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A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach

机译:蓝藻中藻蓝蛋白浓度预测的新算法:近高光谱遥感方法

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摘要

The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. We present a novel spectral band ratio algorithm using 700 and 600 nm that is much less sensitive to the presence of chlorophyll.
机译:这项研究的目的是评估现有光谱带比率算法的性能,并开发一种使用高光谱遥感数据定量蓝藻中藻蓝蛋白(PC)的新算法。我们在两个不同的实验室培养的蓝细菌物种上进行了四个光谱实验,发现现有的谱带比算法对叶绿素高度敏感,从而使它们在存在其他含叶绿素生物的情况下无法准确预测蓝细菌的丰度。我们提出了一种使用700和600 nm的新型光谱带比率算法,该算法对叶绿素的存在不那么敏感。

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