Israel Oceanographic and Limnological Research, The Yigal Allon Kinneret Limnological Laboratory, Migdal 14950, Israel.
Department of Geography, University of Georgia, Athens 30602, GA, USA.
Sci Total Environ. 2022 Jan 20;805:150423. doi: 10.1016/j.scitotenv.2021.150423. Epub 2021 Sep 20.
Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.
蓝藻以产生有害藻类水华而闻名，这些水华对全球水生态系统构成了日益严重的威胁，影响饮用水质量，并破坏了许多水体的娱乐用途。需要使用遥感技术来检测和量化蓝藻水华，以监测其起始和时空变异性。在这项研究中，我们开发了一种新的半分析方法，从高光谱遥感数据中估算蓝藻特异性色素藻蓝蛋白 (PC) 和常见浮游植物色素叶绿素 a (Chl a) 的浓度。PC 算法源自吸收-浓度关系，而 Chl a 算法则基于概念性三波段结构模型设计。所开发的算法应用于由海岸带高光谱成像仪 (HICO™) 传感器获取的卫星图像，并在以色列的 Kinneret 湖强蓝藻微囊藻水华和水华外时期进行了测试。评估了算法对误差的敏感性。Chl a 和 PC 浓度的估计平均绝对百分比差异 (MAPD) 分别为 16%和 28%。敏感性分析表明，后向散射和其他水成分的影响不会影响 PC 估计的准确性 (~2%MAPD)。在 PC 浓度高于 10 mg m 时，可以获得可靠的 PC/Chl a 比值。计算出的 PC/Chl a 比值描述了蓝藻对总浮游植物生物量的贡献，并允许研究环境因素在复杂浮游生物群落形成中的作用。新算法具有广泛的实际适用性，并且如果传感器具有相应的波段，则应该适合使用高光谱遥感数据以及未来多光谱遥感卫星的数据来定量计算 PC 和 Chl a。
