In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

Sun, Xuerong; Shen, Fang; Liu, Dongyan; Bellerby, R. G. J.; Liu, Yangyang; Tang, Rugang

doi:10.1029/2017jc013651

Cited by 22 publications

(16 citation statements)

References 86 publications

(134 reference statements)

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“…A significant relationship ( r = 0.825, p < 0.001) between OC‐CCI chlorophyll‐a concentrations and independent in situ measurements was observed in the study (Figure a), though a slight overestimation in satellite‐derived values was evident, probably due to difficulties in remotely sensing chlorophyll‐a data in optically complex waters (IOCCG, ). However, statistics in the results showed that the OC‐CCI chlorophyll‐a concentration in the BS and YS was comparable with the performances of other chlorophyll‐a algorithms in the same study area (Cui et al, ; He et al, ; Sun et al, , ), providing confidences in the use of OC‐CCI chlorophyll‐a concentration data in the study. As for SST, the result of validation in the study showed high accuracy ( r = 0.971, p < 0.001, N = 23), and in recent years, OISST data sets have been applied extensively in various oceanic regions (Brewin et al, ; Friedland et al, ; González Taboada & Anadón, ; Liu & Wang, ), suggesting a maturity for long timescale research.…”

Section: Discussionsupporting

confidence: 61%

“…Chlorophyll‐a and seven diagnostic pigments concentrations (i.e., fucoxanthin, peridinin, 19‐hex‐fucoxanthin, alloxanthin, 19‐but‐fucoxanthin, chlorophyll‐b, and zeaxanthin) were quantified. Size‐fractionated concentrations and percentages were calculated using diagnostic pigment analysis (Brewin et al, ; Hirata et al, ; Uitz et al, ; Vidussi et al, ) with some adjustments according to the study area, where chlorophyll‐b was regarded as a biomarker of nanoplankton, as explained in Sun et al (). Fractions of each PSCs to total chlorophyll‐a concentration can be inferred as

C_{E} = \sum_{i = 1}^{7} W_{i} P_{i},

F_{m} = \frac{\sum_{i = 1}^{2} W_{i} P_{i}}{C_{E}},

F_{n} = ({, \begin{array}{c} \frac{12.5 C_{HPLC} W_{3} P_{3} + \sum_{i = 4}^{6} W_{i} P_{i}}{C_{E}} & , C_{HPLC} < 0.08 italicmg / m^{3} \\ \frac{\sum_{i = 3}^{6} W_{i} P_{i}}{C_{E}} & , C_{HPLC} > 0.08 italicmg / m^{3} \end{array}),

F_{p} = ({, \begin{array}{c} \frac{((), - 12.5 C_{HPLC} + 1) W_{3} P_{3} + W_{7} P_{7}}{C_{E}} & , C_{HPLC} < 0.08 italicmg / m^{3} \\ \frac{W_{7} P_{7}}{C_{E}} & , C_{} \end{array})

…”

Section: Methodsmentioning

confidence: 99%

“…Based on the in situ parameterization data set in this study and method in Sun et al (), an SST‐independent three‐component model with fixed parameters (Brewin et al, , ) was reparametrized, of which

C_{n, p}^{m}

C_{p}^{m}

, D n,p , and D p were 0.478 mg/m 3 , 0.086 mg/m 3 , 0.998, and 0.129, respectively. In comparison, the performance for all size classes improved when using SST‐dependent three‐component model (Figure ).…”

Section: Methodsmentioning

confidence: 99%

“…Each approach has its own advantages and limitations (Mouw et al, ). Many of these approaches have been designed for global ocean applications (Brewin et al, ; Bricaud et al, ), and some regional studies have also been conducted (Brotas et al, ; Lamont et al, ; Sun et al, , ).…”

Section: Introductionmentioning

confidence: 99%

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Twenty‐Year Variations in Satellite‐Derived Chlorophyll‐a and Phytoplankton Size in the Bohai Sea and Yellow Sea

et al. 2019

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Phytoplankton cell size is a useful ecological indicator for evaluating the response of phytoplankton community structure to environmental changes. Ocean-color remote observations and algorithms have allowed us to estimate phytoplankton size classes (PSCs) at decadal scale, helping us to understand their trends under ocean warming. Here a large data set of pigments, derived through high performance liquid chromatography, was collected in the Bohai Sea (BS) and Yellow Sea (YS) between 2014 and 2016. The data set was used to reparametrize the sea surface temperature (SST)-dependent threecomponent model of Brewin et al. (2017) to the region. The model was validated using independent in situ data set and subsequently applied to satellite chlorophyll-a data from Ocean Colour Climate Change Initiative, spanning from 1997 to 2016, to derive percentages of three PSCs to total chlorophyll-a. Monthlyaveraged PSCs exhibited spatial-temporal variations in the study area, linked to topography, temperature, solar radiation, currents, and monsoonal winds. In the surface central south Yellow Sea (SYS), influenced by bottom Yellow Sea Cold Water Mass, tight relationships between PSCs and environmental factors were observed, where high SST, high sea level anomaly, low mixed-layer depth, and low wind speed resulted in higher proportions of nanoplankton and picoplankton from June to October. Significant interannual anomlies in PSCs were found associated with El Niño events in the central SYS, related to anomalies in SST. The refined model characterized 20-year variations in chlorophyll-a concentration and PSCs in complicated optical, hydrodynamic, and biogeochemical environments in the BS and YS.Plain Language Summary Phytoplankton are the fundamental component of the marine ecosystem, and the size structure of phytoplankton influences many processes in phytoplankton biology, marine ecology, and marine biogeochemistry. Phytoplankton can be divided into three phytoplankton size classes (PSCs): microplankton (>20 μm), nanoplankton (2-20 μm), and picoplankton (<2 μm). The Bohai Sea (BS) and Yellow Sea (YS) are shallow marginal seas in the northwest Pacific Ocean, strongly impacted by large river plumes, ocean processes, and seasonal monsoons, supporting high primary and fishery productivity. Using a 20-year time series satellite ocean color data from 1997 to 2016, and a SSTdependent model that links chlorophyll-a concentration to the size structure of phytoplankton, we observe spatial and temporal variations of PSCs in the BS and YS and tight correlations between the size structure and physical variables in the central south Yellow Sea. Interannual variations in the PSCs are coupled with changes of sea surface temperature in El Niño events. Our results demonstrate that variations in the phytoplankton size structure are coupled with changes in climate variability, with implications for how the regional ecosystem may change with predicted changes in climate.

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Section: Discussionsupporting

confidence: 61%

C_{E} = \sum_{i = 1}^{7} W_{i} P_{i},

F_{m} = \frac{\sum_{i = 1}^{2} W_{i} P_{i}}{C_{E}},

F_{n} = ({, \begin{array}{c} \frac{12.5 C_{HPLC} W_{3} P_{3} + \sum_{i = 4}^{6} W_{i} P_{i}}{C_{E}} & , C_{HPLC} < 0.08 italicmg / m^{3} \\ \frac{\sum_{i = 3}^{6} W_{i} P_{i}}{C_{E}} & , C_{HPLC} > 0.08 italicmg / m^{3} \end{array}),

F_{p} = ({, \begin{array}{c} \frac{((), - 12.5 C_{HPLC} + 1) W_{3} P_{3} + W_{7} P_{7}}{C_{E}} & , C_{HPLC} < 0.08 italicmg / m^{3} \\ \frac{W_{7} P_{7}}{C_{E}} & , C_{} \end{array})

…”

Section: Methodsmentioning

confidence: 99%

C_{n, p}^{m}

C_{p}^{m}

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Twenty‐Year Variations in Satellite‐Derived Chlorophyll‐a and Phytoplankton Size in the Bohai Sea and Yellow Sea

et al. 2019

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“…In situ chlorophyll fluorescence detected by the fluorometer sensors has been widely used as a proxy to estimate phytoplankton biomass (i.e., chlorophyll a concentration; e.g., Sauzède et al, ), size‐based community structure (Brewin et al, ; Sun et al, ), and primary productivity (Falkowski & Kolber, ). A good correlation between the chlorophyll fluorescence and chlorophyll a concentration extracted from suspended particles was often reported in the ECS (Gong et al, ; Liu et al, ; Zhou et al, ).…”

Section: Discussionmentioning

confidence: 99%

Impact of Hydrological Conditions on the Biogeochemical Dynamics of Suspended Particulate Organic Matter in the Upper Mixed Layer of the Southern East China Sea

et al. 2019

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We investigate suspended particles collected from the upper mixed layer in the inner shelf of the southern East China Sea during autumn 2013 for carbon and nitrogen (POC and PN) contents and their isotope compositions (δ13C and δ15N) along with hydrographic parameters to understand the sources and dynamics of particulate organic matter (POM) in the study area. Results indicated that the extensive hydrological processes affect the biogeochemical composition of suspended POM, as revealed by the horizontally mixing POM and the spatial variation of δ15N and molar C/N ratio. Low C/N (2.4–6.5) and a weak correlation between POC and in situ chlorophyll fluorescence suggested that POM is dominated by the recently formed and well‐preserved planktonic OM. By fitting the linear correlation between δ13C and POC data with a photosynthetic fractionation model, we further disentangled dynamic controls of phytoplankton production and species diversity on δ13C variability (−24.3 to −21.3‰), emphasizing the constant effect of productivity‐derived POC on δ13C (0.02‰ per μg/L). The δ15N variability (2.3–7.4‰) is largely controlled by the mixing of isotopically different nitrogen sources, in which the importance of biological nitrogen fixation is unfolded based on the small δ15N and the negative correlation between δ15N of POM and seawater temperature. This implies that Kuroshio‐induced biological N fixation plays an important role in supporting the marine production in the East China Sea. These hydrologically driven δ13C and δ15N changes of marine productivity‐derived POM suggest that internal biophysical dynamics rather than terrestrial versus marine OM mixing largely control the C and N compositional variability in the shelf seas.

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Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans

Sun

Shen

Brewin

et al. 2022

Limnology & Oceanography

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Phytoplankton group composition is complex and highly variable in coastal waters. Given that different taxonomic groups have different pigment signatures, which in turn impact the light absorption spectra of phytoplankton, the absorption spectral-based approach has the potential for distinguishing phytoplankton groups. Using a large dataset of in situ surface observations of concurrent HPLC (high-performance liquid chromatography) pigments and phytoplankton absorption spectra collected from 2015 to 2018 in Chinese coastal oceans, in situ phytoplankton group composition was obtained from chemotaxonomic analysis (CHEMTAX). By using the linear additive principle on phytoplankton absorption spectra and CHEMTAX results, the chlorophyll-specific absorption spectra of eight phytoplankton groups were reconstructed, including prasinophytes, dinoflagellates, cryptophytes, chlorophytes, cyanobacteria, diatoms, chrysophytes, and prymnesiophytes. These chlorophyllspecific absorption spectra were subsequently used as inputs to a spectral-based inversion model for estimating phytoplankton group composition from the phytoplankton absorption coefficient. The optimal band selection and initial guesses of the phytoplankton group composition, derived from correlation and HCA (hierarchical cluster analysis) analyses, were included in the model inversion to improve the accuracy of retrievals. The performance of the proposed model was validated using an independent dataset, showing accurate estimates of chlorophyll a (Chl a) concentrations for seven phytoplankton groups (0.371 ≤ r ≤ 0.721, p < 0.05), apart from chrysophytes. Our results suggest that the absorption spectral-based approach is able to discriminate phytoplankton group composition quantitatively, which has implications for retrieving Chl a concentrations of phytoplankton groups from hyperspectral platforms and satellites.

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In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

Cited by 22 publications

References 86 publications

Twenty‐Year Variations in Satellite‐Derived Chlorophyll‐a and Phytoplankton Size in the Bohai Sea and Yellow Sea

Twenty‐Year Variations in Satellite‐Derived Chlorophyll‐a and Phytoplankton Size in the Bohai Sea and Yellow Sea

Impact of Hydrological Conditions on the Biogeochemical Dynamics of Suspended Particulate Organic Matter in the Upper Mixed Layer of the Southern East China Sea

Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans

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