{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:17:46Z","timestamp":1775841466506,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41776045"],"award-info":[{"award-number":["41776045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Simons Foundation grant Computational Biogeochemical Modeling of Marine Ecosystems","award":["549947"],"award-info":[{"award-number":["549947"]}]},{"name":"Fundamental Research Funds for the Central Universities at Hohai University","award":["2017B06714, B200201013"],"award-info":[{"award-number":["2017B06714, B200201013"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK 20200517"],"award-info":[{"award-number":["BK 20200517"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) &lt; 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPDCV &lt; 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.<\/jats:p>","DOI":"10.3390\/rs13245112","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T21:32:40Z","timestamp":1639690360000},"page":"5112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9638-2549","authenticated-orcid":false,"given":"Yinxue","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China"},{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5230-7384","authenticated-orcid":false,"given":"Guifen","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China"},{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]},{"given":"Shubha","family":"Sathyendranath","sequence":"additional","affiliation":[{"name":"National Centre for Earth Observation, Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK"}]},{"given":"Wenlong","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China"},{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]},{"given":"Yizhe","family":"Xiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China"},{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]},{"given":"Long","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China"},{"name":"College of Oceanography, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1111\/j.1365-2486.2005.1004.x","article-title":"Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models","volume":"11","author":"Harrison","year":"2005","journal-title":"Glob. 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