Discrimination of diatoms from other phytoplankton using ocean-colour data

Sathyendranath, Shubha; Watts, Louisa; Devred, Émmanuel; Platt, Trevor; Caverhill, Carla; Maass, Heidi

doi:10.3354/meps272059

Cited by 220 publications

(161 citation statements)

References 44 publications

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“…Dividing the database of photosynthetic parameters according to the physiological status of the phytoplankton community and according to the community composition would solve this problem. Use of a diatom-identification algorithm (Sathyendranath et al 2004) supplemented by a temporal derivative of chl a concentration provides a tool to map the different phases of the diatom bloom (blooming versus declining) and the mixed phytoplankton community (Fig. 6).…”

Section: Nearest-neighbour Methodsmentioning

confidence: 99%

“…4c). A ratio of fucoxanthin to chl a > 0.4 indicates samples dominated by diatoms (Sathyendranath et al 2004). Also, the increase of chlorophyll-specific absorption by phytoplankton over the time period is an index of the cell size decrease in the phytoplankton community (Fig.…”

Section: C-solas Lagrangian Sitementioning

confidence: 99%

“…Composite image of (a) sea surface temperature (SST, °C) derived from AVHHR (NOAA), (b) chl a (mg chl a m -3 ) derived from visible spectral radiometry (ocean colour), SeaWiFS, and (c) percentage of diatom-dominated pixels during the period of study (Sathyendranath et al 2004). Images span the time period from April 24-May 2, 2003.…”

Section: C-solas Lagrangian Sitementioning

confidence: 99%

“…They are also subject to changes in pigment composition and packaging effect, which 36 Forget et al: Computation of primary production from remote sensing results in variation in their optical properties (Nair 2007). These optical properties can be tracked by remote sensing of ocean colour using algorithms tuned to identify particular PFTs such as diatoms (Sathyendranath et al 2004), Trichodesmium (Subramaniam et al 1999) and other phytoplankton groups (Alvain et al 2005). Using such algorithms with the time derivative of the chl a concentration, the global ocean could be partitioned according to the dominant PFT and according to the occurrence of bloom or declining-bloom conditions.…”

Section: Remote-sensing Applicationsmentioning

confidence: 99%

“…In this algorithm, pixels were assigned to one of 3 possible classes using a combination of the diatom identification algorithm (Sathyendranath et al 2004) and the sign of the rate of change in the remotely- sensed chl a concentration over the 2 wk period (Fig. 6a).…”

Section: Satellite Applicationmentioning

confidence: 99%

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Computation of primary production from remote sensing of ocean colour at the northwestern Atlantic C-SOLAS Lagrangian site

Forget¹,

Fuentes-Yaco²,

Sathyendranath³

et al. 2007

Mar. Ecol. Prog. Ser.

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At the C-SOLAS Lagrangian Study site, the phytoplankton community was dominated by diatoms in declining bloom conditions, which were characterised by a decrease in primary production over time. Measurements of primary production were performed using photosynthesis-light experiments and simulated in situ incubations. Several methods were used to assign photosynthetic parameters for estimation of primary production from remote-sensing of ocean colour: (1) the nearestneighbour method (NNM), (2) a temperature-dependent model, (3) an iterative approach to retrieve photosynthetic parameters from in situ measurements of phytoplankton production, and (4) average values of measured parameters. Owing to the declining status of the diatom population and its patchy distribution, the magnitude of primary production measured from in situ incubations was highly variable. Under bloom conditions, all methods underestimated production compared with results of simulated in situ measurements; however, the NNM provided the closest estimates to the in situ measurements. In the declining bloom phase, the NNM overestimated primary production, whereas the iterative method returned estimates of production in good agreement with the in situ observations. In conditions characterized by mixed-phytoplankton populations, the NNM returned estimates in agreement with the observations. A new method is proposed for classifying image pixels using criteria accessible to remote sensing, according to the phase of the diatom bloom (bloom versus declining conditions) and according to community composition (diatom versus mixed population). Upon separating the database used for parameter assignment into 2 parts, one representing the general phytoplankton community and the other representing declining diatom conditions, estimates of chlorophyll a-normalised water-column production were in good agreement with in situ observations. KEY WORDS: Response · Primary production · Remote sensing of ocean colour · Photosynthetic parametersResale or republication not permitted without written consent of the publisher Mar Ecol Prog Ser 352: 27-38, 2007 through phytoplankton photosynthesis in the ocean amounts to 50 Gt C yr -1 and is equivalent in magnitude to terrestrial primary production , Behrenfeld & Falkowski 1997, Behrenfeld et al. 2001. Moreover, through the biological pump, a proportion of this carbon will be transferred to the ocean floor via sedimentation of organic material (Longhurst & Harrison 1989). Quantification of primary production in the ocean is therefore of central importance to programmes such as SOLAS.Remote sensing of ocean colour is an effective method for computing primary production on a global scale , Antoine et al. 1996, Behrenfeld & Falkowski 1997. The method relies on estimation of chl a at the surface, estimation of the attenuation coefficient for downwelling light and the assignment of parameters describing the biomass profile at each image pixel, as well as the photosynthetic response to available light . The first 2 est...

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Section: Nearest-neighbour Methodsmentioning

confidence: 99%

Section: C-solas Lagrangian Sitementioning

confidence: 99%

Section: C-solas Lagrangian Sitementioning

confidence: 99%

Section: Remote-sensing Applicationsmentioning

confidence: 99%

Section: Satellite Applicationmentioning

confidence: 99%

See 3 more Smart Citations

Computation of primary production from remote sensing of ocean colour at the northwestern Atlantic C-SOLAS Lagrangian site

Forget¹,

Fuentes-Yaco²,

Sathyendranath³

et al. 2007

Mar. Ecol. Prog. Ser.

Self Cite

View full text Add to dashboard Cite

show abstract

A multicomponent model of phytoplankton size structure

et al. 2014

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Size-fractionated filtration (SFF) is a direct method for estimating pigment concentration in various size classes. It is also common practice to infer the size structure of phytoplankton communities from diagnostic pigments estimated by high-performance liquid chromatography (HPLC). In this paper, the three-component model of Brewin et al. (2010) was fitted to coincident data from HPLC and from SFF collected along Atlantic Meridional Transect cruises. The model accounted for the variability in each data set, but the fitted model parameters differed for the two data sets. Both HPLC and SFF data supported the conceptual framework of the three-component model, which assumes that the chlorophyll concentration in small cells increases to an asymptotic maximum, beyond which further increase in chlorophyll is achieved by the addition of larger celled phytoplankton. The three-component model was extended to a multicomponent model of size structure using observed relationships between model parameters and assuming that the asymptotic concentration that can be reached by cells increased linearly with increase in the upper bound on the cell size. The multicomponent model was verified using independent SFF data for a variety of size fractions and found to perform well (0.628 r 0.989) lending support for the underlying assumptions. An advantage of the multicomponent model over the three-component model is that, for the same number of parameters, it can be applied to any size range in a continuous fashion. The multicomponent model provides a useful tool for studying the distribution of phytoplankton size structure at large scales.

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Spatiotemporal chlorophyll-adynamics on the Louisiana continental shelf derived from a dual satellite imagery algorithm

Lehrter

et al. 2014

J. Geophys. Res. Oceans

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A monthly time series of remotely sensed chlorophyll-a (Chla rs ) over the Louisiana continental shelf (LCS) was developed and examined for its relationship to river discharge, nitrate concentration, total phosphorus concentration, photosynthetically available radiation (PAR), wind speed, and interannual variation in hypoxic area size. A new algorithm for Chla rs , tuned separately for clear and turbid waters, was developed using field-observed chlorophyll-a (Chla obs ) collected during 12 cruises from 2002 to 2007. The new algorithm reproduced Chla obs , with $40% and $60% uncertainties at satellite pixel level for clear offshore waters and turbid nearshore waters, respectively. The algorithm was then applied to SeaWiFS and MODIS images to calculate long-term (1998-2013) monthly mean Chla rs estimates at 1 km resolution across the LCS. Correlation and multiple stepwise regression analyses were used to relate the Chla rs estimates to key environmental drivers expected to influence phytoplankton variability. The Chla rs time series covaried with river discharge and nutrient concentration, PAR, and wind speed, and there were spatial differences in how these environmental drivers influenced Chla rs . The main axis of spatial variability occurred in a cross-shelf direction with highest Chla rs observed on the inner shelf. Both inner (<10 m depth) and middle-shelf (10-50 m depth) Chla rs were observed to covary with interannual variations in the size of the hypoxic (O 2 < 63 mmol m 23 ) area, and they explained $70 and $50% variability in interannual hypoxia size, respectively.

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Discrimination of diatoms from other phytoplankton using ocean-colour data

Cited by 220 publications

References 44 publications

Computation of primary production from remote sensing of ocean colour at the northwestern Atlantic C-SOLAS Lagrangian site

Computation of primary production from remote sensing of ocean colour at the northwestern Atlantic C-SOLAS Lagrangian site

A multicomponent model of phytoplankton size structure

Spatiotemporal chlorophyll-adynamics on the Louisiana continental shelf derived from a dual satellite imagery algorithm

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