The scattering and backscattering coefficients of 15 phytoplankton species were determined in the laboratory using the acs and BB9 instruments. The spectral variability of scattering properties was investigated and the homogenous sphere model based on Mie theory was also evaluated. The scattering efficiencies at 510 nm varied from 1.42 to 2.26, and the backscattering efficiencies varied from 0.003 to 0.020. The backscattering ratios at 510 nm varied from 0.17% to 0.97%, with a mean value of 0.58%. The scattering properties were influenced by algal cell size and cellular particulate organic carbon content rather than the chlorophyll a concentration. Comparison of the measured results to the values estimated using the homogenous sphere model showed that: (1) The model could well reproduce the spectral scattering coefficient with relative deviations of 5–39%, which indicates that cell shape and internal structure have no significant effects on predicting the scattering spectra; (2) Although the homogenous sphere model generally reflected the spectral trend of backscattering spectra for most species, it severely underestimated the backscattering coefficients by 1.4–48.6 folds at 510 nm. The deviations for Chaetoceros sp. and Microcystis aeruginosa were large and might be due to algal cell chain links and intracellular gas vacuoles, respectively.

Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (a_ph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

The standing stock of phytoplankton carbon is a basic and essential property for understanding oceanic ecosystems, biogeochemical cycles, and regional climates. However, current related algorithms mainly focus on remote-sensed application, which cannot describe the vertical profile of phytoplankton carbon throughout the whole euphotic zone. In this study, we modified a previous absorption-based bio-optical algorithm to acquire vertical variabilities of the total and size-partitioned phytoplankton carbon based on field data from the South China Sea (SCS). The mean absolute errors and the biases between estimated and field picophytoplankton carbon were <2.14 and 0.6–2.0, respectively. The results showed that the vertical profile of total phytoplankton carbon displayed a Gaussian distribution in the stratified SCS basin. The picophytoplankton carbon was always the fundamental component of the total phytoplankton carbon within the whole euphotic zone. The dominant picophytoplankton species changed from Synechococcus-like cyanobacteria at the sea surface to pico-sized haptophytes at the phytoplankton carbon maximum layer. The strong covariation between total phytoplankton carbon and chlorophyll-a concentration suggested that they can be converted into each other through an accurate carbon-to-chlorophyll ratio in the open SCS. These results provide essential information that can be used to decipher the three-dimensional structure of total and size-partitioned phytoplankton carbon in the open SCS.

The diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm $(K_{\mathrm{d}}\left(490\right))$ has complex characteristics in the coastal regions. However, owing to the scarcity of in situ data, our knowledge on the diurnal variation is inadequate. In this study, an optical-buoy dataset was used to investigate the diurnal variation of $K_{\mathrm{d}}\left(490\right)$ in the coastal East China Sea, and to evaluate the $K_{\mathrm{d}}\left(490\right)$ L2 products of geostationary ocean color imager (GOCI), as well as the performance of six empirical algorithms for $K_{\mathrm{d}}\left(490\right)$ estimation in the Case-2 water. The results of validation show that there was high uncertainty in GOCI L2 $K_{\mathrm{d}}\left(490\right)$, with mean absolute percentage errors (MAPEs) of 69.57% and 68.86% and root mean square errors (RMSEs) of 0.70 and 0.71 m⁻¹ compared to buoy-measured $K_{\mathrm{d}12}\left(490\right)$ and $K_{\mathrm{d}13}\left(490\right)$, respectively. Meanwhile, with the coefficient of determination (R²) of 0.71, as well as the lowest MAPE of 27.31% and RMSE of 0.29 m⁻¹, the new dual ratio algorithm (NDRA) performed the best in estimating $K_{\mathrm{d}}\left(490\right)$ in the target area, among the six algorithms. Further, four main types of $K_{\mathrm{d}}\left(490\right)$ diurnal variation were found from buoy data, showing different variabilities compared to the area closer to the shore. One typical diurnal variation pattern showed that $K_{\mathrm{d}}\left(490\right)$ decreased at flood tide and increased at ebb tide, which was confirmed by GOCI images through the use of NDRA. Hydrometeorological factors influencing the diurnal variations of $K_{\mathrm{d}}\left(490\right)$ were also studied. In addition to verifying the predominant impact of tide, we found that the dominant effect of tide and wind on the water column is intensifying sediment resuspension, and the change of sediment transport produced by them are secondary to it.

Phytoplankton cell size is an important property that affects diverse ecological and biogeochemical processes, and analysis of the absorption and scattering spectra of phytoplankton can provide important information about phytoplankton size. In this study, an inversion method for extracting quantitative phytoplankton cell size data from these spectra was developed. This inversion method requires two inputs: chlorophyll $a$ specific absorption and scattering spectra of phytoplankton. The average equivalent-volume spherical diameter (${\mathrm{ESD}}_v$) was calculated as the single size approximation for the log-normal particle size distribution (PSD) of the algal suspension. The performance of this method for retrieving cell size was assessed using the datasets from cultures of 12 phytoplankton species. The estimations of $a(\lambda )$ and $b(\lambda )$ for the phytoplankton population using ${\mathrm{ESD}}_v$ had mean error values of 5.8%–6.9% and 7.0%–10.6%, respectively, compared to the $a(\lambda )$ and $b(\lambda )$ for the phytoplankton populations using the log-normal PSD. The estimated values of $C_i{\mathrm{ESD}}_v$ were in good agreement with the measurements, with $r^2=0.88$ and relative root mean square error $(\mathrm{NRMSE})=25.3\%$, and relatively good performances were also found for the retrieval of ${\mathrm{ESD}}_v$ with $r^2=0.78$ and $\mathrm{NRMSE}=23.9\%$.