热带海洋学报 ›› 2022, Vol. 41 ›› Issue (2): 77-89.doi: 10.11978/2021048

• 海洋遥感学 • 上一篇    下一篇

基于OC-CCI数据的南海高叶绿素a浓度水域面积的时空变化研究*

李傲1,2(), 冯洋1,3, 王云涛1,4, 薛惠洁1()   

  1. 1. 热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东 广州 510301
    2. 中国科学院大学, 北京 100049
    3. 南方海洋科学与工程广东省实验室(广州), 广东 广州 511458
    4. 自然资源部第二海洋研究所, 浙江 杭州 310000
  • 收稿日期:2021-04-12 修回日期:2021-07-24 出版日期:2022-03-10 发布日期:2021-07-30
  • 通讯作者: 薛惠洁
  • 作者简介:李傲(1995—), 男, 安徽省六安市人, 硕士研究生, 从事热带海洋动力过程的环境效应研究。email: liao@scsio.ac.cn
  • 基金资助:
    中科院先导专项B项目子课题(XDB42010201);自然资源部第二海洋研究所中央级公益性科研院所基本科研业务费专项资金项目(HYGG2002)

Spatiotemporal variation of water area with high chlorophyll a concentration in the South China Sea based on OC-CCI data*

LI Ao1,2(), FENG Yang1,3, WANG Yuntao1,4, XUE Huijie1()   

  1. 1. State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
    4. The Second Institute of Oceanology, Ministry of Natural Resources, Hangzhou 310000, China
  • Received:2021-04-12 Revised:2021-07-24 Online:2022-03-10 Published:2021-07-30
  • Contact: XUE Huijie
  • Supported by:
    Subproject B of the Pilot Special Project of the Chinese Academy of Sciences(XDB42010201);Basic Research Funds of the Second Institute of Oceanology, Ministry of Natural Resources of China(HYGG2002)

摘要:

浮游植物是海洋生态系统食物链的基础组成, 并通过光合作用影响着海表二氧化碳通量变化。文章基于高叶绿素a浓度水域面积指标构建南海浮游植物生物量的估算体系。利用遥感数据, 采用经验正交函数分解插值方法, 重构长时间序列的南海叶绿素a浓度场, 并研究了南海高叶绿素a浓度水域面积特征的时空分布。结果发现: 高叶绿素a浓度水域面积变化有着显著季节特征, 在冬季面积达到最大值, 在夏季达到最小值, 但是该水域对应的叶绿素a浓度却在冬季达到最小值, 在夏季达到最大值, 这一特征可能是由于风驱动的海表动力过程使得海表叶绿素重新分布; 空间分布上, 高叶绿素a浓度水域常年存在于海岸附近, 特别是在中国沿海、越南沿岸、泰国湾以及婆罗洲岛附近。在巽他陆架与湄公河口东部中央海盆, 高叶绿素a浓度区域面积呈年际变化。受厄尔尼诺调控的南海季风, 导致不同年份湄公河口东南沿海存在不同程度的北部冷水侵入, 北部冷水入侵可能是引起局地浮游植物生物量增减的原因。

关键词: 南海, 浮游植物, 高叶绿素a浓度面积, 时空分布特征, 经验正交函数分解插值方法

Abstract:

Phytoplankton are the basis of the marine ecosystem food chain, and affect the variation of CO2 flux through photosynthesis. In this study, an estimation system of phytoplankton biomass in the South China Sea was established based on an area indicator. We used the empirical orthogonal function decomposition interpolation method (DINEOF) to reconstruct the chlorophyll a concentration field in the South China Sea from long time series of remote sensing data. We studied the space-time distribution of the high biomass water area of the South China Sea and found that the changes of water area with high chlorophyll a concentration had significant seasonal characteristics. The area of waters with high chlorophyll concentration reached the maximum in winter, and the minimum in summer. Conversely, the chlorophyll concentration reached the minimum in winter, and maximum in summer. This feature may be due to the wind-driven dynamic processes that redistribute the chlorophyll concentration near the surface. Moreover, waters with high chlorophyll concentration were found near the coast year round, especially in coastal waters of China, along the coast of Vietnam, the Gulf of Thailand, and near Borneo Island. In the Sunda Shelf and the central basin east of the Mekong estuary, the area of high biomass water showed interannual variation. The East Asian monsoon modulated by the El Niño and Southern Oscillation led to different degrees of cold water invasion from the north to the southeast of the Mekong Estuary in different years, which may be the reason for the increase and decrease of local phytoplankton biomass.

Key words: South China Sea, phytoplankton, area of high chlorophyll concentration, spatial and temporal distribution characteristics, Data Interpolating Empirical Orthogonal Functions

中图分类号: 

  • P735.52