Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea

Remote Sensing of Marine Phytoplankton Sizes and Groups Based on the Generalized Addictive Model (GAM)

Marine phytoplankton are the basis of the whole marine ecosystem, and different groups of phytoplankton play different roles in the biogeochemical cycle. Satellite remote sensing is widely used in the retrieval of marine phytoplankton over a wide range and long time series, but not yet for taxonomical composition. In this study, we used coincident in situ measurement data from high-performance liquid chromatography (HPLC) and remote sensing reflectance (Rrs) to investigate the empirical relationships between phytoplankton groups and satellite measurements. A nonparametric model, generalized additive model (GAM), is introduced to establish inversion models of various marine phytoplankton groups. Seven inversion models (two sizes classes among the microphytoplankton and nanophytoplankton and four groups among the diatoms, dinoflagellates, chrysophytes, and cryptophytes) are applied to the South China Sea (SCS) for 2020, and satellite images of phytoplankton sizes and groups are presented. Microphytoplankton prevails in the coastal and continental shelf, and nanophytoplankton prevails in oligotrophic oceans. Among them, the dominant contribution of microphytoplankton comes from diatoms, and nanophytoplankton comes from chrysophytes. Diatoms (nearshore) and chrysophytes (outside the continental shelf) are the dominant groups in the SCS throughout the year. Dinoflagellates only become dominant in some coastal areas, while cryptophytes rarely become dominant.

Association between the Biophysical Environment in Coastal South China Sea and Large-Scale Synoptic Circulation Patterns: The Role of the Northwest Pacific Subtropical High and Typhoons

Synoptic weather conditions can modulate short-term variations in the marine biophysical environment. However, the impact of large-scale synoptic circulation patterns (LSCPs) on variations in chlorophyll-a (chl-a) and sea surface temperature (SST) in the South China Sea (SCS) remains unclear. Using a T-mode principal component analysis method, four types of LSCP related to the Northwest Pacific subtropical high are objectively identified over the SCS for the summers of 2015–2018. Type 1 exhibits a lower chl-a concentration of <0.3 mg m−3 offshore of southern Vietnam with respect to the other three types. For Type 2, the high chl-a concentration zone (>0.3 mg m−3) along the coast of Guangdong exhibits the widest areas of coverage. The offshore chl-a bloom jet (>0.3 mg m−3) formed in southern Vietnam is the most obvious under Type 3. Under Type 4, the high chl-a concentration zone along the coast of Guangdong is the narrowest, while the chl-a concentration in the middle of the SCS is the lowest (<0.1 mg m−3). These type differences are mostly caused by the various monsoon circulations, local ocean mesoscale processes and resultant differences in localized precipitation, wind vectors, photosynthetically active radiation and SST. In particular, precipitation over land helps to transport nutrients from the land to the shore, which is conducive to the increase of chl-a. However, precipitation over ocean will dilute the upper seawater and reduce chl-a. Typhoons pump the deeper seawater with nutrients to the surface, and therefore make a positive contribution to chl-a in most offshore areas; however, they also disturb shallower water and hinder the growth of phytoplankton, making a negative contribution near the coast of Guangdong. In general, our findings will provide a better understanding of wind pump impact: the responses of marine biophysical environments to LSCPs.

Potential Associations between Low-Level Jets and Intraseasonal and Semi-Diurnal Variations in Coastal Chlorophyll—A over the Beibuwan Gulf, South China Sea

Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs on the marine biophysical environment over the Beibuwan Gulf (BBG) in summer during 2015–2019. In terms of the summer average, the sea surface wind vectors on LLJ days became stronger in the southwesterly direction relative to those on non-LLJ days, resulting in enhanced Ekman pumping (the maximum upwelling exceeds 10 × 10−6 m s−1) in most areas of the BBG, accompanied by stronger photosynthetically active radiation (increased by about 20 μmol m−2 s−1) and less precipitation (decreased by about 3 mm day−1). These LLJ-induced hydrometeorological changes led to an increase of about 0.3 °C in the nearshore sea surface temperature and an increase of 0.1–0.5 mg m−3 (decrease of 0.1–0.3 mg m−3) in the chlorophyll-a (chl-a) concentrations in nearshore (offshore) regions. Intraseasonal and diurnal changes in the incidence and intensity of LLJs potentially resulted in changes in the biophysical ocean environment in nearshore regions on intraseasonal and semi-diurnal timescales. The semi-diurnal peak and amplitude of chl-a concentrations on LLJ days increased with respect to those on non-LLJ days. Relative to the southern BBG, LLJ events exhibit greater impacts on the northern BBG, causing increases of the semi-diurnal peak and amplitude with 1.5 mg m−3 and 0.7 mg m−3, respectively. This work provides scientific evidence for understanding the potential mechanism of synoptic-scale changes in the marine ecological environment in marginal seas with frequent LLJ days.

Evaluating semi-analytical algorithms for estimating inherent optical properties in 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 an_w(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.

Factors Regulating the Relationship Between Total and Size-Fractionated Chlorophyll-a in Coastal Waters of the Red Sea

Phytoplankton biomass and size structure are recognized as key ecological indicators. With the aim to quantify the relationship between these two ecological indicators in tropical waters and understand controlling factors, we analyzed the total chlorophyll-a concentration, a measure of phytoplankton biomass, and its partitioning into three size classes of phytoplankton, using a series of observations collected at coastal sites in the central Red Sea. Over a period of 4 years, measurements of flow cytometry, size-fractionated chlorophyll-a concentration, and physical-chemical variables were collected near Thuwal in Saudi Arabia. We fitted a three-component model to the size-fractionated chlorophyll-a data to quantify the relationship between total chlorophyll and that in three size classes of phytoplankton [pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm)]. The model has an advantage over other more empirical methods in that its parameters are interpretable, expressed as the maximum chlorophyll-a concentration of small phytoplankton (pico- and combined pico-nanophytoplankton, Cpm and Cp,nm, respectively) and the fractional contribution of these two size classes to total chlorophyll-a as it tends to zero (D_p and D_p,n). Residuals between the model and the data (model minus data) were compared with a range of other environmental variables available in the dataset. Residuals in pico- and combined pico-nanophytoplankton fractions of total chlorophyll-a were significantly correlated with water temperature (positively) and picoeukaryote cell number (negatively). We conducted a running fit of the model with increasing temperature and found a negative relationship between temperature and parameters Cpm and Cp,nm and a positive relationship between temperature and parameters D_p and D_p,n. By harnessing the relative red fluorescence of the flow cytometric data, we show that picoeukaryotes, which are higher in cell number in winter (cold) than summer (warm), contain higher chlorophyll per cell than other picophytoplankton and are slightly larger in size, possibly explaining the temperature shift in model parameters, though further evidence is needed to substantiate this finding. Our results emphasize the importance of knowing the water temperature and taxonomic composition of phytoplankton within each size class when understanding their relative contribution to total chlorophyll. Furthermore, our results have implications for the development of algorithms for inferring size-fractionated chlorophyll from satellite data, and for how the partitioning of total chlorophyll into the three size classes may change in a future ocean.

Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine

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 (at-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from at-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.