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The Ocean Colour Climate Change Initiative: III. A Round-Robin Comparison on In-Water Bio-Optical AlgorithmsSatellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semianalytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.
Document ID
20150023345
Document Type
Reprint (Version printed in journal)
Authors
Brewin, Robert J.W. (Plymouth Marine Lab. United Kingdom)
Sathyendranath, Shubha (Plymouth Marine Lab. United Kingdom)
Muller, Dagmar (Helmholtz-Zentrum Geesthacht Germany)
Brockmann, Carsten (Brockmann Consult Geesthacht)
Deschamps, Pierre-Yves (Hygeos Earth Observation Lille, France)
Devred, Emmanuel (Laval Univ. Sainte-Foye, Quebec, Canada)
Doerffer, Roland (Helmholtz-Zentrum Geesthacht Germany)
Fomferra, Norman (Brockmann Consult Geesthacht)
Franz, Bryan (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Grant, Mike (Plymouth Marine Lab. United Kingdom)
Groom, Steve (Plymouth Marine Lab. United Kingdom)
Horseman, Andrew (Plymouth Marine Lab. United Kingdom)
Hu, Chuanmin (University of South Florida Saint Petersburg, FL, United States)
Krasemann, Hajo (Helmholtz-Zentrum Geesthacht Germany)
Lee, ZhongPing (Massachusetts Univ. Boston, MA, United States)
Maritorena, Stephane (California Univ. Santa Barbara, CA, United States)
Melin, Frederic (Commission of the European Communities Ispra, Italy)
Peters, Marco (Brockmann Consult Geesthacht)
Platt, Trevor (Plymouth Marine Lab. United Kingdom)
Regner, Peter (European Space Agency. ESRIN Frascati, Italy)
Smyth, Tim (Plymouth Marine Lab. United Kingdom)
Steinmetz, Francois (Hygeos Earth Observation Lille, France)
Swinton, John (Telespazio VEGA UK Ltd. United Kingdom)
Werdell, Jeremy (NASA Goddard Space Flight Center Greenbelt, MD, United States)
White, George N., III (Bedford Inst. of Oceanography Dartmouth, Nova Scotia, Canada)
Date Acquired
December 18, 2015
Publication Date
October 14, 2013
Publication Information
Publication: Remote Sensing of Enviornment
Volume: 162
Subject Category
Earth Resources and Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN23643
Funding Number(s)
WBS: WBS 444491.02.01.02.82
Distribution Limits
Public
Copyright
Other
Keywords
Phytoplankton
Remote Sensing
Ocean Color