An absorption model to determine phytoplankton size classes from satellite ocean colour

Hirata, Takafumi; Aiken, James; Hardman-Mountford, NJ; Smyth, Tim; Barlow, Raymond G

doi:10.1016/j.rse.2008.03.011

Cited by 207 publications

(174 citation statements)

References 30 publications

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“…Pico-sized phytoplankton again dominated the autotrophic community, but with a greater proportion of the total biomass contributed by larger phytoplankton size classes. This was also reflected by the higher chlorophyll concentration (consistent with global-scale pigment analyses; [56][57][58]). …”

Section: Model Set-upsupporting

confidence: 70%

Impacts of light shading and nutrient enrichment geo-engineering approaches on the productivity of a stratified, oligotrophic ocean ecosystem

Hardman-Mountford

Polimene

Hirata

et al. 2013

J. R. Soc. Interface.

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Geo-engineering proposals to mitigate global warming have focused either on methods of carbon dioxide removal, particularly nutrient fertilization of plant growth, or on cooling the Earth's surface by reducing incoming solar radiation (shading). Marine phytoplankton contribute half the Earth's biological carbon fixation and carbon export in the ocean is modulated by the actions of microbes and grazing communities in recycling nutrients. Both nutrients and light are essential for photosynthesis, so understanding the relative influence of both these geo-engineering approaches on ocean ecosystem production and processes is critical to the evaluation of their effectiveness. In this paper, we investigate the relationship between light and nutrient availability on productivity in a stratified, oligotrophic subtropical ocean ecosystem using a one-dimensional water column model coupled to a multi-plankton ecosystem model, with the goal of elucidating potential impacts of these geo-engineering approaches on ecosystem production. We find that solar shading approaches can redistribute productivity in the water column but do not change total production. Macronutrient enrichment is able to enhance the export of carbon, although heterotrophic recycling reduces the efficiency of carbon export substantially over time. Our results highlight the requirement for a fuller consideration of marine ecosystem interactions and feedbacks, beyond simply the stimulation of surface blooms, in the evaluation of putative geo-engineering approaches.

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Section: Model Set-upsupporting

confidence: 70%

Impacts of light shading and nutrient enrichment geo-engineering approaches on the productivity of a stratified, oligotrophic ocean ecosystem

Hardman-Mountford

Polimene

Hirata

et al. 2013

J. R. Soc. Interface.

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“…Phytoplankton composition product References ABUNDANCE Size classes Uitz et al, 2006;Brewin et al, 2010Brewin et al, , 2015 Size classes and multiple taxa Hirata et al, 2011 SPECTRAL REFLECTANCE Multiple taxa Alvain et al, 2005Alvain et al, , 2008Li et al, 2013;Ben Mustapha et al, 2014 Single taxon Coccolithophores Brown and Yoder, 1994;Moore et al, 2012Trichodesmium Subramaniam et al, 2002Westberry et al, 2005 ABSORPTION Size index Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012 Size classes Devred et al, 2006Devred et al, , 2011Hirata et al, 2008;Fujiwara et al, 2011;Roy et al, 2013 Multiple taxa Bracher et al, 2009;Sadeghi et al, 2012a;Werdell et al, 2014 BACK-SCATTERING Size classes Kostadinov et al, 2009Kostadinov et al, , 2016Fujiwara et al, 2011 ECOLOGICAL Taxonomic groups Palacz et al, 2013 Frontiers in Marine Science | www.frontiersin.orgFIGURE 1 | Illustration of phytoplankton diversity as found in nature impacted by environmental conditions, and how it can be derived from observations and modeling. Through in situ measurements (which represent the most real conditions), phytoplankton are grouped according to cellular traits that influence their optical properties such as pigments, size, morphology, and fluorescence, all also responding to photophysiology, which are named optical features of phytoplankton groups (PG).…”

Section: Approachmentioning

confidence: 99%

“…by the variation in phytoplankton structure and pigment composition (Brown and Yoder, 1994;Subramaniam et al, 2002;Alvain et al, 2005Alvain et al, , 2008Westberry et al, 2005;Ciotti and Bricaud, 2006;Devred et al, 2006Devred et al, , 2011Hirata et al, 2008;Bracher et al, 2009;Kostadinov et al, 2009Kostadinov et al, , 2016Mouw and Yoder, 2010;Fujiwara et al, 2011;Bricaud et al, 2012;Moore et al, 2012;Sadeghi et al, 2012a;Li et al, 2013;Roy et al, 2013;Ben Mustapha et al, 2014;Werdell et al, 2014). Spectral-based approaches exploit as much of the backscattered spectrum observed by satellite as necessary to extract the signatures of specific PG to ocean color.…”

Section: Approachmentioning

confidence: 99%

“…While the approach of Raitsos et al (2008) was regionally developed for the North-Atlantic, the approach by Palacz et al (2013) is not purely based on remote sensing data but also requires a coupling to a dynamic plankton model. Products obtained from the PG algorithms (Table 2) are typically dominance (Brown and Yoder, 1994;Alvain et al, 2005;Moore et al, 2012;Ben Mustapha et al, 2014), presence or absence of a certain PT (Westberry et al, 2005;Werdell et al, 2014), fraction or concentration of chl-a of the three PSC (Devred et al, 2006(Devred et al, , 2011Uitz et al, 2006;Hirata et al, 2008Hirata et al, , 2011Kostadinov et al, 2009Kostadinov et al, , 2016Brewin et al, 2010Brewin et al, , 2015Fujiwara et al, 2011;Li et al, 2013;Roy et al, 2013) or a size factor characterizing the contribution of pico-(or micro-) phytoplankton to the phytoplankton community (Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012). Currently, only the products OC-PFT and PhytoDOAS (Bracher et al, 2009;Sadeghi et al, 2012a) enable the simultaneous determination of chl-a for several PT.…”

Section: Approachmentioning

confidence: 99%

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Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit

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“…Thus, we exclude ecologically based methods that require additional physical and spatio-temporal information (e.g., Raitsos et al, 2008;Palacz et al, 2013). We utilize all of the algorithms that Kostadinov et al (2017) directly compare plus three additional algorithms (Hirata et al, 2008;Devred et al, 2011;Li et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

et al. 2017

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Phytoplankton are composed of diverse taxonomical groups, which are manifested as distinct morphology, size, and pigment composition. These characteristics, modulated by their physiological state, impact their light absorption and scattering, allowing them to be detected with ocean color satellite radiometry. There is a growing volume of literature describing satellite algorithms to retrieve information on phytoplankton composition in the ocean. This synthesis provides a review of current methods and a simplified comparison of approaches. The aim is to provide an easily comprehensible resource for non-algorithm developers, who desire to use these products, thereby raising the level of awareness and use of these products and reducing the boundary of expert knowledge needed to make a pragmatic selection of output products with confidence. The satellite input and output products, their associated validation metrics, as well as assumptions, strengths, and limitations of the various algorithm types are described, providing a framework for algorithm organization to assist users and inspire new aspects of algorithm development capable of exploiting the higher spectral, spatial and temporal resolutions from the next generation of ocean color satellites.

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An absorption model to determine phytoplankton size classes from satellite ocean colour

Cited by 207 publications

References 30 publications

Impacts of light shading and nutrient enrichment geo-engineering approaches on the productivity of a stratified, oligotrophic ocean ecosystem

Impacts of light shading and nutrient enrichment geo-engineering approaches on the productivity of a stratified, oligotrophic ocean ecosystem

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

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