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Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

期刊

SCIENCE BULLETIN
卷 64, 期 20, 页码 1540-1556

出版社

ELSEVIER
DOI: 10.1016/j.scib.2019.07.002

关键词

Cyanobacterial blooms; Inland waters; Bio-optical properties; Satellite; MODIS

资金

  1. National Science and Technology Major Project of China [2017ZX07203001]
  2. National Natural Science Foundation of China [41771472, 41621002]
  3. Youth Innovation Promotion Association of Chinese Academy of Sciences [2017365]
  4. Key Research Program of Frontier Sciences of Chinese Academy of Sciences [QYZDB-SSW-DQC016]
  5. Strategic Priority Research Program of Chinese Academy of Sciences [XDA19070301]

向作者/读者索取更多资源

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in olig-otrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models. (C) 2019 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.

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