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

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 climaterelated changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellitederived 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." biological oceanography | phytoplankton | primary productivity | satellite oceanography | ocean optics

Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

“…Confidence in the satellite estimates of SeaWiFS TCHLa can be taken from the fact that they compare favourably with weekly and daily AMT match-ups (Aiken et al, 2009;Brewin et al, 2010). Furthermore, seasonal and inter-annual variations (including trends) in SeaWiFS TCHLa are consistent with those derived using the MODISAqua and MERIS ocean-colour sensors over a similar time-period (2002-2010see Brewin et al, 2014c), lending confidence to the SeaWiFS results. Blending in-situ and satellite observations of TCHLa (e.g.…”

“…For instance, it has been shown that information on the absorption and backscattering properties of phytoplankton size is implicit in remote-sensing algorithms that empirically relate TCHLa to band-ratio reflectance (Dierssen, 2010;IOCCG, 2014). In fact, these algorithms implicitly assume a fixed relationship between PSC and TCHLa (IOCCG, 2014).…”

“…Even standard empirical algorithms used for estimating TCHLa from satellite ocean-colour data implicitly assume a fixed relationship between TCHLa and PSC (Dierssen, 2010, IOCCG, 2014. This assumption implies that a change in one indicator will result in predictable modification in the other.…”

Space station image captures a red tide ciliate bloom at high spectral and spatial resolution

“…The standard chlorophyll a (Chl a) algorithm, however, fails in the optically complex coastal waters of WLIS due to rich concentrations of suspended minerals and colored dissolved organic matter (CDOM) (16)(17)(18). These blue-absorbing and scattering constituents can mimic the water color produced by phytoplankton and result in overestimates of their abundance, as shown by the high Chl a estimates throughout the sound (Fig.…”

“…2A). Tuned to open ocean conditions (16), the limited spectral information prevents differentiation of an M. rubrum bloom from other types of phytoplankton known to occur in this region (e.g., diatoms, dinoflagellates) (17).…”