{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:05:37Z","timestamp":1775365537359,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,27]],"date-time":"2018-03-27T00:00:00Z","timestamp":1522108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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-viewing Wide 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(\u03bb)) were also measured to help understand PSC\u2019s 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<\/jats:p>","DOI":"10.3390\/rs10040526","type":"journal-article","created":{"date-parts":[[2018,3,27]],"date-time":"2018-03-27T12:17:24Z","timestamp":1522153044000},"page":"526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea"],"prefix":"10.3390","volume":"10","author":[{"given":"Shuibo","family":"Hu","sequence":"first","affiliation":[{"name":"Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Wen","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"given":"Guifen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]},{"given":"Wenxi","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"given":"Zhantang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9018-985X","authenticated-orcid":false,"given":"Huizeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2275-6530","authenticated-orcid":false,"given":"Guofeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Wenjing","family":"Zhao","sequence":"additional","affiliation":[{"name":"South China Institute of Environmental Sciences, the Ministry of Environmental Protection of RPC, Guanzhou 510535, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1126\/science.281.5374.200","article-title":"Biogeochemical controls and feedbacks on ocean primary production","volume":"281","author":"Falkowski","year":"1998","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1126\/science.281.5374.237","article-title":"Primary production of the biosphere: Integrating terrestrial and oceanic components","volume":"281","author":"Field","year":"1998","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/2013GB004743","article-title":"Global assessment of ocean carbon export by combining satellite observations and food-web models","volume":"28","author":"Siegel","year":"2014","journal-title":"Glob. 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