Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

Abstract

Phytoplankton in the ocean are extremely diverse. The abundance of various intracellular pigments are often used to study phytoplankton physiology and ecology, and identify and quantify different phytoplankton groups. In this study, phytoplankton absorption spectra ( a p h ( λ ) ) derived from underway flow-through AC-S measurements in the Fram Strait are combined with phytoplankton pigment measurements analyzed by high-performance liquid chromatography (HPLC) to evaluate the retrieval of various pigment concentrations at high spatial resolution. The performances of two approaches, Gaussian decomposition and the matrix inversion technique are investigated and compared. Our study is the first to apply the matrix inversion technique to underway spectrophotometry data. We find that Gaussian decomposition provides good estimates (median absolute percentage error, MPE 21–34%) of total chlorophyll-a (TChl-a), total chlorophyll-b (TChl-b), the combination of chlorophyll-c1 and -c2 (Chl-c1/2), photoprotective (PPC) and photosynthetic carotenoids (PSC). This method outperformed one of the matrix inversion algorithms, i.e., singular value decomposition combined with non-negative least squares (SVD-NNLS), in retrieving TChl-b, Chl-c1/2, PSC, and PPC. However, SVD-NNLS enables robust retrievals of specific carotenoids (MPE 37–65%), i.e., fucoxanthin, diadinoxanthin and 19 ′ -hexanoyloxyfucoxanthin, which is currently not accomplished by Gaussian decomposition. More robust predictions are obtained using the Gaussian decomposition method when the observed a p h ( λ ) is normalized by the package effect index at 675 nm. The latter is determined as a function of “packaged” a p h ( 675 ) and TChl-a concentration, which shows potential for improving pigment retrieval accuracy by the combined use of a p h ( λ ) and TChl-a concentration data. To generate robust estimation statistics for the matrix inversion technique, we combine leave-one-out cross-validation with data perturbations. We find that both approaches provide useful information on pigment distributions, and hence, phytoplankton community composition indicators, at a spatial resolution much finer than that can be achieved with discrete samples.

High Resolution Water Column Phytoplankton Composition Across the Atlantic Ocean From Ship-Towed Vertical Undulating Radiometry

“…E.g., Bracher et al (2015), using in a similar region the same type of prediction models but based on field remote sensing reflectance data, obtained for R 2 cv, MPDcv and RMSDcv values ranging from 0.35 to 0.80, 28 to 43%, and 0.48 to 0.82. Chase et al (2013) and Liu et al (2019) derived phytoplankton pigments (different types of chlorophylls and the two major carotenoid groups) in the global and Arctic Ocean, respectively, by applying the Gaussian band method to hyperspectral IOP data from underway spectrophotometry; their validation results gave MPD values between 36-53% and 21-34%, respectively. Applying the matrix inversion technique in Liu et al (2019) to the same Arctic data set allowed for the quantification of more specific carotenoid pigments, however, with larger MDP values (37-65%).…”

“…Chase et al (2013) and Liu et al (2019) derived phytoplankton pigments (different types of chlorophylls and the two major carotenoid groups) in the global and Arctic Ocean, respectively, by applying the Gaussian band method to hyperspectral IOP data from underway spectrophotometry; their validation results gave MPD values between 36-53% and 21-34%, respectively. Applying the matrix inversion technique in Liu et al (2019) to the same Arctic data set allowed for the quantification of more specific carotenoid pigments, however, with larger MDP values (37-65%). Sauzede et al (2015) predicted fractions of PSC on TChla from fluorometric data using a neutral network based technique combined with the abundance based approach by Uitz et al (2006).…”

“…A few examples have shown this potential which should be further explored. Chase et al (2013) and Liu et al (2019) have derived high spatial resolution data on various pigments' surface concentrations from underway flow-through hyperspectral spectrophotometric measurements combined with HPLC based phytoplankton pigment point measurements. In both studies Gaussian decomposition was used to retrieve different types of chlorophylls besides the pigment groups of photosynthetic and photoprotective carotenoids.…”

“…From their data sets only one specific PG Chla, chlorophytes, could be retrieved. Liu et al (2019) have additionally applied the matrix inversion method by Moisan et al (2011) and successfully derived different types of carotenoids, among them marker pigments for diatoms and haptophytes. However, the pigment data sets of the two studies are too limited to further allow the retrieval of all PG being relevant to explain the total Chla (TChla) in the investigated areas, thereby restricting their further application.…”

Modeling Photoprotection at Global Scale: The Relative Role of Nonphotosynthetic Pigments, Physiological State, and Species Composition

“…Pigment data were obtained from published high-performance liquid chromatography data sets (Booge et al, 2018;Bracher et al, 2015b;Liu et al, 2018Liu et al, , 2019bPeloquin et al, 2013;Soppa et al, 2014;Taylor et al, 2011a;Trimborn et al, 2015;Zindler et al, 2013), from pigment data in the AEsOP-CSIRO database (http://aesop.csiro.au/) and from two not previously published HPLC data sets (Table 4). These new data sets (Bracher, 2019b;) encompassed the following cruises: the R/V Heincke cruise HE462 in the North Sea from 30 April to 7 May 2016 and the R/V Polarstern cruise PS103 in the South Atlantic from 17 December 2016 to 28 January 2017.…”