†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.
§Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210046, China.
Environ Sci Technol. 2015 Jun 2;49(11):6448-56. doi: 10.1021/es505901a. Epub 2015 May 13.
Microcystins (MCs) produced by cyanobacteria pose a serious threat to public health. Intelligence on MCs distributions in freshwater is therefore critical for environmental agencies, water authorities, and public health organizations. We developed and validated an empirical model to quantify MCs in Lake Taihu during cyanobacterial bloom periods using the atmospherically Rayleigh-corrected moderate resolution imaging spectroradiometer (MODIS-Aqua) (Rrc) products and in situ data by means of chlorophyll a concentrations (Chla). First, robust relationships were constructed between MCs and Chla (r = 0.91; p < 0.001; t-test) and between Chla and a spectral index derived from Rrc (r = -0.86; p < 0.05; t-test). Then, a regional algorithm to analyze MCs in Lake Taihu was constructed by combining the two relationships. The model was validated and then applied to an 11-year series of MODIS-Aqua data to investigate the spatial and temporal distributions of MCs. MCs in the lake were markedly variable both spatially and temporally. Cyanobacterial bloom scums, temperature, wind, and light conditions probably affected the temporal and spatial distribution of MCs in Lake Taihu. The findings demonstrate that remote sensing reconnaissance in conjunction with in situ monitoring can greatly aid MCs assessment in freshwater.
微囊藻毒素（MCs）由蓝藻产生，对公众健康构成严重威胁。因此，关于淡水 MCs 分布的信息对环境机构、水管理部门和公共卫生组织至关重要。我们利用大气瑞利校正中分辨率成像光谱仪（MODIS-Aqua）（Rrc）产品和叶绿素 a 浓度（Chla）的原位数据，开发并验证了一个用于量化太湖蓝藻水华期间 MCs 的经验模型。首先，建立了 MCs 与 Chla 之间的稳健关系（r = 0.91；p < 0.001；t 检验），以及 Chla 与 Rrc 衍生的光谱指数之间的关系（r = -0.86；p < 0.05；t 检验）。然后，通过结合这两个关系，构建了一个用于分析太湖 MCs 的区域算法。对模型进行了验证，然后将其应用于 11 年的 MODIS-Aqua 数据系列，以调查 MCs 的时空分布。该湖中的 MCs 在空间和时间上都有明显的变化。蓝藻水华浮渣、温度、风和光照条件可能影响了太湖中 MCs 的时空分布。研究结果表明，遥感侦察与现场监测相结合，可以极大地帮助淡水 MCs 的评估。
