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

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Abstract

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.

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Funding

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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Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, China

    Meng Mu, Yunmei Li, Heng Lyu, Chenggong Du & Shun Bi

  2. Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Meng Mu, Yunmei Li, Heng Lyu, Chenggong Du & Shun Bi

  3. Satellite Environment Application Center, Ministry of Environmental Protection, Beijing, 100029, China

    Chuanqing Wu

  4. Kunming Environment Monitor Center, Kunming, 650032, China

    Shengzhong Fang & Xiang Yan

  5. Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Science, Changchun, 130102, China

    Ge Liu

  6. School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, 341000, China

    Zhubin Zheng

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  1. Meng Mu
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Mu, M., Wu, C., Li, Y. et al. Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake. Environ Sci Pollut Res 26, 11012–11028 (2019). https://doi.org/10.1007/s11356-019-04522-6

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