Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

The differences among phytoplankton carbon (Cphy) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The mat...

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Main Authors: Víctor Martínez-Vicente, Hayley Evers-King, Shovonlal Roy, Tihomir S. Kostadinov, Glen A. Tarran, Jason R. Graff, Robert J. W. Brewin, Giorgio Dall'Olmo, Tom Jackson, Anna E. Hickman, Rüdiger Röttgers, Hajo Krasemann, Emilio Marañón, Trevor Platt, Shubha Sathyendranath
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-12-01
Series:Frontiers in Marine Science
Subjects:
Online Access:http://journal.frontiersin.org/article/10.3389/fmars.2017.00378/full
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spelling doaj-b10a52f04226457fa4b39a65f00652a32020-11-24T23:15:13ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452017-12-01410.3389/fmars.2017.00378265876Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the OceanVíctor Martínez-Vicente0Hayley Evers-King1Shovonlal Roy2Tihomir S. Kostadinov3Tihomir S. Kostadinov4Glen A. Tarran5Jason R. Graff6Robert J. W. Brewin7Robert J. W. Brewin8Giorgio Dall'Olmo9Giorgio Dall'Olmo10Tom Jackson11Anna E. Hickman12Rüdiger Röttgers13Hajo Krasemann14Emilio Marañón15Trevor Platt16Shubha Sathyendranath17Shubha Sathyendranath18Plymouth Marine Laboratory, Plymouth, United KingdomPlymouth Marine Laboratory, Plymouth, United KingdomDepartment of Geography and Environmental Sciences, School of Agriculture, Policy and Development, University of Reading, Reading, United KingdomDepartment of Geography and the Environment, University of Richmond, Richmond, VA, United StatesDivision of Hydrologic Sciences, Desert Research Institute, Reno, NV, United StatesPlymouth Marine Laboratory, Plymouth, United KingdomDepartment of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United StatesPlymouth Marine Laboratory, Plymouth, United KingdomPlymouth Marine Laboratory, National Centre for Earth Observation, Plymouth, United KingdomPlymouth Marine Laboratory, Plymouth, United KingdomPlymouth Marine Laboratory, National Centre for Earth Observation, Plymouth, United KingdomPlymouth Marine Laboratory, Plymouth, United KingdomOcean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, United KingdomHelmholtz-Zentrum Geesthacht, Center for Materials and Coastal Research, Geesthacht, GermanyHelmholtz-Zentrum Geesthacht, Center for Materials and Coastal Research, Geesthacht, GermanyDepartamento de Ecología y Biología Animal, Universidade de Vigo, Vigo, SpainPlymouth Marine Laboratory, Plymouth, United KingdomPlymouth Marine Laboratory, Plymouth, United KingdomPlymouth Marine Laboratory, National Centre for Earth Observation, Plymouth, United KingdomThe differences among phytoplankton carbon (Cphy) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in Cphy. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m−3), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m−3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (bbp) retrieval.http://journal.frontiersin.org/article/10.3389/fmars.2017.00378/fullphytoplankton carboncarbon-to-chlorophyllocean color remote sensingpicophytoplanktonflow cytometryoptical water class
collection DOAJ
language English
format Article
sources DOAJ
author Víctor Martínez-Vicente
Hayley Evers-King
Shovonlal Roy
Tihomir S. Kostadinov
Tihomir S. Kostadinov
Glen A. Tarran
Jason R. Graff
Robert J. W. Brewin
Robert J. W. Brewin
Giorgio Dall'Olmo
Giorgio Dall'Olmo
Tom Jackson
Anna E. Hickman
Rüdiger Röttgers
Hajo Krasemann
Emilio Marañón
Trevor Platt
Shubha Sathyendranath
Shubha Sathyendranath
spellingShingle Víctor Martínez-Vicente
Hayley Evers-King
Shovonlal Roy
Tihomir S. Kostadinov
Tihomir S. Kostadinov
Glen A. Tarran
Jason R. Graff
Robert J. W. Brewin
Robert J. W. Brewin
Giorgio Dall'Olmo
Giorgio Dall'Olmo
Tom Jackson
Anna E. Hickman
Rüdiger Röttgers
Hajo Krasemann
Emilio Marañón
Trevor Platt
Shubha Sathyendranath
Shubha Sathyendranath
Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean
Frontiers in Marine Science
phytoplankton carbon
carbon-to-chlorophyll
ocean color remote sensing
picophytoplankton
flow cytometry
optical water class
author_facet Víctor Martínez-Vicente
Hayley Evers-King
Shovonlal Roy
Tihomir S. Kostadinov
Tihomir S. Kostadinov
Glen A. Tarran
Jason R. Graff
Robert J. W. Brewin
Robert J. W. Brewin
Giorgio Dall'Olmo
Giorgio Dall'Olmo
Tom Jackson
Anna E. Hickman
Rüdiger Röttgers
Hajo Krasemann
Emilio Marañón
Trevor Platt
Shubha Sathyendranath
Shubha Sathyendranath
author_sort Víctor Martínez-Vicente
title Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean
title_short Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean
title_full Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean
title_fullStr Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean
title_full_unstemmed Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean
title_sort intercomparison of ocean color algorithms for picophytoplankton carbon in the ocean
publisher Frontiers Media S.A.
series Frontiers in Marine Science
issn 2296-7745
publishDate 2017-12-01
description The differences among phytoplankton carbon (Cphy) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in Cphy. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m−3), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m−3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (bbp) retrieval.
topic phytoplankton carbon
carbon-to-chlorophyll
ocean color remote sensing
picophytoplankton
flow cytometry
optical water class
url http://journal.frontiersin.org/article/10.3389/fmars.2017.00378/full
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