Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

Abstract

Phytoplankton functional diversity plays a key role in structuring the ocean carbon cycle and can be estimated using measurements of phytoplankton functional type (PFT) groupings. Concentrations of 18 phytoplankton pigments were calculated using a linear matrix inversion algorithm, with an average r 2 value of 0.70 for all pigments with p-values below the statistical threshold of 0.05. The inversion algorithm was then used with a chlorophyll-based absorption spectra model and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) chlorophyll observations to calculate phytoplankton pigment concentrations in an area of the Atlantic Ocean off the United States east coast. Pigment distributions were analyzed to assess the distribution of PFTs. Five unique PFTs were found and delineated into three distinct offshore, transition, and open ocean groups. Group 1 (Diatoms) had highest abundance along the coast. Group 2 (prymnesiophytes, prokaryotes, and green algae) was a year-round stable offshore community that extended at reduced levels into the coast. Group 3 (dinoflagellates) dominated offshore between the Groups 1 and 2. Phytoplankton communities were delineated into coastal and offshore populations, with Group 2 having a dampened seasonal cycle, relative to the coastal populations. Shannon Diversity Indices (H) for the PFTs showed both spatial and temporal variability and had a clear non-linear relationship with chlorophyll. Diversity levels varied seasonally with changes in chlorophyll a levels. Peak PFT H was observed on the shelf where frontal features dominate, with diversity levels declining nearshore and offshore. This region marks an ecotone for phytoplankton in the study domain, and is associated with the coastal-side boundary of dinoflagellate dominance. Highest levels of diversity were observed in the tidally well-mixed regions of the Gulf of Maine and along a band that ran along the shelf region of the study area that was narrowest in the summer periods and broadened during the winter. These peak diversity zones were associated with moderate levels (∼0.8 mg m −3 ) of chlorophyll a. While the sign in the linear trends in chlorophyll between 2002 and 2016 varied depending on the region, the trends in the PFT H values were nearly all negative due to the non-linear relationship between chlorophyll levels and H.

Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

“…At the time this manuscript is drafted, it is the only method that has been applied to underway spectrophotometry data and successfully retrieved the concentrations of various pigments [22]. Compared with Gaussian decomposition and other methods from previous studies, e.g., [14,15,19,26] that are incapable of resolving PPC and PSC, the matrix inversion technique is capable of estimating these and various marker pigments indicative of phytoplankton composition [27,28,34]. Both Gaussian decomposition and the matrix inversion technique rely on the physical links between phytoplankton pigments and their distinct light absorption properties, while other methods' (e.g., principle component analysis) output is often physically uninterpretable.…”

“…The pigment package effect index Q * a (λ) can be calculated as the ratio of the measured a ph (λ) to the absorption coefficient of the same pigments which would be dispersed into solution [73,76]. To partially account for the package effect, Moisan et al [27,28,34] normalized the measured a ph (λ) by dividing it with Q * a (675) and found improved capability of the matrix inversion technique in retrieving pigment concentrations. To test the performances of both Gaussian decomposition and the matrix inversion technique with the normalization strategy, Q * a (675) was calculated, and a ph (λ) was normalized followingâ…”

“…Overall, the SVD-NNLS method achieved simultaneous statistically significant retrievals of TChl-a, total chlorophyll-c (TChl-c), β-carotene (β-Caro), Fuco, Viola, Diadino and peridinin (Peri) in U.S. east coast waters [27,28]. It was recently applied to a ph (λ) modeled from MODIS-Aqua TChl-a data for northeastern U.S. waters, yielding maps of the concentrations of ten pigments [34]. Similar approaches were successful in infering phytoplankton size classes globally [35,36] and taxonomic groups in the Chukchi and Bering Seas [33] from absorption data.…”

Multi-Spectral Remote Sensing of Phytoplankton Pigment Absorption Properties in Cyanobacteria Bloom Waters: A Regional Example in the Western Basin of Lake Erie

“…However, only limited work has been conducted to obtain these phytoplankton pigments from satellite remote sensing data. Pan et al [8] and Moisan et al [60], as two of them, attempted to obtain 12 and 18 different phytoplankton pigments from satellite remote sensing data, respectively, but both of them are empirical approach based. Pan et al [8] proposed using empirical relationships of 12 different phytoplankton pigments with the same band ratios of satellite R rs (λ) around 490 nm to 550 nm.…”

“…However, using 490 and 550 nm alone is not a good strategy for multiple pigments, as different pigments have different absorption peaks and troughs at different wavelengths [1]. Compared with Pan et al [8], Moisan et al [60] directly used the Chl-a product of satellite remote sensing to estimate a ph (λ), then decompose this a ph (λ) to 18 pigments based on their specific absorption coefficients. However, this satellite Chl-a product used similar empirical algorithm and spectral bands as in Pan et al [8].…”