Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

Abstract

The differences among phytoplankton carbon (C phy ) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in C phy . Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m −3 ), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m −3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscatteringbased algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (b bp ) retrieval.

Global Variability of Optical Backscattering by Non‐algal particles From a Biogeochemical‐Argo Data Set

“…The is a key parameter for satellite estimations of phytoplankton biomass in terms of carbon (Behrenfeld et al, , ; Bellacicco et al, , , ; Martínez‐Vicente et al, ; Westberry et al, , ). Recently, Bellacicco et al () highlighted the difference (of around a factor of 2) in the phytoplankton carbon biomass estimation from space by using a variable in space, rather than a single value.…”

“…Recently, Bellacicco et al () highlighted the difference (of around a factor of 2) in the phytoplankton carbon biomass estimation from space by using a variable in space, rather than a single value. Consequently, inclusion of this reported spatial‐temporal and depth variations of into phytoplankton carbon models may help to improve their predictions from remote sensing data (Martínez‐Vicente et al, ) but also from BGC‐Argo floats (Mignot et al, , ).…”

Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm

“…Former research suggested that b bp is mostly influenced by submicron non-algal particles [9][10][11]. However, it has been recently shown that most of b bp is due to particles with equivalent diameters between 1 and 10 µm [8], thus including the contribution of phytoplankton cell and supporting the use of b bp for the retrieval of: (i) particulate organic concentration (POC) [12,13]; (ii) particle size distribution [14,15]; and (iii) phytoplankton carbon biomass concentration (C phyto ; mg m −3 ) [16][17][18], a key parameter also for phytoplankton physiology studies [2,19,20]. Efficiency in the b bp retrieval is crucial for ocean biology and global ocean carbon estimations.…”

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

“…Consequently, the optimization of phytoplankton carbon models (e.g. CbPM) using the spatial-temporal and depth NAP variables is suggested to improve their modeling performance from remote sensing observations [99].…”