

FOLLOWUS
1.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2.University of Chinese Academy of Sciences, Beijing 100190, China
3.State Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming, East China Normal University, Shanghai 200241, China
dyliu@sklec.ecnu.edu.cn
Received:18 November 2025,
Online First:15 September 2026,
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WANG Yueqi,HAO Jiawen,WANG Yujue,et al.Temporal-spatial change of phytoplankton size structure from satellite observations in the Bohai and Yellow Seas of China during 2003–2024[J].Journal of Oceanology and Limnology,
WANG Yueqi,HAO Jiawen,WANG Yujue,et al.Temporal-spatial change of phytoplankton size structure from satellite observations in the Bohai and Yellow Seas of China during 2003–2024[J].Journal of Oceanology and Limnology, DOI:.
Phytoplankton size structure is a key ecological indicator for assessing marine ecosystems status and their response to environmental change; howev
er
long-term monitoring of phytoplankton size has been constrained by limitations in observational data. To address this gap
this study developed a 22-year (2003–2024) daily satellite-derived phytoplankton size class (PSC) dataset for the Bohai and Yellow seas of China (BYS) using an in-situ optimized abundance-based model. Key findings are: (1) micro-phytoplankton dominated the coastal waters all-year round
whereas offshore water exhibited seasonal alternation between pico-phytoplankton (summer) and micro-phytoplankton (other seasons); (2) a significant long-term phytoplankton size miniaturization was detected
with the contributions of micro- and nano-phytoplankton declined at a rates of -0.031% per year (non-significant
P
>
0.05) and -0.049% per year (significant
P
<
0.01)
respectively
while pico-phytoplankton increased by 0.055% per year (significant
P
<
0.01); (3) through explainable machine learning model
sea surface temperature was identified as the dominant driver of PSC variations
exhibiting highly nonlinear relationships and distinct threshold effects with shifts in phytoplankton size structure. This validated
long-term satellite PSC product provides novel insights into the environmental forcing of phytoplankton community restructuring in the BYS and offering a critical baseline for assessing ecosystem responses to climate change and anthropogenic pressure.
Agawin N S R , Duarte C M , Agustí S . 2000 . Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production . Limnology and Oceanography , 45 ( 8 ): 1891 - 1891 , https://doi.org/10.4319/lo.2000.45.3.0591 https://doi.org/10.4319/lo.2000.45.3.0591 .
Aiken J , Pradhan Y , Barlow R et al . 2009 . Phytoplankton pigments and functional types in the Atlantic Ocean: a decadal assessment, 1995-2005 . Deep Sea Research Part II: Topical Studies in Oceanography , 56 ( 15 ): 899 - 917 , https://doi.org/10.1016/j.dsr2.2008.09.017 https://doi.org/10.1016/j.dsr2.2008.09.017 .
Atlas R , Hoffman R N , Ardizzone J et al . 2011 . A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications . Bulletin of the American Meteorological Society , 92 ( 2 ): 157 - 174 , https://doi.org/10.1175/2010BAMS2946.1 https://doi.org/10.1175/2010BAMS2946.1 .
Barnett T P , Pierce D W , Schnur R . 2001 . Detection of anthropogenic climate change in the world’s oceans . Science , 292 ( 5515 ): 270 - 274 , https://doi.org/10.1126/science.1058304 https://doi.org/10.1126/science.1058304 .
Brander K , Kiørboe T . 2020 . Decreasing phytoplankton size adversely affects ocean food chains . Global Change Biology , 26 ( 10 ): 5356 - 5357 , https://doi.org/10.1111/gcb.15216 https://doi.org/10.1111/gcb.15216 .
Breiman L . 2001 . Random forests . Machine Learning , 45 ( 1 ): 5 - 32 , https://doi.org/10.1023/A:1010933404324 https://doi.org/10.1023/A:1010933404324 .
Brewin R J W , Ciavatta S , Sathyendranath S et al . 2017 . Uncertainty in ocean-color estimates of chlorophyll for phytoplankton groups. Frontiers in Marine Science , 4 : 104 , https://doi.org/10.3389/fmars.2017.00104 https://doi.org/10.3389/fmars.2017.00104 .
Brewin R J W , Hardman-Mountford N J , Lavender S J et al . 2011 . An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing . Remote Sensing of Environment , 115 ( 2 ): 325 - 339 , https://doi.org/10.1016/j.rse.2010.09.004 https://doi.org/10.1016/j.rse.2010.09.004 .
Brewin R J W , Sathyendranath S , Hirata T et al . 2010 . A three-component model of phytoplankton size class for the Atlantic Ocean . Ecological Modelling , 221 ( 11 ): 1472 - 1483 , https://doi.org/10.1016/j.ecolmodel.2010.02.014 https://doi.org/10.1016/j.ecolmodel.2010.02.014 .
Brewin R J W , Sathyendranath S , Jackson T et al . 2015 . Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class . Remote Sensing of Environment , 168 : 437 - 450 , https://doi.org/10.1016/j.rse.2015.07.004 https://doi.org/10.1016/j.rse.2015.07.004 .
Campbell J W . 1995 . The lognormal distribution as a model for bio-optical variability in the sea . Journal of Geophysical Research: Oceans , 100 ( C7 ): 13237 - 13254 , https://doi.org/10.1029/95JC00458 https://doi.org/10.1029/95JC00458 .
Chase A P , Kramer S J , Haëntjens N et al . 2020 . Evaluation of diagnostic pigments to estimate phytoplankton size classes . Limnology and Oceanography: Methods , 18 ( 10 ): 570 - 584 , https://doi.org/10.1002/lom3.10385 https://doi.org/10.1002/lom3.10385 .
Chen B Z , Landry M R , Huang B Q et al . 2012 . Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnology and Oceanography , 57 ( 2 ): 519 - 526 , https://doi.org/10.4319/lo.2012.57.2.0519 https://doi.org/10.4319/lo.2012.57.2.0519 .
de Boyer Montégut C , Madec G , Fischer A S et al . 2004 . Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology . Journal of Geophysical Research: Oceans , 109 ( C12 ): C 12003 , https://doi.org/10.1029/2004JC002378 https://doi.org/10.1029/2004JC002378 .
de Lima Oliveira A , Rudorff N , Sathyendranath S et al . 2025 . Phytoplankton size structure in a subtropical area from ocean colour and its applications. Journal of Marine Systems , 248 : 104036 , https://doi.org/10.1016/j.jmarsys.2024.104036 https://doi.org/10.1016/j.jmarsys.2024.104036 .
Devred E , Sathyendranath S , Stuart V et al . 2011 . A three component classification of phytoplankton absorption spectra: application to ocean-color data . Remote Sensing of Environment , 115 ( 9 ): 2255 - 2266 , https://doi.org/10.1016/j.rse.2011.04.025 https://doi.org/10.1016/j.rse.2011.04.025 .
Ding X , Liu J X , Liu W W et al . 2023 . Phytoplankton communities miniaturization driven by extreme weather in subtropical estuary under climate changes. Water Research , 245 : 120588 , https://doi.org/10.1016/j.watres.2023.120588 https://doi.org/10.1016/j.watres.2023.120588 .
Feurer M , Klein A , Eggensperger K et al . 2019 . Auto-sklearn: efficient and robust automated machine learning . In: Hutter F, Kotthoff L, Vanschoren J eds. Automated Machine Learning : Methods, Systems, Challenges. Springer, Cham. p . 113 - 134 , https://doi.org/10.1007/978-3-030-05318-5_6 https://doi.org/10.1007/978-3-030-05318-5_6 .
Field C B , Behrenfeld M J , Randerson J T et al . 1998 . Primary production of the biosphere: integrating terrestrial and oceanic components . Science , 281 ( 5374 ): 237 - 240 , https://doi.org/10.1126/science.281.5374.237 https://doi.org/10.1126/science.281.5374.237 .
Finkel Z V , Beardall J , Flynn K J et al . 2010 . Phytoplankton in a changing world: cell size and elemental stoichiometry . Journal of Plankton Research , 32 ( 1 ): 119 - 137 , https://doi.org/10.1093/plankt/fbp098 https://doi.org/10.1093/plankt/fbp098 .
Frouin R , Franz B A , Werdell P J . 2003 . The SeaWiFS PAR product . In: Hooker S B, Firestone E R eds. Algorithm Updates for the Fourth SeaWiFS Data Reprocessing . p. 46 - 50 , https://ntrs.nasa.gov/citations/20030102166 https://ntrs.nasa.gov/citations/20030102166 .
Fu M Z , Wang Z L , Li Y et al . 2009 . Phytoplankton biomass size structure and its regulation in the Southern Yellow Sea (China): seasonal variability . Continental Shelf Research , 29 ( 18 ): 2178 - 2194 , https://doi.org/10.1016/j.csr.2009.08.010 https://doi.org/10.1016/j.csr.2009.08.010 .
Furuya K , Hayashi M , Yabushita Y et al . 2003 . Phytoplankton dynamics in the East China Sea in spring and summer as revealed by HPLC-derived pigment signatures . Deep Sea Research Part II: Topical Studies in Oceanography , 50 ( 2 ): 367 - 387 , https://doi.org/10.1016/S0967-0645(02)00460-5 https://doi.org/10.1016/S0967-0645(02)00460-5 .
Gao Y J , Zhao J Y , Han L . 2023 . Quantifying the nonlinear relationship between block morphology and the surrounding thermal environment using random forest method. Sustainable Cities and Society , 91 : 104443 , https://doi.org/10.1016/j.scs.2023.104443 https://doi.org/10.1016/j.scs.2023.104443 .
Gholizadeh H , Robeson S M . 2016 . Revisiting empirical ocean-colour algorithms for remote estimation of chlorophyll- a content on a global scale . International Journal of Remote Sensing , 37 ( 11 ): 2682 - 2705 , https://doi.org/10.1080/01431161.2016.1183834 https://doi.org/10.1080/01431161.2016.1183834 .
Gilbert R O . 1987 . Statistical Methods for Environmental Pollution Monitoring . Van Nostrand Reinhold Co. , New York , https://doi.org/10.2307/2531935 https://doi.org/10.2307/2531935 .
Gittings J A , Brewin R J W , Raitsos D E et al . 2019 . Remotely sensing phytoplankton size structure in the Red Sea. Remote Sensing of Environment , 234 : 111387 , https://doi.org/10.1016/j.rse.2019.111387 https://doi.org/10.1016/j.rse.2019.111387 .
Guidi L , Stemmann L , Jackson G A et al . 2009 . Effects of phytoplankton community on production, size, and export of large aggregates: a world-ocean analysis . Limnology and Oceanography , 54 ( 6 ): 1951 - 1963 , https://doi.org/10.4319/lo.2009.54.6.1951 https://doi.org/10.4319/lo.2009.54.6.1951 .
Hao Q , Chai F , Xiu P et al . 2019 . Spatial and temporal variation in chlorophyll a concentration in the Eastern China Seas based on a locally modified satellite dataset . Estuarine, Coastal and Shelf Science , 220 : 220 - 231 , https://doi.org/10.1016/j.ecss.2019.01.004 https://doi.org/10.1016/j.ecss.2019.01.004 .
Hirata T , Hardman-Mountford N J , Brewin R J W et al . 2011 . Synoptic relationships between surface Chlorophyll- a and diagnostic pigments specific to phytoplankton functional types . Biogeosciences , 8 ( 2 ): 311 - 327 , https://doi.org/10.5194/bg-8-311-2011 https://doi.org/10.5194/bg-8-311-2011 .
Hirsch R M , Slack J R . 1984 . A nonparametric trend test for seasonal data with serial dependence . Water Resources Research , 20 ( 6 ): 727 - 732 , https://doi.org/10.1029/WR020i006p00727 https://doi.org/10.1029/WR020i006p00727 .
Hirsch R M , Slack J R , Smith R A . 1982 . Techniques of trend analysis for monthly water quality data . Water Resources Research , 18 ( 1 ): 107 - 121 , https://doi.org/10.3133/ofr81488 https://doi.org/10.3133/ofr81488 .
Hoegh-Guldberg O , Bruno J F . 2010 . The impact of climate change on the world’s marine ecosystems . Science , 328 ( 5985 ): 1523 - 1528 , https://doi.org/10.1126/science.1189930 https://doi.org/10.1126/science.1189930 .
IOCCG . 2014 . Phytoplankton functional types from Space . IOCCG, Dartmouth , https://epic.awi.de/id/eprint/36000/ https://epic.awi.de/id/eprint/36000/ .
Jiang Z B , Chen J F , Gao Y X et al . 2019 . Regulation of spatial changes in phytoplankton community by water column stability and nutrients in the Southern Yellow Sea . Journal of Geophysical Research: Biogeosciences , 124 ( 8 ): 2610 - 2627 , https://doi.org/10.1029/2018JG004785 https://doi.org/10.1029/2018JG004785 .
Jin J , Liu S M , Ren J L et al . 2013 . Nutrient dynamics and coupling with phytoplankton species composition during the spring blooms in the Yellow Sea . Deep Sea Research Part II: Topical Studies in Oceanography , 97 : 16 - 32 , https://doi.org/10.1016/j.dsr2.2013.05.002 https://doi.org/10.1016/j.dsr2.2013.05.002 .
Leblanc K , Quéguiner B , Diaz F et al . 2018 . Nanoplanktonic diatoms are globally overlooked but play a role in spring blooms and carbon export . Nature Communications , 9 ( 1 ): 953 , https://doi.org/10.1038/s41467-018-03376-9 https://doi.org/10.1038/s41467-018-03376-9 .
Lee D G , Oh J H , Noh K M et al . 2023 . What controls the future phytoplankton change over the Yellow and East China Seas under global warming? Frontiers in Marine Science , 10 : 1010341 , https://doi.org/10.3389/fmars.2023.1010341 https://doi.org/10.3389/fmars.2023.1010341 .
Lee K H , Jeong H J , Lee K et al . 2019 . Effects of warming and eutrophication on coastal phytoplankton production . Harmful Algae , 81 : 106 - 118 , https://doi.org/10.1016/j.hal.2018.11.017 https://doi.org/10.1016/j.hal.2018.11.017 .
Lewandowska A M , Boyce D G , Hofmann M et al . 2014 . Effects of sea surface warming on marine plankton . Ecology Letters , 17 ( 5 ): 614 - 623 , https://doi.org/10.1111/ele.12265 https://doi.org/10.1111/ele.12265 .
Lin L , Liu D Y , Wang Y Q et al . 2023 . Effect of wind on summer chlorophyll- a variability in the Yellow Sea. Frontiers in Marine Science , 9 : 1104258 , https://doi.org/10.3389/fmars.2022.1104258 https://doi.org/10.3389/fmars.2022.1104258 .
Litchman E , Klausmeier C A , Schofield O M et al . 2007 . The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level . Ecology Letters , 10 ( 12 ): 1170 - 1181 , https://doi.org/10.1111/j.1461-0248.2007.01117.x https://doi.org/10.1111/j.1461-0248.2007.01117.x .
Liu D Y , Wang Y Q . 2013 . Trends of satellite derived chlorophyll- a (1997-2011) in the Bohai and Yellow Seas, China: effects of bathymetry on seasonal and inter-annual patterns . Progress in Oceanography , 116 : 154 - 166 , https://doi.org/10.1016/j.pocean.2013.07.003 https://doi.org/10.1016/j.pocean.2013.07.003 .
Liu H R , Liu X , Xiao W P et al . 2021 . Spatial and temporal variations of satellite-derived phytoplankton size classes using a three-component m odel bridged with temperature in Marginal Seas of the Western Pacific Ocean. Progress in Oceanography , 191 : 102511 , https://doi.org/10.1016/j.pocean.2021.102511 https://doi.org/10.1016/j.pocean.2021.102511 .
Lundberg S M , Lee S I . 2017 . A unified approach to interpreting model predictions . In: Proceedings of the 31st International Conference on Neural Information Processing Systems . Curran Associates Inc. , Long Beach, USA . p. 4768 - 4777 , https://doi.org/10.48550/arXiv.1705.07874 https://doi.org/10.48550/arXiv.1705.07874
Lv T , Liu D Y , Zhou P et al . 2022 . The coastal front modulates the timing and magnitude of spring phytoplankton bloom in the Yellow Sea. Water Research , 220 : 118669 , https://doi.org/10.1016/j.watres.2022.118669 https://doi.org/10.1016/j.watres.2022.118669 .
Marañón E . 2015 . Cell size as a key determinant of phytoplankton metabolism and community structure . Annual Review of Marine Science , 7 : 241 - 264 , https://doi.org/10.1146/annurev-marine-010814-015955 https://doi.org/10.1146/annurev-marine-010814-015955 .
Marañón E , Cermeño P , Latasa M et al . 2012 . Temperature, resources, and phytoplankton size structure in the ocean . Limnology and Oceanography , 57 ( 5 ): 1266 - 1278 , https://doi.org/10.4319/lo.2012.57.5.1266 https://doi.org/10.4319/lo.2012.57.5.1266 .
Marañón E , Cermeño P , Latasa M et al . 2015 . Resource supply alone explains the variability of marine phytoplankton size structure . Limnology and Oceanography , 60 ( 5 ): 1848 - 1854 , https://doi.org/10.1002/lno.10138 https://doi.org/10.1002/lno.10138 .
Moore T S , Brown C W . 2020 . Incorporating environmental data in abundance-based algorithms for deriving phytoplankton size classes in the Atlantic Ocean. Remote Sensing of Environment , 240 : 111689 , https://doi.org/10.1016/j.rse.2020.111689 https://doi.org/10.1016/j.rse.2020.111689 .
Pálffy K , Smeti E . 2024 . Combined effect of warming, nutrients, and species pool size on the seasonal variability of phytoplankton composition: a modeling perspective . Limnology and Oceanography , 69 ( 5 ): 1056 - 1069 , https://doi.org/10.5194/os-12-379-2016 https://doi.org/10.5194/os-12-379-2016 .
Pitarch J , Volpe G , Colella S et al . 2016 . Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data . Ocean Science , 12 ( 2 ): 379 - 389 , https://doi.org/10.5194/os-12-379-2016 https://doi.org/10.5194/os-12-379-2016 .
Reynolds R W , Smith T M . 1994 . Improved global sea surface temperature analyses using optimum interpolation . Journal of Climate , 7 ( 6 ): 929 - 948 , https://doi.org/10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2. https://doi.org/10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2.
Reynolds R W , Smith T M , Liu C et al . 2007 . Daily high-resolution-blended analyses for sea surface temperature . Journal of Climate , 20 ( 22 ): 5473 - 5496 , https://doi.org/10.1175/2007JCLI1824.1 https://doi.org/10.1175/2007JCLI1824.1 .
Sahay A , Ali S M , Gupta A et al . 2017 . Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon . Remote Sensing of Environment , 198 : 286 - 296 , https://doi.org/10.1016/j.rse.2017.06.017 https://doi.org/10.1016/j.rse.2017.06.017 .
Saulquin B , Fablet R , Mangin A et al . 2013 . Detection of linear trends in multisensor time series in the presence of autocorrelated noise: application to the chlorophyll- a SeaWiFS and MERIS data sets and extrapolation to the incoming Sentinel 3-OLCI mission . Journal of Geophysical Research: Oceans , 118 ( 8 ): 3752 - 3763 , https://doi.org/10.1002/jgrc.20264 https://doi.org/10.1002/jgrc.20264 .
Seegers B N , Stumpf R P , Schaeffer B A et al . 2018 . Performance metrics for the assessment of satellite data products: an ocean color case study . Optics Express , 26 ( 6 ): 7404 - 7422 , https://doi.org/10.1364/OE.26.007404 https://doi.org/10.1364/OE.26.007404 .
Sen P K . 1968 . Estimates of the regression coefficient based on kendall’s tau . Journal of the American Statistical Association , 63 ( 324 ): 1379 - 1389 , https://doi.org/10.1080/01621459.1968.10480934 https://doi.org/10.1080/01621459.1968.10480934 .
Sieburth J M , Smetacek V , Lenz J . 1978 . Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions . Limnology and Oceanography , 23 ( 6 ): 1256 - 1263 , https://doi.org/10.4319/lo.1978.23.6.1256 https://doi.org/10.4319/lo.1978.23.6.1256 .
Smetacek V , Cloern J E . 2008 . On phytoplankton trends . Science , 319 ( 5868 ): 1346 - 1348 , https://doi.org/10.1126/science.1151330 https://doi.org/10.1126/science.1151330 .
Sun D Y , Huan Y , Qiu Z F et al . 2017 . Remote-sensing estimation of phytoplankton size classes from GOCI satellite measurements in Bohai Sea and Yellow Sea . Journal of Geophysical Research: Oceans , 122 ( 10 ): 8309 - 8325 , https://doi.org/10.1002/2017JC013099 https://doi.org/10.1002/2017JC013099 .
Sun D Y , Huan Y , Wang S Q et al . 2019a . Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea . Water Research , 157 : 119 - 133 , https://doi.org/10.1016/j.watres.2019.03.081 https://doi.org/10.1016/j.watres.2019.03.081 .
Sun X R , Brewin R J W , Sathyendranath S et al . 2023 . Coupling ecological concepts with an ocean-colour model: phytoplankton size structure. Remote Sensing of Environment , 285 : 113415 , https://doi.org/10.1016/j.rse.2022.113415 https://doi.org/10.1016/j.rse.2022.113415 .
Sun X R , Shen F , Brewin R J W et al . 2019b . Twenty-year variations in satellite-derived chlorophyll- a and phytoplankton size in the Bohai Sea and Yellow Sea . Journal of Geophysical Research: Oceans , 124 ( 12 ): 8887 - 8912 , https://doi.org/10.1029/2019JC015552 https://doi.org/10.1029/2019JC015552 .
Sun X R , Shen F , Liu D Y et al . 2018 . In situ and satellite observations of phytoplankton size classes in the entire continental shelf sea, China . Journal of Geophysical Research: Oceans , 123 ( 5 ): 3523 - 3544 , https://doi.org/10.1029/2017JC013651 https://doi.org/10.1029/2017JC013651 .
Tan S C , Shi G Y . 2012 . The relationship between satellite-derived primary production and vertical mixing and atmospheric inputs in the Yellow Sea cold water mass . Continental Shelf Research , 48 : 138 - 145 , https://doi.org/10.1016/j.csr.2012.07.015 https://doi.org/10.1016/j.csr.2012.07.015 .
Trees C C , Clark D K , Bidigare R R et al . 2000 . Accessory pigments versus chlorophyll a concentrations within the euphotic zone: a ubiquitous relationship . Limnology and Oceanography , 45 ( 5 ): 1130 - 1143 , https://doi.org/10.4319/lo.2000.45.5.1130 https://doi.org/10.4319/lo.2000.45.5.1130 .
Turner K J , Mouw C B , Hyde K et al . 2021 . Optimization and assessment of phytoplankton size class algorithms for ocean color data on the Northeast U.S. continental shelf. Remote Sensing of Environment , 267 : 112729 , https://doi.org/10.1016/j.rse.2021.112729 https://doi.org/10.1016/j.rse.2021.112729 .
Uitz J , Huot Y , Bruyant F et al . 2008 . Relating phytoplankton photophysiological properties to community structure on large scales . Limnology and Oceanography , 53 ( 2 ): 614 - 630 , https://doi.org/10.4319/lo.2008.53.2.0614 https://doi.org/10.4319/lo.2008.53.2.0614 .
Vantrepotte V , Mélin F . 2011 . Inter-annual variations in the SeaWiFS global chlorophyll a concentration (1997-2007) . Deep Sea Research Part I: Oceanographic Research Papers , 58 ( 4 ): 429 - 441 , https://doi.org/10.1016/j.dsr.2011.02.003 https://doi.org/10.1016/j.dsr.2011.02.003 .
Victoria A H , Maragatham G . 2021 . Automatic tuning of hyperparameters using Bayesian optimization . Evolving Systems , 12 ( 1 ): 217 - 223 , https://doi.org/10.1007/s12530-020-09345-2 https://doi.org/10.1007/s12530-020-09345-2 .
Wang B D , Wang X L , Zhan R . 2003 . Nutrient conditions in the Yellow Sea and the East China Sea . Estuarine, Coastal and Shelf Science , 58 ( 1 ): 127 - 136 , https://doi.org/10.1016/S0272-7714(03)00067-2 https://doi.org/10.1016/S0272-7714(03)00067-2 .
Wang F , Li X G , Tang X H et al . 2023a . The seas around China in a warming climate . Nature Reviews Earth & Environment , 4 ( 8 ): 535 - 551 , https://doi.org/10.1038/s43017-023-00453-6 https://doi.org/10.1038/s43017-023-00453-6 .
Wang Y Q , Gao Z Q . 2020 . Contrasting chlorophyll- a seasonal patterns between nearshore and offshore waters in the Bohai and Yellow Seas, China: a new analysis using improved satellite data. Continental Shelf Research , 203 : 104173 , https://doi.org/10.1016/j.csr.2020.104173 https://doi.org/10.1016/j.csr.2020.104173 .
Wang Y Q , Liu D Y , Gao Z Q et al . 2023b . Characterizing spatial patterns of satellite-derived chlorophyll- a in the Bohai and Yellow Seas of China using self-organizing maps (SOM) approach. Marine Pollution Bulletin , 193 : 115176 , https://doi.org/10.1016/j.marpolbul.2023.115176 https://doi.org/10.1016/j.marpolbul.2023.115176 .
Wang Y Q , Liu D Y , Tang D L . 2017 . Application of a generalized additive model (GAM) for estimating chlorophyll- a concentration from MODIS data in the Bohai and Yellow Seas China . International Journal of Remote Sensing , 38 ( 3 ): 639 - 661 , https://doi.org/10.1080/01431161.2016.1268733 https://doi.org/10.1080/01431161.2016.1268733 .
Wang Y Q , Liu D Y , Wang Y J et al . 2019 . Evaluation of standard and regional satellite chlorophyll- a algorithms for moderate-resolution imaging spectroradiometer (MODIS) in the Bohai and Yellow Seas, China: a comparison of chlorophyll- a magnitude and seasonality . International Journal of Remote Sensing , 40 ( 13 ): 4980 - 4995 , https://doi.org/10.1080/01431161.2019.1577579 https://doi.org/10.1080/01431161.2019.1577579 .
Wang Y Q , Liu D Y , Wang Y J et al . 2024 . Optimizing an abundance-based model for satellite remote sensing of phytoplankton size classes in the Bohai and Yellow Seas of China. IEEE Transactions on Geoscience and Remote Sensing , 62 : 4204813 , https://doi.org/10.1109/TGRS.2024.3383391 https://doi.org/10.1109/TGRS.2024.3383391 .
Wang Y Q , Tian X P , Gao Z Q . 2021 . Evolution of satellite derived chlorophyll- a trends in the Bohai and Yellow Seas during 2002-2018: comparison between linear and nonlinear trends. Estuarine , Coastal and Shelf Science , 259 : 107449 , https://doi.org/10.1016/j.ecss.2021.107449 https://doi.org/10.1016/j.ecss.2021.107449 .
Wei Q S , Fu M Z , Ge R F et al . 2010 . Chemicohydrographic characteristics and the seasonal variations of nutrients at 35°N transect in the cold water mass of the Southern Yellow Sea . Environmental Science , 31 ( 9 ): 2603 - 2074 , https://doi.org/10.1631/jzus.A1000244 https://doi.org/10.1631/jzus.A1000244 .
Wei Q S , Yu Z G , Wang B D et al . 2016 . Coupling of the spatial-temporal distributions of nutrients and physical conditions in the southern Yellow Sea . Journal of Marine Systems , 156 : 30 - 45 , https://doi.org/10.1016/j.jmarsys.2015.12.001 https://doi.org/10.1016/j.jmarsys.2015.12.001 .
Xu Y , Zhang D , Lin J et al . 2024 . Prediction of phytoplankton biomass and identification of key influencing factors using interpretable machine learning models. Ecological Indicators , 158 : 111320 , https://doi.org/10.1016/j.ecolind.2023.111320 https://doi.org/10.1016/j.ecolind.2023.111320 .
Xuan J L , Zhou F , Huang D et al . 2011 . Physical processes and their role on the spatial and temporal variability of the spring phytoplankton bloom in the central Yellow Sea . Acta Ecologica Sinica , 31 ( 1 ): 61 - 70 , https://doi.org/10.1016/j.chnaes.2010.11.011 https://doi.org/10.1016/j.chnaes.2010.11.011 .
Yamaguchi H , Kim H C , Son Y B et al . 2012 . Seasonal and summer interannual variations of SeaWiFS chlorophyll a in the Yellow Sea and East China Sea . Progress in Oceanography , 105 : 22 - 29 , https://doi.org/10.1016/j.pocean.2012.04.004 https://doi.org/10.1016/j.pocean.2012.04.004 .
Zapata M , Rodríguez F , Garrido J L . 2000 . Separation of chlorophylls and carotenoids from marine phytoplankton: a new HPLC method using a reversed phase C 8 column and pyridine-containing mobile phases . Marine Ecology Progress Series , 195 : 29 - 45 , https://doi.org/10.3354/meps195029 https://doi.org/10.3354/meps195029 .
Zhai F G , Liu Z Z , Gu Y Z et al . 2023 . Satellite-observed interannual variations in sea surface chlorophyll- a concentration in the Yellow Sea over the past two decades . Journal of Geophysical Research: Oceans , 128 ( 4 ): e2022 JC 019528 , https://doi.org/10.1029/2022JC019528 https://doi.org/10.1029/2022JC019528 .
Zhai F G , Wu W F , Gu Y Z et al . 2021 . Interannual-decadal variation in satellite-derived surface chlorophyll- a concentration in the Bohai Sea over the past 16 years. Journal of Marine Systems , 215 : 103496 , https://doi.org/10.1016/j.jmarsys.2020.103496 https://doi.org/10.1016/j.jmarsys.2020.103496 .
Zhang M W , Tang J W , Dong Q et al . 2010 . Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery . Remote Sensing of Environment , 114 ( 2 ): 392 - 403 , https://doi.org/10.1016/j.rse.2009.09.016/ https://doi.org/10.1016/j.rse.2009.09.016/ .
Zhou F , Xuan J L , Huang D J et al . 2013 . The timing and the magnitude of spring phytoplankton blooms and their relationship with physical forcing in the central Yellow Sea in 2009 . Deep Sea Research Part II: Topical Studies in Oceanography , 97 : 4 - 15 , https://doi.org/10.1016/j.dsr2.2013.05.001 https://doi.org/10.1016/j.dsr2.2013.05.001 .
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