Cyanobacteria blooms are a threat to water quality of lakes and reservoirs worldwide, requiring scalable monitoring solutions. Existing approaches for remote sensing of cyanobacteria focus on quantifying (accessory) photosynthetic pigment to map surface accumulations. These approaches have proven challenging to validate against in situ observations, limiting uptake in water quality management. Optical Water Types (OWTs) have been used in inland and ocean waters to dynamically select suitable algorithms over optical gradients, thereby helping to limit out-of-scope application of individual algorithms. Here, we present a proof-of-concept study in Winam Gulf, Lake Victoria, extending an existing OWT framework using a hybrid approach combining in situ and satellite-derived water types. This extended OWT set of 25 water types, obtained from K-means clustering > 18 million Sentinel-3 Ocean and Land Colour Instrument (OLCI) spectra, was found to better capture the optical diversity of cyanobacteria bloom phases compared to the original OWT set. We translate this framework into a novel Cyanobacteria Occurrence Index (COI) by assigning weights to key optical features observed in the OWT set, such as phycocyanin absorption and surface accumulation. COI was strongly correlated with established algorithms for chlorophyll-a (Maximum Peak Height; r = 0.9) and phycocyanin (Simis07; r = 0.84), while potentially capturing various bloom phases in optically mixed conditions. We demonstrate how COI could be mapped onto a three-category risk classification to facilitate communication of cyanobacteria occurrence risk. Initial tests across diverse waterbodies suggest potential for wider application, though further validation across different environmental conditions is needed. This work provides a foundation for improved cyanobacteria monitoring in optically complex waters, particularly where conventional sampling approaches face limitations.

中文翻译：

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从热带湖泊中的光学水型得出的新型蓝藻出现指数


蓝藻大量繁殖对全球湖泊和水库的水质构成威胁，需要可扩展的监测解决方案。现有的蓝藻遥感方法侧重于量化（附属）光合色素以绘制表面积累。事实证明，这些方法很难根据原位观测进行验证，从而限制了水质管理的采用。光学水型 （OWT） 已在内陆和海洋水域用于动态选择合适的算法，而不是光学梯度，从而有助于限制单个算法的超范围应用。在这里，我们在维多利亚湖的 Winam Gulf 进行了一项概念验证研究，使用结合原位和卫星衍生水类型的混合方法扩展了现有的 OWT 框架。这个扩展的 OWT 集包含 25 种水类型，从 K-means 聚类 > 1800 万个 Sentinel-3 海洋和陆地颜色仪器 （OLCI） 光谱中获得，与原始 OWT 集相比，发现可以更好地捕捉蓝藻水华阶段的光学多样性。我们通过为 OWT 集中观察到的关键光学特征（例如藻蓝蛋白吸收和表面积累）分配权重，将该框架转化为新的蓝细菌发生指数 （COI）。COI 与叶绿素-a （最大峰高;r = 0.9） 和藻蓝蛋白 （Simis07;r = 0.84） 的既定算法密切相关，同时可能在光学混合条件下捕获各种光晕阶段。我们演示了如何将 COI 映射到三类风险分类上，以促进蓝藻发生风险的交流。在不同水体中的初步测试表明，尽管需要在不同环境条件下进行进一步验证，但仍有可能得到更广泛的应用。 这项工作为改进光学复杂水域中的蓝藻监测奠定了基础，尤其是在传统采样方法面临限制的情况下。