Environmental Science and Pollution Research

, Volume 26, Issue 11, pp 11012–11028 | Cite as

Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake

  • Meng Mu
  • Chuanqing Wu
  • Yunmei LiEmail author
  • Heng Lyu
  • Shengzhong Fang
  • Xiang Yan
  • Ge Liu
  • Zhubin Zheng
  • Chenggong Du
  • Shun Bi
Research Article


High-frequency and reliable data on cyanobacteria blooming over a long time period is crucial to identify the outbreak mechanism of blooms and to forecast future trends. However, in cloudy and rainy areas, it is difficult to retrieve useful satellite images, especially in the rainy season. To address this problem, we used data from the HJ-1/CCD (Chinese environment and disaster monitoring and forecasting satellite/charge coupled device), GF-1/WFV (Chinese high-resolution satellite/wide field of view), and Landsat-8/OLI (Operational Land Imager) satellites to generate a time series of the bloom area from 2009 to 2016 in Dianchi Lake, China. We then correlated the responses of bloom dynamics to meteorological factors. Several findings can be drawn: (1) a higher bloom frequency and a larger bloom area occurred in 2011, 2013, and 2016, compared to the other years; (2) the frequency of blooms peaked in April, August, and November each year and expanded from north to south starting in July; (3) air temperature in spring and sunshine hours in summer greatly correlated to the yearly bloom area; (4) wind speed and sunshine hours strongly affected the short-term expansion of blooms and thereafter influenced the monthly bloom scale; and (5) rainfall had a strong short-term influence on the occurrence of blooms. Cyanobacteria blooms often occurred when wind speeds were less than 2.35 ± 0.78 m/s in the dry season and 2.01 ± 0.75 m/s in the rainy season, when there were 48 to 72 h of sunshine in the dry season and 35 to 57 h of sunshine in the rainy season, and when there was more than 10 mm of daily precipitation.


Dianchi Lake Cyanobacteria bloom Multi-source remote sensing image Meteorological factors Multi-timescales 


Funding information

This study was supported by the National Key R&D Program of China (Grant No. 2017YFB0503902), the National Natural Science Foundation of China (Grant Nos. 41671340, 41701412, and 41701423), the Major Science and Technology Program for Water Pollution Control and Treatment (Grant No. 2017ZX07302-003), and the Natural Science Foundation of Jiangxi Province (Grant No. 20171BAB213024).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Meng Mu
    • 1
    • 2
  • Chuanqing Wu
    • 3
  • Yunmei Li
    • 1
    • 2
    Email author
  • Heng Lyu
    • 1
    • 2
  • Shengzhong Fang
    • 4
  • Xiang Yan
    • 4
  • Ge Liu
    • 5
  • Zhubin Zheng
    • 6
  • Chenggong Du
    • 1
    • 2
  • Shun Bi
    • 1
    • 2
  1. 1.Key Laboratory of Virtual Geographic Environment of Education MinistryNanjing Normal UniversityNanjingChina
  2. 2.Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and ApplicationNanjingChina
  3. 3.Satellite Environment Application CenterMinistry of Environmental ProtectionBeijingChina
  4. 4.Kunming Environment Monitor CenterKunmingChina
  5. 5.Northeast Institute of Geography and Agricultural EcologyChinese Academy of ScienceChangchunChina
  6. 6.School of Geography and Environmental EngineeringGannan Normal UniversityGanzhouChina

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