Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing

被引:33
|
作者
Hu, Zifeng [1 ,2 ,3 ]
Pan, Delu [1 ]
He, Xianqiang [1 ]
Bai, Yan [1 ]
机构
[1] State Ocean Adm, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, 36 North Bao Chu Rd, Hangzhou 310012, Zhejiang, Peoples R China
[2] Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, 164 Xingangxi Rd, Guangzhou 510301, Guangdong, Peoples R China
[3] Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
GOCI; turbidity front; entropy-based algorithm; sea surface currents; SURFACE TEMPERATURE FRONTS; EAST CHINA SEA; SUSPENDED SEDIMENT CONCENTRATION; YELLOW SEA; EDGE-DETECTION; SEASONAL VARIABILITY; SPATIAL VARIABILITY; TEMPORAL VARIATION; CONTINENTAL-SHELF; COASTAL WATERS;
D O I
10.3390/rs8020147
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Monitoring front dynamics is essential for studying the ocean's physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes.
引用
下载
收藏
页数:15
相关论文
共 50 条
  • [31] Diurnal variability of ocean optical properties during a coastal algal bloom: implications for ocean colour remote sensing
    Cui, Tingwei
    Cao, Wenxi
    Zhang, Jie
    Hao, Yanling
    Yu, Yonggui
    Zu, Tingting
    Wang, Dongxiao
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (23) : 8301 - 8318
  • [32] Diurnal variability of turbidity and light attenuation in the southern North Sea from the SEVIRI geostationary sensor
    Neukermans, G.
    Ruddick, K. G.
    Greenwood, N.
    REMOTE SENSING OF ENVIRONMENT, 2012, 124 : 564 - 580
  • [33] Extending satellite ocean color remote sensing to the near-blue ultraviolet bands
    Wang, Yongchao
    Lee, Zhongping
    Wei, Jianwei
    Shang, Shaoling
    Wang, Menghua
    Lai, Wendian
    REMOTE SENSING OF ENVIRONMENT, 2021, 253
  • [34] Remote sensing estimation of phytoplankton groups using Chinese ocean Color satellite data
    Sun D.
    Chen Y.
    Liu J.
    Wang S.
    He Y.
    National Remote Sensing Bulletin, 2023, 27 (01) : 128 - 144
  • [35] Importance and estimation of aerosol vertical structure in satellite ocean-color remote sensing
    Duforet, Lucile
    Frouin, Robert
    Dubuisson, Philippe
    APPLIED OPTICS, 2007, 46 (07) : 1107 - 1119
  • [36] Atmospheric Correction of Satellite Ocean Color Remote Sensing in the Presence of High Aerosol Loads
    Mao, Zhihua
    Tao, Bangyi
    Chen, Peng
    Chen, Jianyu
    Hao, Zengzhou
    Zhu, Qiankun
    Huang, Haiqing
    REMOTE SENSING, 2020, 12 (01)
  • [37] Simulation of Sedimentation in Lake Taihu with Geostationary Satellite Ocean Color Data
    He, Anpeng
    He, Xianqiang
    Bai, Yan
    Zhu, Qiankun
    Gong, Fang
    Huang, Haiqing
    Pan, Delu
    REMOTE SENSING, 2019, 11 (04)
  • [38] Satellite ocean remote sensing at NOAA/NESDIS
    Bayler, EJ
    ATMOSPHERIC AND ENVIRONMENTAL REMOTE SENSING DATA PROCESSING AND UTILIZATION: AN END TO END SYSTEM PERSPECTIVE, 2004, 5548 : 238 - 252
  • [39] A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability
    Bracaglia, Marco
    Santoleri, Rosalia
    Volpe, Gianluca
    Colella, Simone
    Benincasa, Mario
    Brando, Vittorio Ernesto
    REMOTE SENSING, 2020, 12 (10)
  • [40] A stochastic technique for remote sensing of ocean color
    Frouin, Robert
    Pelletier, Bruno
    REMOTE SENSING OF THE MARINE ENVIRONMENT, 2006, 6406