Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = −0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = −0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity.

中文翻译：

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使用韩国内陆水域的高光谱图像评估对蓝藻水华的理化影响

了解有害藻华对于保护水生生态系统和人类健康至关重要。这项研究描述了蓝细菌水华的时空分布，以识别百济水库水华与环境因素之间的关系。基于高光谱图像（HSI），使用一个固定站的两年蓝藻细胞数据和藻蓝蛋白（PC）浓度的四个遥感分布来描述水华的时空变化与影响因素之间的关系。实施了人工神经网络模型和三维水动力模型，分别使用遥感HSI估算了PC浓度并模拟了水​​动力。统计测试结果表明，蓝细菌生物量的变化很大程度上取决于水温（坡度= 0.13，p值<0.01），总氮（坡度= -0.487，p值<0.01）和总磷的变化。 （坡度= 20.7，p值<0.05），而生物量的变化适度取决于流出量的变化（坡度= -0.0097，p值= 0.065）。水温是影响从八月到十月的三个月中PC浓度变化的主要因素，并且在三个月中差异显着（p值<0.01）。在这三个月中，水动力参数对PC浓度的变化也有部分影响。全面的，这项研究有助于描述蓝藻水华的时空变化，并确定影响水华变化的因素。这项研究可能会作为制定减少开花频率和严重程度的策略的基础发挥重要作用。