TY - JOUR
T1 - An ocean-colour time series for use in climate studies
T2 - The experience of the ocean-colour climate change initiative (OC-CCI)
AU - Sathyendranath, Shubha
AU - Brewin, Robert J.W.
AU - Brockmann, Carsten
AU - Brotas, Vanda
AU - Calton, Ben
AU - Chuprin, Andrei
AU - Cipollini, Paolo
AU - Couto, André B.
AU - Dingle, James
AU - Doerffer, Roland
AU - Donlon, Craig
AU - Dowell, Mark
AU - Farman, Alex
AU - Grant, Mike
AU - Groom, Steve
AU - Horseman, Andrew
AU - Jackson, Thomas
AU - Krasemann, Hajo
AU - Lavender, Samantha
AU - Martinez-Vicente, Victor
AU - Mazeran, Constant
AU - Mélin, Frédéric
AU - Moore, Timothy S.
AU - Müller, Dagmar
AU - Regner, Peter
AU - Roy, Shovonlal
AU - Steele, Chris J.
AU - Steinmetz, François
AU - Swinton, John
AU - Taberner, Malcom
AU - Thompson, Adam
AU - Valente, André
AU - Zühlke, Marco
AU - Brando, Vittorio E.
AU - Feng, Hui
AU - Feldman, Gene
AU - Franz, Bryan A.
AU - Frouin, Robert
AU - Gould, Richard W.
AU - Hooker, Stanford B.
AU - Kahru, Mati
AU - Kratzer, Susanne
AU - Mitchell, B. Greg
AU - Muller-Karger, Frank E.
AU - Sosik, Heidi M.
AU - Voss, Kenneth J.
AU - Werdell, Jeremy
AU - Platt, Trevor
N1 - Funding Information:
Funding: This work was funded by the Ocean Colour Climate Change initiative of the European Space Agency (Grant Number 4000101437/10/I-LG). We acknowledge additional funding support by NERC through the National Centre for Earth Observation (Grant Number PR140015). Additional funding from a Simons Foundation Grant (549947, SS) is also gratefully acknowledged. V.B. also acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme grant agreement N_ 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation – PORTWIMS.
Funding Information:
This work was funded by the Ocean Colour Climate Change initiative of the European Space Agency (Grant Number 4000101437/10/I-LG). We acknowledge additional funding support by NERC through the National Centre for Earth Observation (Grant Number PR140015). Additional funding from a Simons Foundation Grant (549947, SS) is also gratefully acknowledged. V.B. also acknowledges funding from the European Union?s Horizon 2020 Research and Innovation Programme grant agreement N_ 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation ? PORTWIMS. The work would not have been possible without the free and open access to NASA ocean-colour data, and the help from the NASA OBPG at many stages of the work. We sincerely thank all contributors to the in situ database used in this work. We also thank two reviewers for all their helpful comments on an earlier version of this manuscript.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
AB - Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
KW - Chlorophyll-a
KW - Climate Change Initiative
KW - Essential Climate Variable
KW - Inherent optical properties
KW - Ocean colour
KW - Optical water classes
KW - Phytoplankton
KW - Remote-sensing reflectance
KW - Uncertainty characterisation
KW - Water-leaving radiance
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U2 - 10.3390/s19194285
DO - 10.3390/s19194285
M3 - Article
C2 - 31623312
AN - SCOPUS:85073539022
VL - 19
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 19
M1 - 4285
ER -