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Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu, China

机译：太湖内陆浑浊水中藻蓝蛋白色素的遥感

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摘要

Cyanobacterial blooms are an environmental issue that can cause health hazards by toxins and malodorous compounds. The pigment phycocyanin is indicative of cyanobacterial presence. In eutrophic inland waters in which nitrogen is not a limiting nutrient, the phycocyanin concentration (PC) is closely related to cyanobacterial biomass. This study proposes a simple semi-analytical four-band algorithm for PC estimation to overcome the deficiency of existing algorithms. This algorithm was calibrated using a data set collected from Lake Taihu in 2007. Optimal reference wavelengths for the algorithm were located through model tuning and accuracy optimization. The algorithm was evaluated for its accuracy against an independent data set collected in 2008. The performance of the algorithm was also compared with that of the nested band-ratio algorithm, which was developed for PC estimation in turbid waters. Although both algorithms enabled the establishment of a linear relationship between measured and predicted PC, the nested band-ratio algorithm did not have a satisfactory performance with either data set, having a high level of uncertainty. Its mean relative error stands at 51.07% and 51% for the 2007 and 2008 data sets, respectively. It accounted for 68% and 74% of the variation in PC in the 2007 and 2008 data sets, respectively. The four-band algorithm worked well in PC estimation. It accounted for 87% of the variation in PC for the 2007 data set and 86% of the variation in the 2008 data set. Furthermore, it decreased estimation uncertainty, compared with the nested band-ratio algorithm, by more than 20%. The values of mean relative error for the correspondence data sets are 29.1% and 30%. Therefore, the proposed four-band algorithm holds great potential in estimating PC in highly turbid waters.
机译：蓝藻水华是一个环境问题，可能会由于毒素和恶臭化合物而危害健康。色素藻蓝蛋白指示存在蓝细菌。在氮不是限制性养分的富营养化内陆水域中，藻蓝蛋白浓度（PC）与蓝细菌生物量密切相关。这项研究提出了一种简单的半解析四频带PC估计算法，以克服现有算法的不足。使用2007年从太湖收集的数据集对该算法进行了校准。通过模型调整和精度优化确定了该算法的最佳参考波长。针对2008年收集的独立数据集对该算法的准确性进行了评估。还将该算法的性能与为在浑浊水中进行PC估计而开发的嵌套带比率算法进行了比较。尽管这两种算法都可以在被测PC和预测PC之间建立线性关系，但是嵌套带比率算法对于这两个数据集都没有令人满意的性能，具有很高的不确定性。对于2007年和2008年的数据集，其平均相对误差分别为51.07％和51％。在2007年和2008年的数据集中，它分别占PC差异的68％和74％。四频带算法在PC估计中效果很好。它占2007年数据集PC差异的87％，占2008年数据集PC差异的86％。此外，与嵌套带比率算法相比，它将估计不确定性降低了20％以上。对应数据集的平均相对误差值为29.1％和30％。因此，提出的四频带算法在估计高浑浊水域的PC方面具有很大的潜力。

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  • 来源
    《International journal of remote sensing》 |2011年第23期|p.8253-8269|共17页
  • 作者

    CHENGFENG LE; YUNMEI LI; YONG ZHA; QIAO WANG; HONG ZHANG; BIN YIN;

  • 作者单位

    Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210046, P. R. China;

    Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210046, P. R. China;

    Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210046, P. R. China;

    Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210046, P. R. China;

    Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210046, P. R. China;

    Key Laboratory of Virtual Geographic Environment, Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210046, P. R. China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
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  • 正文语种 eng
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