Cook Katherine V, Beyer Jessica E, Xiao Xiangming, Hambright K David
Plankton Ecology and Limnology Laboratory, Department of Biology, University of Oklahoma, Norman, USA; Program in Ecology and Evolutionary Biology, Department of Biology, University of Oklahoma, Norman, USA.
Center for Earth Observation and Modeling, Department of Microbiology and Plant Biology, University of Oklahoma, Norman, USA.
Water Res. 2023 Aug 15;242:120076. doi: 10.1016/j.watres.2023.120076. Epub 2023 May 23.
Cyanobacteria are the most prevalent bloom-forming harmful algae in freshwater systems around the world. Adequate sampling of affected systems is limited spatially, temporally, and fiscally. Remote sensing using space- or ground-based systems in large water bodies at spatial and temporal scales that are cost-prohibitive to standard water quality monitoring has proven to be useful in detecting and quantifying cyanobacterial harmful algal blooms. This study aimed to identify a regional 'universal' multispectral reflectance model that could be used for rapid, remote detection and quantification of cyanoHABs in small- to medium-sized productive reservoirs, such as those typical of Oklahoma, USA. We aimed to include these small waterbodies in our study as they are typically overlooked in larger, continental wide studies, yet are widely distributed and used for recreation and drinking water supply. We used Landsat satellite reflectance and in-situ pigment data spanning 16 years from 38 reservoirs in Oklahoma to construct empirical linear models for predicting concentrations of chlorophyll-a and phycocyanin, two key algal pigments commonly used for assessing total and cyanobacterial algal abundances, respectively. We also used ground-based hyperspectral reflectance and in-situ pigment data from seven reservoirs across five years in Oklahoma to build multispectral models predicting algal pigments from newly defined reflectance bands. Our Oklahoma-derived Landsat- and ground-based models outperformed established reflectance-pigment models on Oklahoma reservoirs. Importantly, our results demonstrate that ground-based multispectral models were far superior to Landsat-based models and the Cyanobacteria Index (CI) for detecting cyanoHABs in highly productive, small- to mid-sized reservoirs in Oklahoma, providing a valuable tool for water management and public health. While satellite-based remote sensing approaches have proven effective for relatively large systems, our novel results indicate that ground-based remote sensing may offer better cyanoHAB monitoring for small or highly dendritic turbid lakes, such as those throughout the southern Great Plains, and thus prove beneficial to efforts aimed at minimizing public health risks associated with cyanoHABs in supply and recreational waters.
蓝藻是全球淡水系统中最常见的形成水华的有害藻类。对受影响系统进行充分采样在空间、时间和资金方面都受到限制。在大型水体中使用天基或地基系统进行遥感，其空间和时间尺度对于标准水质监测来说成本过高，但已被证明在检测和量化蓝藻有害藻华方面很有用。本研究旨在确定一个区域“通用”多光谱反射率模型，该模型可用于快速、远程检测和量化中小型生产性水库中的蓝藻有害藻华，比如美国俄克拉荷马州典型的水库。我们旨在将这些小型水体纳入研究，因为它们在更大范围的大陆性研究中通常被忽视，但分布广泛且用于娱乐和饮用水供应。我们使用了俄克拉荷马州38个水库16年的陆地卫星反射率和现场色素数据，构建了经验线性模型，用于预测叶绿素a和藻蓝蛋白的浓度，这两种关键藻类色素通常分别用于评估总藻类丰度和蓝藻丰度。我们还使用了俄克拉荷马州7个水库5年的地基高光谱反射率和现场色素数据，构建了从新定义的反射率波段预测藻类色素的多光谱模型。我们源自俄克拉荷马州的陆地卫星和地基模型在俄克拉荷马州水库上的表现优于已有的反射率 - 色素模型。重要的是，我们的结果表明，地基多光谱模型在检测俄克拉荷马州高产中小型水库中的蓝藻有害藻华方面远优于基于陆地卫星的模型和蓝藻指数（CI），为水资源管理和公共卫生提供了一个有价值的工具。虽然基于卫星的遥感方法已被证明对相对较大的系统有效，但我们的新结果表明，地基遥感可能为小型或高度分支的浑浊湖泊提供更好的蓝藻有害藻华监测，比如整个大平原南部地区此类湖泊，从而证明对旨在将供应水和娱乐用水中与蓝藻有害藻华相关的公共卫生风险降至最低的努力有益。
