Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

TLDR: For example, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more as discussed by the authors, due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals.

Abstract: Phytoplankton biomass and productivity have been continuously monitored from ocean color satellites for over a decade. Yet, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more. Such variability is due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals. The empirical algorithms have built-in assumptions that follow the basic precept of biological oceanography—namely, oligotrophic regions with low phytoplankton biomass are populated with small phytoplankton, whereas more productive regions contain larger bloom-forming phytoplankton. With a changing world ocean, phytoplankton composition may shift in response to altered environmental forcing, and CDOM and mineral concentrations may become uncoupled from phytoplankton stocks, creating further uncertainty and error in the empirical approaches. Hence, caution is warranted when using empirically derived Chl to infer climate-related changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellite-derived Chl for different phytoplankton assemblages. Accurate global assessments of phytoplankton will require improved technology and modeling, enhanced field observations, and ongoing validation of our “eyes in space.”