{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:52:12Z","timestamp":1774965132053,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-11-IDEX-0004-17-EURE-0006"],"award-info":[{"award-number":["ANR-11-IDEX-0004-17-EURE-0006"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Observing the vertical dynamic of phytoplankton in the water column is essential to understand the evolution of the ocean primary productivity under climate change and the efficiency of the CO2 biological pump. This is usually made through in-situ measurements. In this paper, we propose a machine learning methodology to infer the vertical distribution of phytoplankton pigments from surface satellite observations, allowing their global estimation with a high spatial and temporal resolution. After imputing missing values through iterative completion Self-Organizing Maps, smoothing and reducing the vertical distributions through principal component analysis, we used a Self-Organizing Map to cluster the reduced profiles with satellite observations. These referent vector clusters were then used to invert the vertical profiles of phytoplankton pigments. The methodology was trained and validated on the MAREDAT dataset and tested on the Tara Oceans dataset. The different regression coefficients R2 between observed and estimated vertical profiles of pigment concentration are, on average, greater than 0.7. We could expect to monitor the vertical distribution of phytoplankton types in the global ocean.<\/jats:p>","DOI":"10.3390\/rs13081445","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T21:27:44Z","timestamp":1617917264000},"page":"1445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8206-9006","authenticated-orcid":false,"given":"Agathe","family":"Puissant","sequence":"first","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anographie et du Climat Exp\u00e9rimentations et Approches Num\u00e9riques (LOCEAN), Sorbonne Universit\u00e9, CNRS, IRD, MNHN, 75005 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6454-1645","authenticated-orcid":false,"given":"Roy","family":"El Hourany","sequence":"additional","affiliation":[{"name":"Institut de Biologie de l\u2019\u00c9cole Normale Sup\u00e9rieure (IBENS), \u00c9cole Normale Sup\u00e9rieure, CNRS, INSERM, PSL Universit\u00e9, 75005 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anastase Alexandre","family":"Charantonis","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anographie et du Climat Exp\u00e9rimentations et Approches Num\u00e9riques (LOCEAN), Sorbonne Universit\u00e9, CNRS, IRD, MNHN, 75005 Paris, France"},{"name":"\u00c9cole Nationale Sup\u00e9rieure d\u2019Informatique pour l\u2019Industrie et l\u2019Entreprise (ENSIIE), 91000 \u00c9vry, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Bowler","sequence":"additional","affiliation":[{"name":"Institut de Biologie de l\u2019\u00c9cole Normale Sup\u00e9rieure (IBENS), \u00c9cole Normale Sup\u00e9rieure, CNRS, INSERM, PSL Universit\u00e9, 75005 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvie","family":"Thiria","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anographie et du Climat Exp\u00e9rimentations et Approches Num\u00e9riques (LOCEAN), Sorbonne Universit\u00e9, CNRS, IRD, MNHN, 75005 Paris, France"},{"name":"Observatoire de Versailles Saint-Quentin-en-Yvelins (OVSQ), Versailles Saint-Quentin-en-Yvelines University, 78280 Guyancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.cosust.2012.05.007","article-title":"Future biological and ecosystem impacts of ocean acidification and their socioeconomic-policy implications","volume":"4","author":"Turley","year":"2012","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s11160-004-6749-0","article-title":"Effects of global climate change on marine and estuarine fishes and fisheries","volume":"14","author":"Roessig","year":"2004","journal-title":"Rev. Fish Biol. Fish."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1111\/j.1461-0248.2005.00871.x","article-title":"The impacts of climate change in coastal marine systems","volume":"9","author":"Harley","year":"2006","journal-title":"Ecol. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.jmarsys.2013.04.014","article-title":"Consequences of a future climatic scenario for the anchovy fishery in the Alboran Sea (SW Mediterranean): A modeling study","volume":"135","author":"Navarro","year":"2014","journal-title":"J. Mar. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5921","DOI":"10.1002\/2014JC010158","article-title":"Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models","volume":"119","author":"Gregg","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.5194\/bg-17-3439-2020","article-title":"Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections","volume":"17","author":"Kwiatkowski","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_7","unstructured":"P\u00f6rtner, H.-O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., and Mintenbeck, K. (2019). Polar Regions. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, IPCC, WMO, UNEP."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1038\/s41579-019-0222-5","article-title":"Scientists\u2019 warning to humanity: Microorganisms and climate change","volume":"17","author":"Cavicchioli","year":"2019","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1016\/j.dsr.2005.06.015","article-title":"Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery","volume":"52","author":"Alvain","year":"2005","journal-title":"Deep Sea Res. Part I Oceanogr. Res. Pap."},{"key":"ref_10","unstructured":"Sathyendranath, S., Aiken, J., Alvain, S., Barlow, R., Bouman, H., Bracher, A., Brewin, R., Bricaud, A., Brown, C., and Ciotti, A. (2014). Phytoplankton Functional Types from Space, International Ocean-Colour Coordinating Group. (Reports of the International Ocean-Colour Coordinating Group (IOCCG), 15)."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1029\/2018JC014450","article-title":"Estimation of Secondary Phytoplankton Pigments From Satellite Observations Using Self-Organizing Maps (SOMs)","volume":"124","author":"Faour","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5827","DOI":"10.1029\/2019JC015131","article-title":"Phytoplankton diversity in the Mediterranean Sea from satellite data using self-organizing maps","volume":"124","author":"Faour","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.4319\/lo.1989.34.8.1545","article-title":"Surface pigments, algal biomass profiles, and potential production of the euphotic layer: Relationships reinvestigated in view of remote-sensing applications","volume":"34","author":"Morel","year":"1989","journal-title":"Limnol. Oceanogr."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Uitz, J., Claustre, H., Morel, A., and Hooker, S.B. (2006). Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll. J. Geophys. Res. Ocean., 111.","DOI":"10.1029\/2005JC003207"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3389\/fmars.2017.00055","article-title":"Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development","volume":"4","author":"Bracher","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2015.03.019","article-title":"Retrieving the evolution of vertical profiles of Chlorophyll-a from satellite observations using Hidden Markov Models and Self-Organizing Topological Maps","volume":"163","author":"Charantonis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"63","article-title":"Chlorophyll profile estimation in ocean waters by a set of artificial neural networks","volume":"22","author":"Cortivo","year":"2017","journal-title":"Comput. Assist. Methods Eng. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1002\/2014JC010355","article-title":"Retrieving the vertical distribution of chlorophyll a concentration and phytoplankton community composition from in situ fluorescence profiles: A method based on a neural network with potential for global-scale applications","volume":"120","author":"Claustre","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sammartino, M., Marullo, S., Santoleri, R., and Scardi, M. (2018). Modelling the vertical distribution of phytoplankton biomass in the Mediterranean Sea from satellite data: A neural network approach. Remote Sens., 10.","DOI":"10.3390\/rs10101666"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sammartino, M., Buongiorno Nardelli, B., Marullo, S., and Santoleri, R. (2020). An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations. Remote Sens., 12.","DOI":"10.3390\/rs12244123"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6581","DOI":"10.1002\/2015JC010738","article-title":"Inferring the seasonal evolution of phytoplankton groups in the Senegalo-Mauritanian upwelling region from satellite ocean-color spectral measurements","volume":"120","author":"Farikou","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4545","DOI":"10.1002\/2015JC011472","article-title":"The Sicily Channel surface circulation revisited using a neural clustering analysis of a high-resolution simulation","volume":"121","author":"Jouini","year":"2016","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/LGRS.2017.2665603","article-title":"Reconstruction of subsurface velocities from satellite observations using iterative self-organizing maps","volume":"14","author":"Chapman","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109","DOI":"10.5194\/essd-5-109-2013","article-title":"The MAREDAT global database of high performance liquid chromatography marine pigment measurements","volume":"5","author":"Peloquin","year":"2013","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2015.23","article-title":"Open science resources for the discovery and analysis of Tara Oceans data","volume":"2","author":"Pesant","year":"2015","journal-title":"Sci. Data"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19939","DOI":"10.1029\/1999JC000308","article-title":"Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter","volume":"106","author":"Vidussi","year":"2001","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1016\/j.rse.2008.03.011","article-title":"An absorption model to determine phytoplankton size classes from satellite ocean colour","volume":"112","author":"Hirata","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"311","DOI":"10.5194\/bg-8-311-2011","article-title":"Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types","volume":"8","author":"Hirata","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jeffrey, S. (1980). Algal pigment systems. Primary Productivity in the Sea, Springer.","DOI":"10.1007\/978-1-4684-3890-1_3"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"293","DOI":"10.3354\/meps035293","article-title":"Chlorophyllase distribution in ten classes of phytoplankton: A problem for chlorophyll analysis","volume":"35","author":"Jeffrey","year":"1987","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"259","DOI":"10.3354\/meps038259","article-title":"Fucoxanthin pigment markers of marine phytoplankton analysed by HPLC and HPTLC","volume":"38","author":"Wright","year":"1987","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"412","DOI":"10.4319\/lo.1985.30.2.0412","article-title":"Synechococcus spp. as likely zeaxanthin-dominant ultraphytoplankton in the North Atlantic 1","volume":"30","author":"Guillard","year":"1985","journal-title":"Limnol. Oceanogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.dsr2.2003.07.018","article-title":"Seasonal and interannual variability of ocean color and composition of phytoplankton communities in the North Atlantic, equatorial Pacific and South Pacific","volume":"51","author":"Dandonneau","year":"2004","journal-title":"Deep Sea Res. Part II Top. Stud. Oceanogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.4319\/lo.1991.36.8.1662","article-title":"Light limitation of phytoplankton biomass and macronutrient utilization in the Southern Ocean","volume":"36","author":"Mitchell","year":"1991","journal-title":"Limnol. Oceanogr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1017\/S0954102094000684","article-title":"Regional variations in bio-optical properties of the surface waters in the Southern Ocean","volume":"6","author":"Fenton","year":"1994","journal-title":"Antarct. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"15587","DOI":"10.1029\/98JC00930","article-title":"Primary production in Southern Ocean waters","volume":"103","author":"Arrigo","year":"1998","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/0198-0149(91)90094-V","article-title":"Bio-optical properties of Antarctic Peninsula waters: Differentiation from temperate ocean models","volume":"38","author":"Mitchell","year":"1991","journal-title":"Deep Sea Res. Part A Oceanogr. Res. Pap."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1016\/0198-0149(91)90093-U","article-title":"Observations of modeling of the Antartic phytoplankton crop in relation to mixing depth","volume":"38","author":"Mitchell","year":"1991","journal-title":"Deep Sea Res. Part A Oceanogr. Res. Pap."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0924-7963(92)90032-4","article-title":"Predictive bio-optical relationships for polar oceans and marginal ice zones","volume":"3","author":"Mitchell","year":"1992","journal-title":"J. Mar. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.dsr2.2003.04.002","article-title":"SeaWiFS in the southern ocean: Spatial and temporal variability in phytoplankton biomass around South Georgia","volume":"51","author":"Korb","year":"2004","journal-title":"Deep Sea Res. Part II Top. Stud. Oceanogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s00300-010-0949-y","article-title":"A phytoplankton absorption-based primary productivity model for remote sensing in the Southern Ocean","volume":"34","author":"Hirawake","year":"2011","journal-title":"Polar Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"26301","DOI":"10.1029\/1999JC000296","article-title":"Bio-optical properties and remote sensing ocean color algorithms for Antarctic Peninsula waters","volume":"105","author":"Dierssen","year":"2000","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7125","DOI":"10.1029\/1999JC000311","article-title":"A chlorophyll-dependent semianalytical reflectance model derived from field measurements of absorption and backscattering coefficients within the Southern Ocean","volume":"106","author":"Reynolds","year":"2001","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/01431160903547940","article-title":"Blending of ocean colour algorithms applied to the Southern Ocean","volume":"1","author":"Kahru","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Casey, K.S., Brandon, T.B., Cornillon, P., and Evans, R. (2010). The past, present, and future of the AVHRR Pathfinder SST program. Oceanography from Space, Springer.","DOI":"10.1007\/978-90-481-8681-5_16"},{"key":"ref_47","unstructured":"Saha, K., Zhao, X., Zhang, H., Casey, K., Zhang, D., Baker-Yeboah, S., Kilpatrick, K., Evans, R., Ryan, T., and Relph, J. (2018). AVHRR Pathfinder Version 5.3 Level 3 Collated (L3C) Global 4km Sea Surface Temperature for 1981-Present, NOAA National Centers for Environmental Information."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"281","DOI":"10.3354\/meps273281","article-title":"Nonlinear dynamics in marine-phytoplankton population systems","volume":"273","author":"Belgrano","year":"2004","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","article-title":"Essentials of the self-organizing map","volume":"37","author":"Kohonen","year":"2013","journal-title":"Neural Netw."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mwasiagi, J.I. (2011). Self Organizing Maps: Applications and Novel Algorithm Design, BoD\u2013Books on Demand.","DOI":"10.5772\/566"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4152","DOI":"10.1029\/2018JC014377","article-title":"Ocean circulation in the western Gulf of Mexico using self-organizing maps","volume":"124","author":"Enriquez","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.rse.2012.11.025","article-title":"Reconstruction of satellite chlorophyll images under heavy cloud coverage using a neural classification method","volume":"131","author":"Jouini","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1016\/j.procs.2015.05.496","article-title":"Completion of a sparse GLIDER database using multi-iterative Self-Organizing Maps (ITCOMP SOM)","volume":"51","author":"Charantonis","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_54","first-page":"1957","article-title":"Practical approaches to principal component analysis in the presence of missing values","volume":"11","author":"Ilin","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3389\/fmars.2020.00235","article-title":"High resolution water column phytoplankton composition across the Atlantic Ocean from ship-towed vertical undulating radiometry","volume":"7","author":"Bracher","year":"2020","journal-title":"Front. Mar. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.4319\/lo.1993.38.7.1420","article-title":"Temporal variability of phytoplankton community structure based on pigment analysis","volume":"38","author":"Letelier","year":"1993","journal-title":"Limnol. Oceanogr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.5194\/bg-7-1543-2010","article-title":"Plankton in the open Mediterranean Sea: A review","volume":"7","author":"Christaki","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_58","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":"Quere","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rumyantseva, A., Henson, S., Martin, A., Thompson, A.F., Damerell, G.M., Kaiser, J., and Heywood, K.J. (2019). Phytoplankton spring bloom initiation: The impact of atmospheric forcing and light in the temperate North Atlantic Ocean. Prog. Oceanogr., 178.","DOI":"10.1016\/j.pocean.2019.102202"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/lno.10011","article-title":"Physical controls of variability in N orth A tlantic phytoplankton communities","volume":"60","author":"Barton","year":"2015","journal-title":"Limnol. Oceanogr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3389\/fmars.2017.00132","article-title":"Assessing pigment-based phytoplankton community distributions in the Red Sea","volume":"4","author":"Kheireddine","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-06928-z","article-title":"Microbial planktonic communities in the Red Sea: High levels of spatial and temporal variability shaped by nutrient availability and turbulence","volume":"7","author":"Pearman","year":"2017","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1445\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:12:08Z","timestamp":1760364728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,8]]},"references-count":62,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081445"],"URL":"https:\/\/doi.org\/10.3390\/rs13081445","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,8]]}}}