Spatial and temporal variations of satellite-derived phytoplankton size classes using a three-component model bridged with temperature in Marginal Seas of the Western Pacific Ocean

Liu, Haoran; Liu, Xin; Xiao, Wei

doi:10.1016/j.pocean.2021.102511

Cited by 15 publications

(9 citation statements)

References 101 publications

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“…Models relating PSC to the Chla concentration estimated by EO are well established (e.g., Hirata et al, 2008;Brewin et al, 2010;amongst others), and used in operational EO services such as the United Kingdom NERC EO Data Analysis and AI Service (NEODAAS 1 ) and the Copernicus Marine Environment Monitoring Service (CMEMS 2 ). These models have been extensively validated with in situ data of photosynthetic pigments concentration from High Performance Liquid Chromatography (HPLC) in the water column (Brewin et al, 2012(Brewin et al, , 2017aBrotas et al, 2013;Brito et al, 2015;Lamont et al, 2018;Liu et al, 2021;amongst others).…”

Section: Introductionmentioning

confidence: 99%

Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long Transect in the Atlantic Ocean

et al. 2022

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Phytoplankton biomass, through its proxy, Chlorophyll a, has been assessed at synoptic temporal and spatial scales with satellite remote sensing (RS) for over two decades. Also, RS algorithms to monitor relative size classes abundance are widely used; however, differentiating functional types from RS, as well as the assessment of phytoplankton structure, in terms of carbon remains a challenge. Hence, the main motivation of this work it to discuss the links between size classes and phytoplankton groups, in order to foster the capability of assessing phytoplankton community structure and phytoplankton size fractionated carbon budgets. To accomplish our goal, we used data (on nutrients, photosynthetic pigments concentration and cell numbers per taxa) collected in surface samples along a transect on the Atlantic Ocean, during the 25th Atlantic Meridional Transect cruise (AMT25) between 50° N and 50° S, from nutrient-rich high latitudes to the oligotrophic gyres. We compared phytoplankton size classes from two methodological approaches: (i) using the concentration of diagnostic photosynthetic pigments, and assessing the abundance of the three size classes, micro-, nano-, and picoplankton, and (ii) identifying and enumerating phytoplankton taxa by microscopy or by flow cytometry, converting into carbon, and dividing the community into five size classes, according to their cell carbon content. The distribution of phytoplankton community in the different oceanographic regions is presented in terms of size classes, taxonomic groups and functional types, and discussed in relation to the environmental oceanographic conditions. The distribution of seven functional types along the transect showed the dominance of picoautotrophs in the Atlantic gyres and high biomass of diatoms and autotrophic dinoflagellates (ADinos) in higher northern and southern latitudes, where larger cells constituted the major component of the biomass. Total carbon ranged from 65 to 4 mg carbon m–3, at latitudes 45° S and 27° N, respectively. The pigment and cell carbon approaches gave good consistency for picoplankton and microplankton size classes, but nanoplankton size class was overestimated by the pigment-based approach. The limitation of enumerating methods to accurately resolve cells between 5 and 10 μm might be cause of this mismatch, and is highlighted as a knowledge gap. Finally, the three-component model of Brewin et al. was fitted to the Chlorophyll a (Chla) data and, for the first time, to the carbon data, to extract the biomass of three size classes of phytoplankton. The general pattern of the model fitted to the carbon data was in accordance with the fits to Chla data. The ratio of the parameter representing the asymptotic maximum biomass gave reasonable values for Carbon:Chla ratios, with an overall median of 112, but with higher values for picoplankton (170) than for combined pico-nanoplankton (36). The approach may be useful for inferring size-fractionated carbon from Earth Observation.

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Section: Introductionmentioning

confidence: 99%

Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long Transect in the Atlantic Ocean

et al. 2022

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“…The DPA is commonly used to derive Chla concentrations in PSCs (Brewin et al, 2015; Chase et al, 2020; Vidussi et al, 2001) and PTGs (Losa et al, 2017; Soppa et al, 2014; Xi et al, 2020). DPA estimates the PTG Chla concentrations through the weighted sum of diagnostic pigments (indicating the corresponding PTGs) to the TChla concentration (Brewin et al, 2015; Liu et al, 2021; Uitz et al, 2006; Vidussi et al, 2001). In the present study, seven diagnostic pigments were used to estimate the corresponding PTG Chla concentrations: TChlb (Chlor), Fuco (Diat), Perid (Dino), But (chrysophytes, Chry and pelagophytes, Pelag), Hex (Hapt), Allo (Crypt), and Zea (Cyan) (Table S2).…”

Section: Methodsmentioning

confidence: 99%

A global satellite observation of phytoplankton taxonomic groups over the past two decades

Sun

Wang

et al. 2023

Global Change Biology

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“…In particular, several recent papers have reported that the proportion of small phytoplankton is increasing, owing to warming water temperatures [9][10][11][12]. The dominant size distribution of phytoplankton plays a critical role in variations in energy efficiency and primary productivity for upper trophic marine organisms [13][14][15][16][17]. The physiological characteristics of phytoplankton are also highly related to its size; therefore, it plays a crucial role in the identification of the impacts of environmental change on marine ecosystems [17].…”

Section: Introductionmentioning

confidence: 99%

“…The dominant size distribution of phytoplankton plays a critical role in variations in energy efficiency and primary productivity for upper trophic marine organisms [13][14][15][16][17]. The physiological characteristics of phytoplankton are also highly related to its size; therefore, it plays a crucial role in the identification of the impacts of environmental change on marine ecosystems [17]. Nevertheless, there is inadequate long-term information on the community fluctuations and spatio-temporal distributions of phytoplankton according to size.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning

Kang¹,

Oh²,

Youn³

et al. 2022

JMSE

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The size of phytoplankton (a key primary producer in marine ecosystems) is known to influence the contribution of primary productivity and the upper trophic level of the food web. Therefore, it is essential to identify the dominant sizes of phytoplankton while inferring the responses of marine ecosystems to change in the marine environment. However, there are few studies on the spatio-temporal variations in the dominant sizes of phytoplankton in the littoral sea of Korea. This study utilized a deep learning model as a classification algorithm to identify the dominance of different phytoplankton sizes. To train the deep learning model, we used field measurements of turbidity, water temperature, and phytoplankton size composition (chlorophyll-a) in the littoral sea of Korea, from 2018 to 2020. The new classification algorithm from the deep learning model yielded an accuracy of 70%, indicating an improvement compared with the existing classification algorithms. The developed classification algorithm could be substituted in satellite ocean color data. This enabled us to identify spatio-temporal variation in phytoplankton size composition in the littoral sea of Korea. We consider this to be highly effective as fundamental data for identifying the spatio-temporal variation in marine ecosystems in the littoral sea of Korea.

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Spatial and temporal variations of satellite-derived phytoplankton size classes using a three-component model bridged with temperature in Marginal Seas of the Western Pacific Ocean

Cited by 15 publications

References 101 publications

Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long Transect in the Atlantic Ocean

Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long Transect in the Atlantic Ocean

A global satellite observation of phytoplankton taxonomic groups over the past two decades

Estimation of Phytoplankton Size Classes in the Littoral Sea of Korea Using a New Algorithm Based on Deep Learning

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