Concentrations of Multiple Phytoplankton Pigments in the Global Oceans Obtained from Satellite Ocean Color Measurements with MERIS
Source abstract
Abstract
The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (Rrs(λ)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from Rrs(λ). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll a, b, c ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS Rrs(λ) based on a closure between the input and output Rrs(λ) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a].
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Marine and coastal ecosystems · Marine Biology and Ecology Research · Microbial Community Ecology and Physiology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA
Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA
References (87)
Show source-provided references
1. The role of phytoplankton photosynthesis in global biogeochemical cycles10.1007/BF00014586 ↗
2. Turbulence, phytoplankton cell size, and the structure of pelagic food webs10.1016/S0065-2881(08)60129-7 ↗
3. Phytoplankton pigment concentrations in the Middle Atlantic Bight: Comparison of ship determinations and CZCS estimates10.1364/AO.22.000020 ↗
4. McClain, Ocean color chlorophyll algorithms for SeaWiFS10.1029/98JC02160 ↗
5. Hu, C., Lee, Z., and Franz, B. (2012). Chlorophyll algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. J. Geophys. Res., 117.10.1029/2011JC007395 ↗
6. Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery10.1016/j.dsr.2005.06.015 ↗
7. Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data10.5194/bg-6-751-2009 ↗
8. Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil10.4319/lom.2006.4.237 ↗
9. A three-component model of phytoplankton size class for the Atlantic Ocean10.1016/j.ecolmodel.2010.02.014 ↗
10. Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types10.5194/bg-8-311-2011 ↗
11. Mouw, C.B., and Yoder, J.A. (2010). Optical determination of phytoplankton size composition from global SeaWiFS imagery. J. Geophys. Res. Oceans, 115.10.1029/2010JC006337 ↗
12. Remote sensing of phytoplankton pigments: A comparison of empirical and theoretical approaches10.1080/014311601449925 ↗
13. Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study10.1016/j.rse.2007.01.016 ↗
14. Photosynthetic rates derived from satellite-based chlorophyll concentration10.4319/lo.1997.42.1.0001 ↗
15. Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data10.1016/S1568-9883(02)00083-5 ↗
16. Comparison of chlorophyll a and carotenoid pigments as predictors of phytoplankton biomass10.1007/BF00397117 ↗
17. Nutrient limitation in relation to phytoplankton carotenoid/chlorophyll a ratios in freshwater mesocosms10.1093/plankt/19.7.891 ↗
18. Annual variations of phytoplankton biomass in the Eastern English Channel: Comparison by pigment signatures and microscopic counts10.1093/plankt/22.8.1423 ↗
19. Chlorophyll content and fluorescence responses cannot be used to gauge reliably phytoplankton biomass, nutrient status or growth rate10.1111/j.1469-8137.2005.01601.x ↗
20. Revaluating ocean warming impacts on global phytoplankton10.1038/nclimate2838 ↗
21. Derivative analysis of spectral absorption by photosynthetic pigments in the western Sargasso Sea10.1357/002224089785076325 ↗
22. Jeffrey, S.W., and Vesk, M. (1997). Introduction to marine phytoplankton and their pigment signature. Phytoplankton Pigments in Oceanography, UNESCO Publishing.
23. Optical discrimination of a phytoplankton species in natural mixed populations10.4319/lo.2000.45.2.0467 ↗
24. Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments10.1016/S0378-4347(00)00603-4 ↗
25. Roy, S., Llewellyn, C.A., Egeland, E.S., and Johnsen, G. (2011). Phytoplankton Pigments: Characterization, Chemotaxonomy and Applications in Oceanography, Cambridge University Press.10.1017/CBO9780511732263 ↗
26. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water10.4319/lo.2005.50.1.0237 ↗
27. Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes10.1080/01431160802007640 ↗
28. CHEMTAX—A program for estimating class abundances from chemical markers: Application to HPLC measurements of phytoplankton10.3354/meps144265 ↗
29. Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter10.1029/1999JC000308 ↗
30. Uitz, J., Claustre, H., Morel, A., and Hooker, S.B. (2006). Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll. J. Geophys. Res. Oceans, 111.10.1029/2005JC003207 ↗
31. Sathyendranath, S., Aiken, J., Alvain, S., Barlow, R., Bouman, H., Bracher, A., and Clementson, L.A. (2014). Phytoplankton functional types from Space. Reports of the International Ocean-Colour Coordinating Group (IOCCG), International Ocean-Colour Coordinating Group.
32. Assessing phytoplankton community composition from hyperspectral measurements of phytoplankton absorption coefficient and remote-sensing reflectance in open-ocean environments10.1016/j.rse.2015.09.027 ↗
33. Phytoplankton pigment communities can be modeled using unique relationships with spectral absorption signatures in a dynamic coastal environment10.1002/2017JC013195 ↗
34. Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development10.3389/fmars.2017.00055 ↗
35. Retrieval of pigment concentrations and size structure of algal populations from their absorption spectra using multilayered perceptrons10.1364/AO.46.001251 ↗
36. Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient10.4319/lo.2002.47.2.0404 ↗
37. A three component classification of phytoplankton absorption spectra: Application to ocean-color data10.1016/j.rse.2011.04.025 ↗
38. Determination of the major groups of phytoplankton pigments from the absorption spectra of total particulate matter10.1029/93JC01273 ↗
39. An absorption model to determine phytoplankton size classes from satellite ocean colour10.1016/j.rse.2008.03.011 ↗
40. Phytoplankton spectral absorption as influenced by community size structure and pigment composition10.1093/plankt/25.1.35 ↗
41. Moisan, J.R., Moisan, T.A., and Linkswiler, M.A. (2011). An inverse modeling approach to estimating phytoplankton pigment concentrations from phytoplankton absorption spectra. J. Geophys. Res. Oceans, 116.10.1029/2010JC006786 ↗
42. Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site)10.1364/AO.52.002257 ↗
43. A consumer’s guide to satellite remote sensing of multiple phytoplankton groups in the global ocean10.3389/fmars.2017.00041 ↗
44. Studies on the phytoplankton ecology of the trondheemsfjord. II. Chloroplast pigments in relation to abundance and physiological state of the phytoplankton10.1016/0022-0981(73)90052-X ↗
45. Interpretation of fast repetition rate (FRR) fluorescence: Signatures of phytoplankton community structure versus physiological state10.3354/meps07830 ↗
46. Remote sensing of phytoplankton pigment distribution in the United States northeast coast10.1016/j.rse.2010.05.015 ↗
47. Satellite observations of phytoplankton functional type spatial distributions, phenology, diversity, and ecotones10.3389/fmars.2017.00189 ↗
48. A semianalytic radiance model of ocean color10.1029/JD093iD09p10909 ↗
49. Hyperspectral remote sensing for shallow waters. I. A semianalytical model10.1364/AO.37.006329 ↗
50. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters10.1364/AO.41.005755 ↗
51. Lee, Z. (2006). Remote Sensing of Inherent Optical Properties: Fundamentals, Tests of Algorithms, and Applications, International Ocean-Colour Coordinating Group.
52. Optimization of a semianalytical ocean color model for global-scale applications10.1364/AO.41.002705 ↗
53. Generalized ocean color inversion model for retrieving marine inherent optical properties10.1364/AO.52.002019 ↗
54. Werdell, P.J., McKinna, L.I., Boss, E., Ackleson, S.G., Craig, S.E., Gregg, W.W., and Stramski, D. (2018). An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Prog. Oceanogr.10.1016/j.pocean.2018.01.001 ↗
55. Adaptive semianalytical inversion of ocean color radiometry in optically complex waters10.1364/AO.51.002808 ↗
56. Regional ocean-colour chlorophyll algorithms for the Red Sea10.1016/j.rse.2015.04.024 ↗
57. Bukata, R.P., Jerome, J.H., Kondratyev, A.S., and Pozdnyakov, D.V. (2018). Optical Properties and Remote Sensing of Inland and Coastal Waters, CRC Press.10.1201/9780203744956 ↗
58. Devred, E., Sathyendranath, S., Stuart, V., Maass, H., Ulloa, O., and Platt, T. (2006). A two-component model of phytoplankton absorption in the open ocean: Theory and applications. J. Geophys. Res. Oceans, 111.10.1029/2005JC002880 ↗
59. Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea10.1029/96JC03243 ↗
60. Satellite retrieval of inherent optical properties by linear matrix inversion of oceanic radiance models: An analysis of model and radiance measurement errors10.1029/96JC01414 ↗
61. In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance10.1029/95JC00455 ↗
62. Uncertainties of inherent optical properties obtained from semianalytical inversions of ocean color10.1364/AO.44.004074 ↗
63. 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 Environments10.1002/2017JC013632 ↗
64. Retrieving absorption coefficients of multiple phytoplankton pigments from hyperspectral remote sensing reflectance measured over cyanobacteria bloom waters10.1002/lom3.10102 ↗
65. Effect of pigment composition on absorption properties of phytoplankton10.3354/meps073011 ↗
66. Chase, A.P., Boss, E., Cetinić, I., and Slade, W. (2017). Estimation of phytoplankton accessory pigments from hyperspectral reflectance spectra: Toward a global algorithm. J. Geophys. Res. Oceans.10.1002/2017JC012859 ↗
67. Lutz, V.A., Sathyendranath, S., and Head, E.J.H. (1996). Absorption coefficient of phytoplankton: Regional variations in the North Atlantic. Mar. Ecol. Prog. Ser., 197–213.10.3354/meps135197 ↗
68. Algorithms for determining the absorption coefficient for aquatic particulates using the quantitative filter technique10.1117/12.21440 ↗
69. Mobley, C.D. (1994). Light and Water: Radiative Transfer in Natural Waters, Academic Press.
70. Robust approach to directly measuring water-leaving radiance in the field10.1364/AO.52.001693 ↗
71. Mueller, J.L. (2003). Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4: Radiometric Measurements and Data Analysis Protocols, Goddard Space Flight Center.
72. A multi-sensor approach for the on-orbit validation of ocean color satellite data products10.1016/j.rse.2006.01.015 ↗
73. Mueller, J.L., Fargion, G.S., and McClain, C.R. (2003). Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples, Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume IV: Inherent Optical Properties: Instruments, Characterizations, Field Measurements and Data Analysis Protocols.
74. Wang, G., Lee, Z., and Mouw, C. (2017). Multi-spectral remote sensing of phytoplankton pigment absorption properties in cyanobacteria bloom waters: A regional example in the western basin of Lake Erie. Remote Sens., 9.10.3390/rs9121309 ↗
75. Optical properties of pure water and pure sea water
76. Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements10.1364/AO.36.008710 ↗
77. Hyperspectral absorption coefficient of “pure” seawater in the range of 350–550 nm inverted from remote sensing reflectance10.1364/AO.54.000546 ↗
78. Seasonal dynamics of colored dissolved material in the Sargasso Sea10.1016/S0967-0637(97)00106-4 ↗
79. Babin, M., Stramski, D., Ferrari, G.M., Claustre, H., Bricaud, A., Obolensky, G., and Hoepffner, N. (2003). Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. J. Geophys. Res. Oceans, 108.10.1029/2001JC000882 ↗
80. Design and testing of a generalized reduced gradient code for nonlinear programming10.1145/355769.355773 ↗
81. Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands10.1364/OE.18.024109 ↗
82. Ruddick, K., Park, Y., Astoreca, R., Neukermans, G., and Van Mol, B. (2008). Validation of MERIS water products in the Southern North Sea. Proceedings of the 2nd MERIS—(A) ATSR Workshop, ESA Publications Office Frascati.
83. The trophic status of various oceanic provinces as revealed by phytoplankton pigment signatures10.4319/lo.1994.39.5.1206 ↗
84. Phytoplankton pigments and functional types in the Atlantic Ocean: A decadal assessment, 1995–200510.1016/j.dsr2.2008.09.017 ↗
85. Variability of phytoplankton pigment ratios across aquatic environments10.1080/09670260802618942 ↗
86. Climate-driven trends in contemporary ocean productivity10.1038/nature05317 ↗
87. Bricaud, A., Claustre, H., Ras, J., and Oubelkheir, K. (2004). Natural variability of phytoplanktonic absorption in oceanic waters: Influence of the size structure of algal populations. J. Geophys. Res. Oceans, 109.10.1029/2004JC002419 ↗