Incorporating environmental data in abundance-based algorithms for deriving phytoplankton size classes in the Atlantic Ocean

Global Chlorophyll a Concentrations of Phytoplankton Functional Types With Detailed Uncertainty Assessment Using Multisensor Ocean Color and Sea Surface Temperature Satellite Products

“…Nonetheless, HPLC‐based pigment measurements still remain as one of the most widespread and quality‐controlled methods currently available, and are robust for quantifying pigments and their concentrations (Van Heukelem & Hooker, 2011). However, the methods that transform the pigment concentrations into PFT quantities rely on assumptions that might be violated (Moore & Brown, 2020). There are still large uncertainties in how we characterize the PSCs and PFTs in situ (e.g., Brewin et al., 2014; Chase et al., 2020).…”

“…Ward (2015) reported increases of microphytoplankton and nanophytoplankton Chl-a and decreases of picophytoplankton Chl-a in cold polar regions using the temperature-dependent functions compared to the temperature-independent functions. Moore & Brown (2020), XI ET AL.…”

“…(2017) both incorporated temperature dependence into the three‐component model and improved the model’s ability in representing Chl‐a concentrations in all three PSCs using satellite estimates of SST and total Chl‐a concentration. Moore and Brown (2020) assessed the impact of separately including SST and estimates of light level in the surface mixed‐layer on the skill of abundance‐based PSC models. Lange et al.…”

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

“…Remotely-sensed physical ocean properties such as SST can be useful to further constrain empirical models that predict algal abundance. SST can be used as a powerful predictor for the accumulation of cells when direct or indirect relationships between SST and certain ecological conditions that favor the target taxon are well known, as previously demonstrated for the prediction of blooms of the harmful dinoflagellate Alexandrium fundyense in the Bay of Fundy [61] and blooms of the diatom Pseudo-nitzschia in Chesapeake Bay [62], and in other predicting models for the biomass of specific phytoplankton groups [30,63,64]. In our study, the inclusion of SST was relevant for predicting the abundances of Prochlorococcus and picoeukaryotes, as ecological niches of both taxa are extremely constrained by temperature [65][66][67].…”

“…Bio-optical algorithms are subsequently applied to the R rs (λ) to produce estimates of near-surface concentrations of the photosynthetic pigment chlorophyll-a (Chl; mg m −3 ) and other metrics of phytoplankton community composition [25][26][27]. Other existing bio-optical algorithms provide abundances or biomass of different phytoplankton using unique empirical relationships between cell abundance and R rs (λ), as well as additional satellite observables such as sea surface temperature (SST;°C) and photosynthetically active radiation (PAR; µE m −2 s −1 ) [9,[28][29][30].…”