-Colour Coordinating Group, IOCCG, Dartmouth, Canada, ISBN 978-1-896246-54-3, 2000. a
Kramer, S. J., Siegel, D. A., Maritorena, S., and Catlett, D.: Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales, Remote Sens. Environ., 270, 112879, https://doi.org/10.1016/j.rse.2021.112879, 2022. a
Kudela, R. M., Hooker, S. B., Houskeeper, H. F., and McPherson, M.: The Influence of Signal to Noise Ratio of Legacy Airborne and Satellite Sensors for Simulating Next-Generation Coastal and Inland Water Products, Remote Sensing, 11, 2071, https://doi.org/10.3390/rs11182071, 2019. a
Lee, Z.: Remote Sensing of Inherent Optical Properties: Fundamentals, Tests of Algorithms, and Applications, IOCCG Report 5, International Ocean-Colour Coordinating Group (IOCCG), Dartmouth, Canada, an Affiliated Program of the Scientific Committee on Oceanic Research (SCOR) and An Associate Member of the Committee on Earth Observation Satellites (CEOS), ISBN 978-1-896246-56-7, 2006. a, b, c, d
Lee, Z., Carder, K. L., and Arnone, R. A.: Deriving inherent optical properties from water color: a multiband qua si-analytical algorithm for optically deep waters, Appl. Optics, 41, 5755–5772, https://doi.org/10.1364/AO.41.005755, 2002. a, b, c
Lee, Z., Carder, K. L., and Arnone, R. A.: Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters, Appl. Optics, 41, 5755–5772, https://doi.org/10.1364/AO.41.005755, 2002. a, b
Loisel, H., Stramski, D., Dessailly, D., Jamet, C., Li, L., and Reynolds, R. A.: An Inverse Model for Estimating the Optical Absorption and Backscattering Coefficients of Seawater From Remote-Sensing Reflectance Over a Broad Range of Oceanic and Coastal Marine Environments, J. Geophys. Res.-Oceans, 123, 2141–2171, https://doi.org/10.1002/2017JC013632, 2018. a
Loisel, H., Jorge, D. S. F., Reynolds, R. A., and Stramski, D.: A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean, Earth Syst. Sci. Data, 15, 3711–3731, https://doi.org/10.5194/essd-15-3711-2023, 2023. a, b, c
Maritorena, S. and Siegel, D. A.: Consistent merging of satellite ocean color data sets using a bio-optical model, Remote Sens. Environ., 94, 429–440, https://doi.org/10.1016/j.rse.2004.08.014, 2005. a
Maritorena, S., Siegel, D. A., and Peterson, A. R.: Optimization of a semianalytical ocean color model for global-scale applications, Appl. Optics, 41, 2705–2714, https://doi.org/10.1364/AO.41.002705, 2002. a
Mason, J. D., Cone, M. T., and Fry, E. S.: Ultraviolet (250–550 nm) absorption spectrum of pu re water, Appl. Optics, 55, 7163–7172, https://doi.org/10.1364/AO.55.007163, 2016. a
Mobley, C. D. E.: The Oceanic Optics Book, International Ocean Colour Coordinating Group (IOCCG), Dartmouth, NS, Canada, https://www.oceanopticsbook.info/ (last access: 1 July 2025), 2022. a
Mouw, C. B., Hardman-Mountford, N. J., Alvain, S., Bracher, A., Brewin, R. J. W., Bricaud, A., Ciotti, A. M., Devred, E., Fujiwara, A., Hirata, Takafumi an d Hirawake, T., Kostadinov, T. S., Roy, S., and Uitz, J. I.: A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean, Front. Mar. Sci., 4, 41, https://doi.org/10.3389/fmars.2017.00041, 2017. a, b
NASA Goddard Space Flight Center: SeaBASS Validation Search, https://seabass.gsfc.nasa.gov/search/?search_type=Perform Validation Search&val_sata=1&val_products=11&val_source=0 (last access: June 2024), 2024. a
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kahru, M., and McClain, C.: Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res.-Oceans, 103, 24937–24953, https://doi.org/10.1029/98JC02160, 1998. a
Prochaska, J. X.: BING: Bayesian INferences with Gordon coefficients, Zenodo [code], https://doi.org/10.5281/zenodo.13292700, 2024. a
Prochaska, J. X.: ocean-colour/bing: Final version for EGU Sphere (v0.3), Zenodo [code], https://doi.org/10.5281/zenodo.15857015, 2025. a
Prochaska, J. X. and Gray, P.: On the Fundamental Additive Modes of Ocean Color Absorption, Limnol. Oceanogr, 70, 2267–2283, https://doi.org/10.22541/essoar.171828481.15444713/v1, 2024. a
Prochaska, J. X. and Gray, P.: ocean-colour/ocpy: First DOI; coincides with publication of the BING paper (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.17088615, 2025. a
Roesler, C. S., Perry, M. J., and Carder, K. L.: Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters, Limnol. Oceanogr., 34, 1510–1523, https://doi.org/10.4319/lo.1989.34.8.1510, 1989. a, b, c
Seegers, B. N., Stumpf, R. P., Schaeffer, B. A., Loftin, K. A., and Werdell, P. J.: Performance metrics for the assessment of satellite data products: an ocean color case study, Opt. Express, 26, 7404, https://doi.org/10.1364/OE.26.007404, 2018. a, b
Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A., and Lorenzi-Kayser, M.: Global distribution and dynamics of colored dissolved and detrital organic materials, J. Geophys. Res.-Oceans, 107, 21-1–21-14, https://doi.org/10.1029/2001JC000965, 2002. a
Siegel, D. A., Behrenfeld, M. J., Maritorena, S., McClain, C. R., Antoine, D., Bailey, S. W., Bontempi, P. S., Boss, E. S., Dierssen, H. M., Doney, S. C., Eplee, R. E., J., Evans, R. H., Feldman, G. C., Fields, E., Franz, B. A., Kuring, N. A., Mengelt, C., Nelson, N. B., Patt, F. S., Robinson, W. D., Sarmiento, J. L., Swan, C. M., Werdell, P. J., Westberry, T. K., Wilding, J. G., and Yoder, J. A.: Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission, Remote Sens. Environ., 135, 77–91, https://doi.org/10.1016/j.rse.2013.03.025, 2013. a
Siegel, D. A., Buesseler, K. O., Doney, S. C., Sailley, S. F., Behrenfeld, M. J., and Boyd, P. W.: Global assessment of ocean carbon export by combining satellite observations and food-web models, Global Biogeochem. Cy., 28, 181–196, https://doi.org/10.1002/2013GB004743, 2014. a
Stramski, D., Bricaud, A., and Morel, A.: Modeling the Inherent Optical Properties of the Ocean Based on the Detailed Composition of the Planktonic Community, Appl. Optics, 40, 2929–2945, https://doi.org/10.1364/AO.40.002929, 2001. a
Stramski, D., Joshi, I., and Reynolds, R. A.: Ocean color algorithms to estimate the concentration of particulate organic carbon in surface waters of the global ocean in support of a long-term data record from multiple satellite missions, Remote Sens. Environ., 269, 112776, https://doi.org/10.1016/j.rse.2021.112776, 2022. a
Sydor, M., Gould, R. W., Arnone, R. A., Haltrin, V. I., and Goode, W.: Uniqueness in Remote Sensing of the Inherent Optical Properties of Ocean Water, Appl. Optics, 43, 2156–2162, https://doi.org/10.1364/AO.43.002156, 2004. a, b
Twardowski, M. S., Jamet, C., and Loisel, H.: Analytical model to derive suspended particulate matter concentration in natural waters by inversion of optical attenuation and backscattering, in: Ocean Sensing and Monitoring X, edited by Hou, W. W. and Arnone, R. A., vol. 10631 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, p. 106310H, https://doi.org/10.1117/12.2309995, 2018. a
Werdell, P. J. and Bailey, S. W.: The SeaWiFS Bio-optical Archive and Storage System (SeaBASS): Current architecture and implementation, NASA Tech. Memo. 2002-211617, NASA Goddard Space Flight Center, Greenbelt, Maryland, https://ntrs.nasa.gov/citations/20020091607 (last access: 15 January 2025), 2002. a, b, c
Werdell, P. J., Franz, B. A., Bailey, S. W., Feldman, G. C., Boss, E., Brando, V. E., Dowell, M., Hirata, T., Lavender, S. J., Lee, Z., Loisel, H., Maritorena, S., Mélin, F., Moore, T. S., Smyth, T. J., Antoine, D., Devred, E., d'Andon, O. H. F., and Mangin, A.: Generalized ocean color inversion model for retrieving marine inherent optical properties, Appl. Optics, 52, 2019, https://doi.org/10.1364/AO.52.002019, 2013. a
Werdell, P. J., McKinna, L. I. W., Boss, E., Ackleson, S. G., Craig, S. E., Gregg, W. W., Lee, Z., Maritorena, S., Roesler, C. S., Rousseaux, C. S., Stramski, D., Sullivan, J. M., Twardowski, M. S., Tzortziou, M., and Zhang, X.: An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing, Prog. Oceanogr., 160, 186–212, https://doi.org/10.1016/j.pocean.2018.01.001, 2018. a, b, c
Wolanin, A., Rozanov, V. V., Dinter, T., Noël, S., Vountas, M., Burrows, J. P., and Bracher, A.: Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: Feasibility study and first results, Remote Sens. Environ., 166, 243–261, https://doi.org/10.1016/j.rse.2015.05.018, 2015. a
Woo Kim, Y., Kim, T., Shin, J., Lee, D.-S., Park, Y.-S., Kim, Y., and Cha, Y.: Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters, Ecol. Indic., 137, 108737, https://doi.org/10.1016/j.ecolind.2022.108737, 2022. a
Zege, E. P., Ivanov, A. P., and Katsev, I. L.: Image Transfer Through a Scattering Medium, Springer-Verlag, ISBN 0-387-51978-5, 1991. a
Zhang, M., Ibrahim, A., Franz, B. A., and a nd Andrew M. Sayer, Z. A.: Estimating pixel-level uncertainty in ocean color retrievals from MODI S, Opt. Express, 30, 31415–31438, https://doi.org/10.1364/OE.460735, 2022. a, b, c, d
Short summary
Satellites monitor ocean health globally, but we discovered a fundamental physics limitation when measuring phytoplankton – tiny plants essential to marine ecosystems. Our analysis shows that even advanced satellites cannot reliably distinguish phytoplankton from other ocean components. This challenges decades of research and suggests that existing measurements have greater uncertainties than realized. Combining satellite data with direct ocean sampling is needed for better monitoring of these vital organisms.
Satellites monitor ocean health globally, but we discovered a fundamental physics limitation...
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