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Remote Sens. 2018, 10(6), 915; https://doi.org/10.3390/rs10060915

Extraction of Photosynthesis Parameters from Time Series Measurements of In Situ Production: Bermuda Atlantic Time-Series Study

1
Institute of Oceanography and Fisheries, Šetalište I. Meštrovića 63, 21000 Split, Croatia
2
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
3
National Centre for Earth Observations, Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
4
Bigelow Laboratory for Ocean Sciences, 60 Bigelow Drive, East Boothbay, ME 04544, USA
*
Author to whom correspondence should be addressed.
Received: 29 April 2018 / Revised: 2 June 2018 / Accepted: 7 June 2018 / Published: 9 June 2018
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Abstract

Computing the vertical structure of primary production in ocean ecosystem models requires information about the vertical distribution of available light, chlorophyll concentration and photosynthesis response parameters. Conversely, given information on vertical structure of chlorophyll and light, we can extract photosynthesis parameters from vertical profiles of primary production measured at sea, as we illustrate here for the Bermuda Atlantic Time-Series Study. The procedure is based on a model of the production profile, which itself depends on the underwater light field. To model the light field, attenuation coefficients were estimated from measured optical profiles using a simple model of exponential decay of photosynthetically-available irradiance with depth, which accounted for 97% of the variance in the measured optical data. With the underwater light climate known, an analytical solution for the production profile was employed to recover photosynthesis parameters by minimizing the residual model error. The recovered parameters were used to model normalized production profiles and normalized watercolumn production. The model explained 95% of the variance in the measured normalized production at depth and 97% of the variance in measured normalized watercolumn production. A shifted Gaussian function was used to model biomass profiles and accounted for 93% of the variance in measured biomass at depth. An analytical solution for watercolumn production with the shifted Gaussian biomass was also tested. With the recovered photosynthesis parameters, maximum instantaneous growth rates were estimated by using a literature value for the carbon-to-chlorophyll ratio in this region of the Atlantic. An exact relationship between the maximum instantaneous growth rate and the daily growth rate in the ocean was derived. It was shown that calculating the growth rate by dividing the production by the carbon-to-chlorophyll ratio is equivalent to calculating it from the ratio of the final to the initial biomass, even when production is time dependent. Finally, the seasonal cycle of the recovered assimilation number at the Bermuda Station was constructed and analysed. The presented approach enables the estimation of photosynthesis parameters and growth rates from measured production profiles with only a few model assumptions, and increases the utility of in situ primary production measurements. The retrieved parameters have direct applications in satellite-based estimates of primary production from ocean-colour data, of which we give an example. View Full-Text
Keywords: primary production; photosynthesis parameters; production profile; phytoplankton growth rates; seasonal cycle; Bermuda Atlantic Time-series Study primary production; photosynthesis parameters; production profile; phytoplankton growth rates; seasonal cycle; Bermuda Atlantic Time-series Study
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kovač, Ž.; Platt, T.; Sathyendranath, S.; Lomas, M.W. Extraction of Photosynthesis Parameters from Time Series Measurements of In Situ Production: Bermuda Atlantic Time-Series Study. Remote Sens. 2018, 10, 915.

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