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DOI10.1016/j.watres.2019.03.081
Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea
Sun, Deyong1,2,3; Huan, Yu1; Wang, Shengqiang1,3; Qiu, Zhongfeng1,3; Ling, Zunbin1; Mao, Zhihua2; He, Yijun1,3
发表日期2019
ISSN0043-1354
卷号157页码:119-133
英文摘要

Marine phytoplankton accounts for roughly half the planetary primary production, and plays significant roles in marine ecosystem functioning, physical and biogeochemical processes, and climate changes. Documenting phytoplankton assemblages' dynamics, particularly their community structure properties, is thus a crucial and also challenging task. A large number of in situ and space-borne observation datasets are collected that cover the marginal seas in the west Pacific, including Bohai Sea, Yellow Sea, and East China Sea. Here, a customized region-specific semi-analytical model is developed in order to detect phytoplankton community structure properties (using phytoplankton size classes, PSCs, as its first-order delegate), and repeatedly tested to assure its reliable performance. Independent in situ validation datasets generate relatively low and acceptable predictive errors (e.g., mean absolute percentage errors, MAPE, are 38.4%, 22.7%, and 34.4% for micro-, nano-, and picophytoplankton estimations, respectively). Satellite synchronization verification also produces comparative predictive errors. By applying this model to long time-series of satellite data, we document the past two-decadal (namely from 1997 to 2017) variation on the PSCs. Satellite-derived records reveal a general spatial distribution rule, namely microphytoplankton accounts for most variation in nearshore regions, when nanophytoplankton dominates offshore water areas, together with a certain high contribution from picophytoplankton. Long time-series of data records indicate a roughly stable tendency during the period of the past twenty years, while there exist periodical changes in a short-term one-year scale. High covariation between marine environment factors and PSCs are further found, with results that underwater light field and sea surface temperature are the two dominant climate variables which exhibit a good ability to multivariate statistically model the PSCs changes in these marginal seas. Specifically, three types of influence induced by underwater light field and sea surface temperature can be generalized to cover different water conditions and regions, and meanwhile a swift response time (approximately < 1 month) of phytoplankton to the changing external environment conditions is found by the wavelet analysis. This study concludes that phytoplankton community structures in the marginal seas remain stable and are year-independent over the past two decades, together with a short-term in-year cycle; this change rule need to be considered in future oceanographic studies. (C) 2019 Elsevier Ltd. All rights reserved.


WOS研究方向Engineering ; Environmental Sciences & Ecology ; Water Resources
来源期刊WATER RESEARCH
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/99162
作者单位1.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Jiangsu, Peoples R China;
2.Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou, Zhejiang, Peoples R China;
3.NUIST, Jiangsu Res Ctr Ocean Survey Technol, Nanjing, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Sun, Deyong,Huan, Yu,Wang, Shengqiang,et al. Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea[J],2019,157:119-133.
APA Sun, Deyong.,Huan, Yu.,Wang, Shengqiang.,Qiu, Zhongfeng.,Ling, Zunbin.,...&He, Yijun.(2019).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.
MLA Sun, Deyong,et al."Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea".WATER RESEARCH 157(2019):119-133.
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