Remote Estimation of Chlorophyll-a in Inland Waters by a NIR-Red-Based Algorithm: Validation in Asian Lakes
Remote Estimation of Chlorophyll-a in Inland Waters by a NIR-Red-Based Algorithm: Validation in Asian Lakes
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DOI：
10.3390/rs6043492
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
Gongliang Yu;Wei Yang;B. Matsushita;Renhui Li;Y. Oyama;T. Fukushima
中科院分区：
文献类型：
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作者：
Gongliang Yu;Wei Yang;B. Matsushita;Renhui Li;Y. Oyama;T. Fukushima
卫星遥感是监测水体叶绿素a浓度的一种非常有用的工具。基于近红外波段的遥感算法在内陆沃茨叶绿素a的反演中具有很大的潜力。这项研究测试了最近开发的近红外基于算法，SAMO-LUT（半分析模型优化和查找表）的性能，使用广泛的数据集从五个亚洲湖泊。结果表明，SAMO-LUT算法反演的Chl-a与实测的Chl-a具有很强的相关性（R-2 = 0.94），均方根误差（RMSE）和归一化均方根误差（NRMS）分别为8.9 mg centerdot m（-3）和72.6%。然而，SAMO-LUT算法在叶绿素a小于10 mg中心点m（-3）的位点产生较大误差（RMSE = 1.8 mg中心点m（-3）和NRMS = 217.9%）。这是因为在NIR-红色波长处离开水的辐射度的差异（即，665 nm、705 nm和754 nm）太小，这是由于水组分的浓度低。对于低组分浓度的沃茨，使用蓝绿算法（OC 4 E）代替SAMO-LUT，RMSE和NRMS将分别降低至1.0 mg中心点m（-3）和16.0%。这表明（1）当水成分浓度相对较低时，NIR-红色算法不能很好地工作;（2）应根据水成分浓度使用不同的算法;因此（3）有必要开发分类方法以选择合适的算法。
Satellite remote sensing is a highly useful tool for monitoring chlorophyll-a concentration (Chl-a) in water bodies. Remote sensing algorithms based on near-infrared-red (NIR-red) wavelengths have demonstrated great potential for retrieving Chl-a in inland waters. This study tested the performance of a recently developed NIR-red based algorithm, SAMO-LUT (Semi-Analytical Model Optimizing and Look-Up Tables), using an extensive dataset collected from five Asian lakes. Results demonstrated that Chl-a retrieved by the SAMO-LUT algorithm was strongly correlated with measured Chl-a (R-2 = 0.94), and the root-mean-square error (RMSE) and normalized root-mean-square error (NRMS) were 8.9 mg center dot m(-3) and 72.6%, respectively. However, the SAMO-LUT algorithm yielded large errors for sites where Chl-a was less than 10 mg center dot m(-3) (RMSE = 1.8 mg center dot m(-3) and NRMS = 217.9%). This was because differences in water-leaving radiances at the NIR-red wavelengths (i.e., 665 nm, 705 nm and 754 nm) used in the SAMO-LUT were too small due to low concentrations of water constituents. Using a blue-green algorithm (OC4E) instead of the SAMO-LUT for the waters with low constituent concentrations would have reduced the RMSE and NRMS to 1.0 mg center dot m(-3) and 16.0%, respectively. This indicates (1) the NIR-red algorithm does not work well when water constituent concentrations are relatively low; (2) different algorithms should be used in light of water constituent concentration; and thus (3) it is necessary to develop a classification method for selecting the appropriate algorithm.