Abstract
Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPDCV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.
Highlights
The magnitude of oceanic carbon fixation by phytoplankton through photosynthesis is comparable to net production by terrestrial plants at the global scale, making phytoplankton a key component in the global carbon cycle
Low values of their median absolute percent difference (MAPD) suggest that the Chl-a model could retrieve these pigments reliably
We explored a number of methods for the retrieval of pigments concentrations from bio-optical measurements, including an empirical model based on chlorophyll-a, a Gaussian model, and a partial least squares (PLS) regression model applied to phytoplankton absorption data after they were subjected to principal component analysis
Summary
The magnitude of oceanic carbon fixation by phytoplankton through photosynthesis is comparable to net production by terrestrial plants at the global scale, making phytoplankton a key component in the global carbon cycle. Phytoplankton dynamics are important when examining existential threats such as the greenhouse effect and ocean acidification [1,2,3,4]. The role of phytoplankton in various biogeochemical cycles varies with their functional type. Important phytoplankton types can be distinguished through auxiliary pigments that are referred to as marker pigments. It is a basic requirement in the field of marine ecology to study the quantitative distribution of these marker pigments at large spatiotemporal scales. Their concentrations, together with that of the main photosynthetic pigment, chlorophyll-a (Chl-a), can be estimated using high-performance liquid chromatography (HPLC) techniques.
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