Shi Kun, Zhang Yunlin, Qin Boqiang, Zhou Botian
Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China.
Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Sci Bull (Beijing). 2019 Oct 30;64(20):1540-1556. doi: 10.1016/j.scib.2019.07.002. Epub 2019 Jul 2.
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 oligotrophic 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.
及时监测、检测和量化蓝藻水华对于控制公共卫生风险和理解水生生态系统动态尤为重要。由于在大地理区域同时进行数据采集以及具有高时间覆盖度的优势，遥感技术极大地促进了内陆水域蓝藻水华的监测。我们提供了一份关于内陆水域蓝藻水华遥感的全面综述，包括蓝藻的光学特性、叶绿素、藻蓝蛋白和蓝藻水华面积的业务遥感算法以及卫星成像应用。我们得出结论，在过去30年里，蓝藻色素遥感算法取得了许多重大进展。红和近红外（NIR）光谱区域的波段比值算法在富营养和超富营养内陆水域叶绿素a的遥感估算方面具有很大潜力，而浮游藻类指数（FAI）是检测密集蓝藻水华最广泛使用的光谱指数。由于具有适当的时间、空间和光谱分辨率，陆地卫星、中分辨率成像光谱仪（MODIS）和中等分辨率成像光谱仪（MERIS）是监测内陆水域蓝藻时空动态最广泛使用的产品。未来的工作应主要集中在通用算法的开发、贫营养水域蓝藻水华的遥感反演以及算法在大时空尺度上绘制藻蓝蛋白的适用性。通过结合数值模型和生态系统动力学模型，卫星图像的应用将极大地增进我们对蓝藻水华驱动机制的理解。
