{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:40:59Z","timestamp":1781052059742,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of the People\u2019s Republic of China","award":["2019YFE0125000"],"award-info":[{"award-number":["2019YFE0125000"]}]},{"name":"National Nature Science Foundation of China -Shandong Joint Fund","award":["U1906215"],"award-info":[{"award-number":["U1906215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Subsurface chlorophyll maxima (SCMs), commonly occurring beneath the surface mixed layer in coastal seas and open oceans, account for main changes in depth-integrated primary production and hence significantly contribute to the global carbon cycle. To fill the gap of previous methods (in situ measurement, remote sensing, and the extrapolating function based on surface-ocean data) for obtaining SCM characteristics (intensity, depth, and thickness), we developed an improved deep neural network (IDNN) model using a Gaussian radial basis activation function to retrieve the vertical profile of chlorophyll a concentration (Chl a) and associated SCM characteristics from surface-ocean data. The annually averaged SCM depth was further incorporated into the bias term and the Gaussian activation function to improve the estimation accuracy of the IDNN model. Based on the Biogeochemical-Argo (BGC-Argo) data acquired for three regions in the northwestern Pacific Ocean, vertical Chl a profiles produced by our improved DNN model using sea surface Chl a and sea surface temperature (SST) were in good agreement with the observations, especially in regions with low surface Chl a. Compared to other neural-network-based models with one hidden layer and a sigmoid activation function, the IDNN model retrieved vertical Chl a profiles well in more eutrophic subpolar regions. Furthermore, the application of the IDNN model to infer vertical Chl a profiles from remote-sensing information was validated in the northwestern Pacific Ocean.<\/jats:p>","DOI":"10.3390\/rs14030632","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T01:43:27Z","timestamp":1643420607000},"page":"632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5578-2391","authenticated-orcid":false,"given":"Jianqiang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Gong","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China"},{"name":"Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4832-8625","authenticated-orcid":false,"given":"Xinyu","family":"Guo","sequence":"additional","affiliation":[{"name":"Center for Marine Environmental Study, Ehime University, 2-5 Bunkyo-cho, Matsuyama 790-8577, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9940-1204","authenticated-orcid":false,"given":"Xiaogang","family":"Xing","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiwang","family":"Gao","sequence":"additional","affiliation":[{"name":"Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China"},{"name":"Key Laboratory of Marine Environment and Ecology, Ministry of Education of China, Ocean University of China, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2026-0355","authenticated-orcid":false,"given":"Xiang","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jungblut, S., Liebich, V., and Bode, M. (2018). Phytoplankton Responses to Marine Climate Change\u2014An Introduction. Proceedings of the 2017 Conference for YOUng MARine RESearchers in Kiel, Germany. YOUMARES 8\u2013Oceans Across Boundaries: Learning from Each Other, Springer.","DOI":"10.1007\/978-3-319-93284-2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.pocean.2007.09.002","article-title":"Beneath the surface: Characteristics of oceanic ecosystems under weak mixing conditions\u2014A theoretical investigation","volume":"75","author":"Beckmann","year":"2007","journal-title":"Prog. Oceanogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10533-013-9831-7","article-title":"The contribution of the deep chlorophyll maximum to primary production in a seasonally stratified shelf sea, the North Sea","volume":"113","author":"Fernand","year":"2013","journal-title":"Biogeochemistry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.5194\/bg-16-1321-2019","article-title":"Bio-optical characterization of subsurface chlorophyll maxima in the Mediterranean Sea from a Biogeochemical-Argo float database","volume":"16","author":"Barbieux","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"537332","DOI":"10.3389\/feart.2020.537332","article-title":"The Impact of Eddies on Nutrient Supply, Diatom Biomass and Carbon Export in the Northern South China Sea","volume":"8","author":"Shih","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Silsbe, G.M., and Malkin, S.Y. (2016). Where Light and Nutrients Collide: The Global Distribution and Activity of Subsurface Chlorophyll Maximum layers, Springer International Publishing.","DOI":"10.1007\/978-3-319-30259-1_12"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1146\/annurev-marine-010213-135111","article-title":"Subsurface chlorophyll maximum layers: Enduring enigma or mystery solved?","volume":"7","author":"Cullen","year":"2015","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s10872-005-0066-7","article-title":"Estimating chlorophyll a vertical profiles from satellite data and the implication for primary production in the Kuroshio front of the East China Sea","volume":"61","author":"Siswanto","year":"2005","journal-title":"J. Oceanogr."},{"key":"ref_9","first-page":"386","article-title":"Subsurface chlorophyll maximum in the Pacific ocean. Limnol","volume":"14","author":"Anderson","year":"1969","journal-title":"Oceanogr"},{"key":"ref_10","first-page":"41","article-title":"Deep maxima of photosynthetic chlorophyll in the Pacific Ocean","volume":"71","author":"Venrick","year":"1973","journal-title":"Fish. Bull."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3354\/meps092289","article-title":"Variability of deep chlorophyll maximum characteristics in the Northwestern Mediterranean","volume":"92","author":"Estrada","year":"1993","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_12","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. Oceans"},{"key":"ref_13","unstructured":"Riley, G.A., Stommel, H., and Bumpus, D.F. (1949). Quantitative ecology of the plankton of the western north Atlantic. Bull. Bingham Oceanogr. Collection Peabody Museum of Natural History, Yale University."},{"key":"ref_14","first-page":"211","article-title":"A study of production in the Gulf of Mexico","volume":"22","author":"Steele","year":"1964","journal-title":"J. Mar. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1017\/S0025315400013266","article-title":"The vertical distribution of chlorophyll","volume":"39","author":"Steele","year":"1960","journal-title":"J. Mar. Biol. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/BF01313438","article-title":"Subsurface chlorophyll maximum in the tropical and subtropical western pacific ocean: Vertical profiles of phytoplankton biomass and its relationship with chlorophyll a and particulate organic carbon","volume":"107","author":"Furuya","year":"1990","journal-title":"Mar. Biol."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/0198-0149(88)90064-7","article-title":"Ocean primary production and available light: Further algorithms for remote sensing","volume":"35","author":"Platt","year":"1988","journal-title":"Deep. Sea Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005JC003207","article-title":"Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll","volume":"111","author":"Uitz","year":"2006","journal-title":"J. Geophys. Res. Ocean"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.pocean.2003.07.003","article-title":"A dynamic quantitative approach for predicting the shape of phytoplankton profiles in the ocean","volume":"59","author":"Richardson","year":"2003","journal-title":"Prog. Oceanogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1016\/j.csr.2009.09.003","article-title":"Deriving depths of deep chlorophyll maximum and water inherent optical properties: A regional model","volume":"29","author":"Xiu","year":"2009","journal-title":"Cont. Shelf Res."},{"key":"ref_22","first-page":"63","article-title":"Chlorophyll profile estimation in ocean waters by a set of artificial neural networks","volume":"22","author":"Chalhoub","year":"2015","journal-title":"Comput. Assist. Methods Eng. Sci."},{"key":"ref_23","first-page":"1","article-title":"Vertical distribution of chlorophyll a based on neural network International","volume":"2","author":"Osawa","year":"2005","journal-title":"J. Remote Sens. Earth Sci."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s00521-015-1908-x","article-title":"Multiplicative neuron model artificial neural network based on Gaussian activation function","volume":"27","author":"Gundogdu","year":"2016","journal-title":"Neural. Comput. Applic."},{"key":"ref_26","first-page":"310","article-title":"Activation functions in neural networks","volume":"4","author":"Sharma","year":"2020","journal-title":"Int. J. Eng. Appl. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","article-title":"A survey of deep neural network architectures and their applications","volume":"234","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Das, H.S., and Roy, P. (2019). A deep dive into deep learning techniques for solving spoken language identification problems. Intelligent Speech Signal Processing, Elsevier.","DOI":"10.1016\/B978-0-12-818130-0.00005-2"},{"key":"ref_29","unstructured":"D\u2019Ortenzio, F., Claustre, H., Testor, P., Coatanoan, C., Tedetti, M., Guinet, C., Poteau, A., Prieur, L., Lefevre, D., and Bourrin, F. (2021, December 01). White Book on Oceanic Autonomous Platforms for Biogeochemical Studies: Instrumentation and Measure (PABIM). Available online: https:\/\/doi.org\/10.13140\/RG.2.1.3706.5763."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1029\/JC088iC04p02565","article-title":"Phytoplankton and thermal structure in the upper ocean: Consequences of nonuniformity in chlorophyll profile","volume":"88","author":"Lewis","year":"1983","journal-title":"J. Geophys. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"905","DOI":"10.5194\/bg-12-905-2015","article-title":"Steady-state solutions for subsurface chlorophyll maximum in stratified water columns with a bell-shaped vertical profile of chlorophyll","volume":"12","author":"Gong","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3779","DOI":"10.1029\/2018JC014880","article-title":"Temporal and vertical variations of particulate and dissolved optical properties in the South China Sea","volume":"124","author":"Xing","year":"2019","journal-title":"J. Geophys. Oceans"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kirkland, E.J. (2010). Bilinear Interpolation. Advanced Computing in Electron Microscopy, Springer.","DOI":"10.1007\/978-1-4419-6533-2"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.5194\/bg-8-2391-2011","article-title":"From the shape of the vertical profile of in vivo fluores-cence to Chlorophyll-a concentration","volume":"8","author":"Mignot","year":"2011","journal-title":"Biogeosciences"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/632\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:09:45Z","timestamp":1760134185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/632"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,28]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030632"],"URL":"https:\/\/doi.org\/10.3390\/rs14030632","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,28]]}}}