Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi-sensor ocean color and sea surface temperature satellite products

Xi, Hongyan

doi:10.1002/essoar.10505775.1

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Our results also illustrate the potential for combining satellite data from multiple platforms in a neural network framework. The inclusion of ancillary environmental information such as water temperature and salinity can improve phytoplankton group modeling efforts (Figure S10 and Table S2 in Supporting Information ; see also Brewin et al., 2019; Xi et al., 2021). Looking forward, the upcoming NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission will include polarimetry and hyperspectral ocean color instruments that can potentially be combined with other remotely sensed or modeled data.…”

Section: Discussionmentioning

confidence: 99%

Plankton Imagery Data Inform Satellite‐Based Estimates of Diatom Carbon

Chase

Boss

Haëntjens

et al. 2022

Geophysical Research Letters

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Estimating the biomass of phytoplankton communities via remote sensing is a key requirement for understanding global ocean ecosystems. Of particular interest is the carbon associated with diatoms given their unequivocal ecological and biogeochemical roles. Satellite‐based algorithms often rely on accessory pigment proxies to define diatom biomass, despite a lack of validation against independent diatom biomass measurements. We used imaging‐in‐flow cytometry to quantify diatom carbon in the western North Atlantic, and compared results to those obtained from accessory pigment‐based approximations. Based on this analysis, we offer a new empirical formula to estimate diatom carbon concentrations from chlorophyll a. Additionally, we developed a neural network model in which we integrated chlorophyll a and environmental information to estimate diatom carbon distributions in the western North Atlantic. The potential for improving satellite‐based diatom carbon estimates by integrating environmental information into a model, compared to models that are based solely on chlorophyll a, is discussed.

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Section: Discussionmentioning

confidence: 99%