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A bio-optical algorithm for the remote estimation of the chlorophyll- a concentration in case 2 waters

机译:一种生物光学算法,用于远程估计叶绿素 - 案例2水中的浓度

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摘要

The objective of this work was to test the performance of a recently developed three-band model and its special case, a two-band model, for the remote estimation of the chlorophyll-a (chl-a) concentration in turbid productive case 2 waters. We specifically focused on (a) determining the ability of the models to estimate chl-a u3c 20 mg m−3, typical for coastal and estuarine waters, and (b) assessing the potential of MODIS and MERIS to estimate chl-a concentrations in turbid productive waters, using red and near-infrared (NIR) bands. Reflectance spectra and water samples were collected in 89 stations over lakes in the United States with a wide variability in optical parameters (i.e. 2.1 u3c chl-a u3c 184 mg m−3; 0.5 u3c Secchi disk depth u3c 4.2 m; 1.2 u3c total suspended matter u3c 15 mg l−1). The three-band model, using wavebands around 670, 710 and 750 nm, explains more than 89% of the chl-a variation for chl-a ranging from 2 to 20 mg m−3 and can be used to estimate chlorophyll-a concentrations with a root mean square error (RMSE) of u3c1.65 mg m−3. MODIS (bands 13 and 15) and MERIS (bands 7, 9, and 10) red and NIR reflectances were simulated from the collected reflectance spectra and potential estimation errors were assessed. The MODIS two-band model is able to estimate chl-a concentrations with a RMSE of u3c7.5 mg m−3 for chl-a ranging from 2 to 50 mg m−3; however, the model loses its sensitivity for chl-a u3c 20 mg m−3. Benefiting from the higher spectral resolution of the MERIS data, the MERIS three-band model accounts for 93% of chl-a variation and is able to estimate chl-a concentrations with a RMSE of u3c5.1 mg m−3 for chl-a ranging from 2 to 50 mg m−3, and a RMSE of u3c1.7 mg m−3 for chl-a ranging from 2 to 20 mg m−3. These findings imply that, provided that an atmospheric correction scheme specific to the red and NIR spectral region is available, the extensive database of MODIS and MERIS images could be used to quantitatively monitor chl-a in case 2 waters.
机译:这项工作的目的是测试一个新近开发的三波段模型在浑浊的生产二类水性能和其特殊的情况下,两带模型,对叶绿素a(叶绿素a)的远程估计浓度。我们特别专注于(a)确定所述模型来估计能力叶绿素a U3C 20毫克M-3,典型的沿海和河口水,和(b)评估MODIS和MERIS的电位来估计叶绿素a浓度在浑浊生产性水域,使用红色和近红外(NIR)波段。反射光谱和水样中的89台,收集了在美国湖泊与光学参数的很大的可变性(即2.1 U3C叶绿素a U3C 184毫克间3; 0.5 U3C沙奇磁盘深度 U3C4.2米; 1.2 U3C总悬浮物 U3C 15毫克L-1)。三带模型,使用围绕670,710和750nm波段,解释了叶绿素a的变化超过89%为叶绿素a为2至20毫克M-3,并且可以用来估计叶绿素a浓度用均方根的 u3c1.65毫克M-3方误差(RMSE)。 MODIS(带13和15)和MERIS(带7,图9和10)的红色和NIR反射率是从所收集的反射光谱和潜在的估计误差的模拟进行了评估。所述MODIS双带模型能够估计与 u3c7.5毫克间3所述的RMSE叶绿素a浓度叶绿素a为2至50毫克间3;然而,该模型失去其灵敏度叶绿素a U3C 20毫克M-3。从MERIS数据的更高的光谱分辨率受益,所述MERIS三波段模型占叶绿素a变异的93%,并且能够估计与 u3c5.1毫克间3 chl-一个RMSE叶绿素a浓度一个范围为2至50mg M-3,和 u3c1.7毫克间3叶绿素a范围为2至20mg米-3的RMSE。这些发现暗示,条件是大气校正方案特有的红色和NIR光谱区是可用的,MODIS和MERIS图像的庞大的数据库可以被用于定量监测叶绿素a在壳体2米的水域。

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