The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (a_t-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from a_t-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r² between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea.

Using large amounts of bio-optical data collected in the South China Sea (SCS) from 2003 to 2016, this study checks the consistency between well-known semi-analytical algorithms (SAAs)—the quasi-analytical algorithm (QAA) and the default generalized inherent optical property (GIOP-DC)—in retrieving the non-water absorption coefficient (a_nw(λ)), phytoplankton absorption coefficient (a_ph(λ)) and particulate backscattering coefficient (b_bp(λ)) from remote-sensing reflectance (R_rs(λ)) data at 412, 443, 490, 531, and 555 nm. The samples from the SCS are further separated into oligotrophic and mesotrophic water types for the comparison of the SAAs. Several findings are made: First, the values of a_nw(λ) derived from the two SAAs deliver similar performance, with R² values ranging from 0.74 to 0.85 and 0.74 to 0.87, implying absolute percent error differences (APDs) from 37.93% to 74.88% and from 32.32% to 71.75% for the QAA and GIOP-DC, respectively. The QAA shows a value of R² between 0.64 and 0.91 and APDs between 43.57% to 83.53%, while the GIOP-DC yields R² between 0.76 to 0.89 and APDs between 44.65% to 79.46% when estimating a_ph(λ). The values of b_bp(λ) derived from the QAA are closer to the in-situ b_bp(λ) values, as indicated by the low values of the normalized centered root-mean-square deviation and normalized standard deviation, which are close to one. Second, a regionally tuned estimation of a_ph(λ) is proposed and recommended for the SCS. This consistency check of inherent optical properties products from SAAs can serve as reference for algorithm selection for further applications, including primary production, carbon, and biogeochemical models of the SCS, and can provide guidance for improving a_ph(λ) estimation.

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.