Cyanobacterial blooms pose great risks to aquatic systems due to its increase in frequency and severity worldwide. This study developed multi-temporal satellite remote sensing models that accurately portray spatial distribution of freshwater cyanobacterial blooms, which traditional monitoring methods cannot accomplish. The models were developed using multiple linear regression involving in-situ measurements and Landsat imagery in Lake Champlain for cyanobacterial blooms and other water quality variables i.e. chlorophyll-a, total phytoplankton, chlorophyte, chrysophyta, pyrrophyta, secchi disk and temperature. The developed models with Landsat imagery successfully captured the distribution of cyanobacterial blooms and other variables (adjusted R² = 0.6–0.98). Multi-temporal analysis of remote sensing highlighted the high bloom years with quantified cyanobacterial levels over the entire water body, associated with temperature, indicating the impact of precipitation on cyanobacterial bloom growth. The modeling approach in this study can provide information to improve our understanding of algal dominance and dynamics. This approach can be applied to other freshwater systems, providing a pathway for rapid response systems.

蓝藻水华在全世界范围内的频率和严重性不断提高，对水生系统构成了巨大风险。这项研究开发了多时相卫星遥感模型，可以准确地描绘淡水蓝藻水华的空间分布，而传统的监测方法则无法做到。该模型使用涉及多元线性回归研制现场测量和陆地卫星图像在尚普兰湖的蓝藻水华等水质变量即叶绿素一，总浮游植物，绿藻，金藻门，甲藻门，萨克氏盘和温度。利用Landsat影像开发的模型成功捕获了蓝藻水华的分布和其他变量（调整后的R ² = 0.6–0.98）。遥感的多时相分析突出显示了整个水体中蓝藻含量较高的盛水年，与温度相关，表明降水对蓝藻水华生长的影响。本研究中的建模方法可以提供信息，以增进我们对藻类优势和动力学的理解。这种方法可以应用于其他淡水系统，为快速响应系统提供途径。