{"refrec":{"BRefID":362778,"RR":"<b>Bellacicco, M.; Pitarch, J.; Organelli, E.; Martinez-Vicente, V.; Volpe, G.; Marullo, S.</b> (2020). Improving the retrieval of carbon-based phytoplankton biomass from satellite ocean colour observations. <i>Remote Sens. 12(21)</i>: 3640. <a href=\"https://dx.doi.org/10.3390/rs12213640\" target=\"_blank\">https://dx.doi.org/10.3390/rs12213640</a>","BEntID":360496,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Remote Sens. 12(21)</i>: 3640. <a href=\"https://dx.doi.org/10.3390/rs12213640\" target=\"_blank\">https://dx.doi.org/10.3390/rs12213640</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Bellacicco, M.; Pitarch, J.; Organelli, E.; Martinez-Vicente, V.; Volpe, G.; Marullo, S.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Bellacicco, M. <i>et al.</i>","Englishabstract":"<div class=\"html-p\">Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine ecosystem. To observe phytoplankton from space, chlorophyll-<span class=\"html-italic\">a</span> concentration (Chl) has been widely used as a proxy of algal biomass, although it can be impacted by physiology. Therefore, there has been an increasing focus towards estimating phytoplankton biomass in units of carbon (C<sub>phyto</sub>). Here, we developed an algorithm to quantify C<sub>phyto</sub> from space-based observations that accounts for the spatio-temporal variations of the backscattering coefficient associated with the fraction of detrital particles that do not covary with Chl. The main findings are: (i) a spatial and temporal variation of the detritus component must be accounted for in the C<sub>phyto</sub> algorithm; (ii) the refined C<sub>phyto</sub> algorithm performs better (relative bias of 23.7%) than any previously existing model; and (iii) our algorithm shows the lowest error in C<sub>phyto</sub> across areas where picophytoplankton dominates (relative bias of 14%). In other areas, it is currently not possible to accurately assess the performance of the refined algorithm due to the paucity of in situ carbon data associated with nano- and micro-phytoplankton size classes.</div>","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Improving the retrieval of carbon-based phytoplankton biomass from satellite ocean colour observations","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-11 01:32:59.807828","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"phytoplankton carbon; optical backscattering; non-algal particles; ocean colour observations; QAA algorithm; ESA OC-CCI","OtherDescriptors":null,"Notes":null,"AnaPub":2020,"MonPub":null,"DateUpdate":"2023-03-28","DateCreate":"2023-03-27","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000589271100001","VABBcode":null,"OpenAcc":1,"DOI":"10.3390/rs12213640"},"refs":null,"anarec":{"AnaID":362778,"PubliDate":2020,"Pagination":"3640","XtraPublOfAnaID":null,"ISBN":null,"Volume":"12","Issue":"21","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":221386,"SerRR":"Remote Sensing. 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