Comparison of satellite-derived phytoplankton size classes using in-situ measurements in the South China Sea

Shuibo Hu; Wen Zhou; Guifen Wang; Wenxi Cao; Zhantang Xu; Huizeng Liu; Guofeng Wu; Wenjing Zhao

doi:10.3390/rs10040526

Comparison of satellite-derived phytoplankton size classes using in-situ measurements in the South China Sea

Shuibo Hu, Wen Zhou^*, Guifen Wang, Wenxi Cao, Zhantang Xu, Huizeng Liu, Guofeng Wu, Wenjing Zhao

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › peer-review

17 Citations (Scopus)

Abstract

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-viewingWide 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.

Original language	English
Article number	526
Number of pages	17
Journal	Remote Sensing
Volume	10
Issue number	4
Early online date	27 Mar 2018
DOIs	https://doi.org/10.3390/rs10040526
Publication status	Published - Apr 2018

User-Defined Keywords

Comparison
Ocean colour
Phytoplankton size classes (PSCs)
Remote sensing
South China Sea

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/rs10040526

Cite this

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title = "Comparison of satellite-derived phytoplankton size classes using in-situ measurements in the South China Sea",

abstract = "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-viewingWide 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 (aph(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.",

keywords = "Comparison, Ocean colour, Phytoplankton size classes (PSCs), Remote sensing, South China Sea",

author = "Shuibo Hu and Wen Zhou and Guifen Wang and Wenxi Cao and Zhantang Xu and Huizeng Liu and Guofeng Wu and Wenjing Zhao",

note = "This work was funded by National Natural Science Foundation of China grants (Grant Nos. 41606199, 41506202, 41576030, 41776045 and 41776044), The General Financial Grant from the China Postdoctoral Science Foundation (No. 2016M592521), the Open Project Program of State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China(Project Nos. LTO1611, LTOZZ1602), Science and Technology Planning Project of Guangzhou City, China (201504010034), Youth Foundation Project of Guangdong Natural Science Foundation of China (No. 2014A030310287). We are indebted to the NASA Ocean Biology Processing Group (OBPG) who distributed the MODIS, SeaWiFS and MERIS data. Publisher Copyright: {\textcopyright} 2018 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Hu, Shuibo

AU - Zhou, Wen

AU - Wang, Guifen

AU - Cao, Wenxi

AU - Xu, Zhantang

AU - Liu, Huizeng

AU - Wu, Guofeng

AU - Zhao, Wenjing

N1 - This work was funded by National Natural Science Foundation of China grants (Grant Nos. 41606199, 41506202, 41576030, 41776045 and 41776044), The General Financial Grant from the China Postdoctoral Science Foundation (No. 2016M592521), the Open Project Program of State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China(Project Nos. LTO1611, LTOZZ1602), Science and Technology Planning Project of Guangzhou City, China (201504010034), Youth Foundation Project of Guangdong Natural Science Foundation of China (No. 2014A030310287). We are indebted to the NASA Ocean Biology Processing Group (OBPG) who distributed the MODIS, SeaWiFS and MERIS data. Publisher Copyright: © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2018/4

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N2 - 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-viewingWide 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 (aph(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.

AB - 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-viewingWide 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 (aph(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.

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DO - 10.3390/rs10040526

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JO - Remote Sensing

JF - Remote Sensing

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ER -

Comparison of satellite-derived phytoplankton size classes using in-situ measurements in the South China Sea

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