一种新的水体叶绿素a指数及其应用潜力分析

A New Water Chlorophyll-a Index and Analysis of Its Application Potential

以具有代表性的野外实测光谱数据作为端元,对水体遥感反射率进行线性混合光谱分解,基于分解得到的各组分丰度,构建了一种新的水体叶绿素a指数CSI(叶绿素a光谱指数)。以太湖、巢湖、滇池以及三峡水库水体的307组实测叶绿素a浓度及高光谱数据为基础,分析了CSI的特性。以该指数为自变量,构建了内陆浑浊二类水体叶绿素a浓度估算模型,并分析了模型的抗噪性和传感器适应性。结果表明:1)CSI对水体叶绿素a浓度大小有较好的指示作用,以fCSI=0为条件将实测光谱分为2个类别,可以表征光谱特征的明显差异;2)CSI作为自变量的叶绿素a浓度估算模型在实测高光谱数据集中的精度与三波段算法(TBA)相近(二者估算结果的平均相对误差分别为0.332和0.330,均方根误差分别为9.892和9.929);3)以CSI为自变量得到的估算模型对无偏移噪声和有偏移噪声都有较好的抗性,其中无偏移噪声几乎不影响算法的精度,而三波段算法对两种噪声同样敏感,随着噪声增加,估算结果出现较大误差;4)新的估算算法对传感器波段设置不敏感,其优势在宽波段多光谱数据集中更加明显。相比于传统水体叶绿素半经验算法,CSI算法具有更高的稳定性和更强的应用潜力。

A chlorophyll-a spectrum index （CSI） is built based on the linear spectral unmixing method using the endmembers selected from in situ spectra. The character of CSI is analyzed based on a dataset including 307 samples collected from the Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Georges Reservoir. Furthermore, a chlorophyll-a concentration （Cchla） estimation model is built and compared with traditional models in terms of noise immunity and sensor adaptability. The results show that： 1） CSI is a good indicator for Cchla. Two spectrum sets that are divided by fCSI=0 clearly show different spectrum characteristics; 2） for the hyperspectral dataset, the CSI algorithm gets similar performance to that of the three band algorithm （TBA） （their validation mean absolute percent errors are 0.332 and 0.330, respectively, root mean squares errors are 9.892 and 9.929, respectively）; 3） the CSI algorithm is not sensitive to both unbiased and biased noise. Its accuracy is nearly independent on the unbiased noise. Meanwhile, the traditional three band algorithm is sensitive to both of the two kinds of noise; 4） the CSI algorithm is less sensitive to the band settings of the remote sensor than the traditional algorithms. The advantage is more obvious for wide band multispectral sensors. Compared with traditional Cchla semi-empirical models, the CSI algorithm is more stable and potential.