National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD, USA.
National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA.
Harmful Algae. 2016 Apr;54:160-173. doi: 10.1016/j.hal.2016.01.005.
Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments - since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins - especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.
利用卫星图像来量化蓝藻毒素的空间分布模式存在一些挑战。这些挑战包括需要替代色素——由于蓝藻毒素不能被遥感直接检测，色素与蓝藻毒素之间的关系存在可变性——特别是微囊藻毒素（MC），以及各种测量方法缺乏标准化。双模型策略可以提供一种解决这些挑战的方法。一种模型使用现场采集的叶绿素-a（Chl-a）或藻蓝蛋白（PC）作为替代物来估计 MC 浓度。另一种模型使用遥感算法来估计替代色素的浓度。在蓝藻和真核藻类混合的水华情况下，PC 应该是 Chl-a 的首选替代物。在蓝藻占主导地位的情况下，Chl-a 比 PC 更适合遥感。藻蓝蛋白对光学遥感的检测不太敏感，它的测量频率较低，PC 的实验室方法仍未标准化，而且 PC 具有更大的细胞内变异性。任何水体都不应假定两种色素与 MC 之间存在固定的关系。MC-色素关系在数周内可能是有效的，但由于相对于蓝藻生物量产生的 MC 量的变化，具有相当大的年内和年际可变性。为了通过卫星检测色素，已经使用了三类算法（分析、半分析和衍生）。分析和半分析算法比衍生算法更敏感，但不如衍生算法稳健，因为它们依赖于精确的大气校正；因此衍生算法更常用。衍生算法可以估计 Chl-a 浓度，研究表明它们可以检测并可能量化 PC。然而，为了评估湖泊之间参数化的可重复性，需要对衍生算法进行标准化。本文描述了一种从蓝藻生物量中生成有用的微囊藻毒素估计值的策略，前提是解决了蓝藻毒素的可变性问题。
