Plankton Imagery Data Inform Satellite‐Based Estimates of Diatom Carbon

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.