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Sathyendranath S, Brewin RJW, Brockmann C, Brotas V, Calton B, Chuprin A, Cipollini P, Couto AB, Dingle J, Doerffer R, Donlon C, Dowell M, Farman A, Grant M, Groom S, Horseman A, Jackson T, Krasemann H, Lavender S, Martinez-Vicente V, Mazeran C, Mélin F, Moore TS, Müller D, Regner P, Roy S, Steele CJ, Steinmetz F, Swinton J, Taberner M, Thompson A, Valente A, Zühlke M, Brando VE, Feng H, Feldman G, Franz BA, Frouin R, Gould RW, Hooker SB, Kahru M, Kratzer S, Mitchell BG, Muller-Karger FE, Sosik HM, Voss KJ, Werdell J, Platt T. An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors (Basel) 2019; 19:E4285. [PMID: 31623312 PMCID: PMC6806290 DOI: 10.3390/s19194285] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 09/15/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Shubha Sathyendranath
- National Centre for Earth Observation, Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Robert J W Brewin
- National Centre for Earth Observation, Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Carsten Brockmann
- Brockmann Consult, Max-Planck-Straße 2, D-21502 Geesthacht, Germany.
| | - Vanda Brotas
- Marine Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal.
| | - Ben Calton
- PML Applications Ltd, Prospect Place, Plymouth PL1 3DH, UK.
| | - Andrei Chuprin
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Paolo Cipollini
- Telespazio Vega UK for ESA Climate Office, European Space Agency/ECSAT, Harwell Campus OX11 0FD, UK.
| | - André B Couto
- Marine Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal.
| | - James Dingle
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Roland Doerffer
- Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH, Max-Planck-Straße 1, D-21502 Geesthacht, Germany.
| | - Craig Donlon
- European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands.
| | - Mark Dowell
- European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, I-21027 Ispra, Italy.
| | - Alex Farman
- Telespazio VEGA UK Ltd., 350 Capability Green, Luton, Bedfordshire LU1 3LU, UK.
| | - Mike Grant
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Steve Groom
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Andrew Horseman
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Thomas Jackson
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Hajo Krasemann
- Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH, Max-Planck-Straße 1, D-21502 Geesthacht, Germany.
| | - Samantha Lavender
- Telespazio VEGA UK Ltd., 350 Capability Green, Luton, Bedfordshire LU1 3LU, UK.
| | | | | | - Frédéric Mélin
- European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, I-21027 Ispra, Italy.
| | - Timothy S Moore
- Ocean Process Analysis Laboratory, Morse Hall, University of New Hampshire, Durham, NH 03824, USA.
| | - Dagmar Müller
- Brockmann Consult, Max-Planck-Straße 2, D-21502 Geesthacht, Germany.
- Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH, Max-Planck-Straße 1, D-21502 Geesthacht, Germany.
| | - Peter Regner
- European Space Agency, ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (Roma), Italy.
| | - Shovonlal Roy
- Department of Geography and Environmental Sciences, University of Reading, Whiteknights, Reading RG6 6DW, UK.
| | - Chris J Steele
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | | | - John Swinton
- Telespazio VEGA UK Ltd., 350 Capability Green, Luton, Bedfordshire LU1 3LU, UK.
| | - Malcolm Taberner
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - Adam Thompson
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
| | - André Valente
- Marine Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal.
| | - Marco Zühlke
- Brockmann Consult, Max-Planck-Straße 2, D-21502 Geesthacht, Germany.
| | | | - Hui Feng
- Ocean Process Analysis Laboratory, Morse Hall, University of New Hampshire, Durham, NH 03824, USA.
| | - Gene Feldman
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
| | - Bryan A Franz
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
| | - Robert Frouin
- Scripps Institution of Oceanography Mail Code 0218, University of California San Diego, La Jolla, CA 92039-0218, USA.
| | - Richard W Gould
- Naval Research Laboratory, Bldg. 1009, Code 7331, Stennis Space Center, MS 39529, USA.
| | | | - Mati Kahru
- Scripps Institution of Oceanography Mail Code 0218, University of California San Diego, La Jolla, CA 92039-0218, USA.
| | - Susanne Kratzer
- Department of Ecology, Environment and Plant Sciences, University of Stockholm, 106 91 Stockholm, Sweden.
| | - B Greg Mitchell
- Scripps Institution of Oceanography Mail Code 0218, University of California San Diego, La Jolla, CA 92039-0218, USA.
| | - Frank E Muller-Karger
- Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South St, Petersburg, FL 33701, USA.
| | - Heidi M Sosik
- Biology Department, MS 32, Woods Hole Oceanographic Institution, Woods Hole, MA 02543-1049, USA.
| | - Kenneth J Voss
- Department of Physics, University of Miami, James L. Knight Physics Building, 1320 Campo Sano Dr., Coral Gables, FL 33124, USA.
| | - Jeremy Werdell
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
| | - Trevor Platt
- Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK.
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Carvalho GA, Minnett PJ, Banzon VF, Baringer W, Heil CA. Long-term evaluation of three satellite ocean color algorithms for identifying harmful algal blooms (Karenia brevis) along the west coast of Florida: A matchup assessment. Remote Sens Environ 2011; 115:1-18. [PMID: 22180667 PMCID: PMC3238914 DOI: 10.1016/j.rse.2010.07.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique - taken from the published literature - and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75°N and 28.25°N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5 × 10(4) cells L(-1)) varied considerably (sensitivity-Empirical: 86%; Bio-optical: 77%; Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity-Empirical: 53%; Bio-optical: 65%; Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (~70% for both) and showing low levels of uncertainties (i.e. few cases of false-negatives and false-positives: ~30%)-improved positive predictive values (~60%) were also observed along with good negative predictive values (~80%).
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Affiliation(s)
- Gustavo A. Carvalho
- Division of Meteorology and Physical Oceanography, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
- NSF NIEHS Oceans and Human Health Center, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Peter J. Minnett
- Division of Meteorology and Physical Oceanography, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
- NSF NIEHS Oceans and Human Health Center, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Viva F. Banzon
- Division of Meteorology and Physical Oceanography, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Warner Baringer
- Division of Meteorology and Physical Oceanography, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Cynthia A. Heil
- Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, St. Petersburg, FL, USA
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