利用Shimadzu UV2450分光光度计测量了含有不同叶绿素浓度的湖水样本在可见光和近红外区域的透射光谱， 并使用实验室手段测量了水体的叶绿素含量。 分析了湖水样本的透射光谱特性， 同时引入二维相关光谱技术， 利用叶绿素浓度值作为微扰量， 得到水体叶绿素的动态光谱， 进而结合二维同步谱图和异步谱图确定表征水体叶绿素浓度的特征波段。 综合观察二维相关光谱中的同步谱图和异步谱图， 更加精确地阐明了水体光谱特征， 同时剔除水体中其他物质对于光谱信息的影响， 更有效、 全面地提取反映水体叶绿素信息的敏感波段。 利用所选特征波段构建归一化水体叶绿素指数， 将特征波段与叶绿素指数分别与水体叶绿素浓度建立线性预测模型。 结果显示， 归一化水体叶绿素指数的预测模型测定R2达到0.771 2， 均方根误差是45.509 8 mg·L-1， 预测R2达到0.765 8， 均方根误差是39.503 8 mg·L-1。 模型精度较利用特征波段建立的多元线性回归模型有了较大的提高， 达到了实用水平。

Twenty five samples were collected from 10 different ponds in Jiangsu Province of China. According to the different water status and surface area of each pond, different numbers of water samples were collected. The present paper aims to detect chlorophyll content in water body based on hyperspectrum. The visible and near infrared spectral transmittance of the water samples was measured by using a Shimadzu UV-2450 spectrograph. At the same time, the chlorophyll content of each sample was measured using hot-ethanol extraction method in the laboratory. Then the spectral characteristics were analyzed for the water samples and the results showed that with chlorophyll concentration increasing, spectral transmittance decreased gradually. There is an apparent transmission valley at 676 nm. And then two dimensional correlation spectrum technology was used to analyze the sensitive absorption band of chlorophyll in water body. Comprehensive observation of the spectral characteristics of water samples can be carried out much accurately by analyzing two-dimensional correlation spectra of synchronous and asynchronous spectrograms. And the effective spectral response bands of the chlorophyll content were found at 488 and 676 nm. Then the NDWCI (normalized difference water chlorophyll index) was established with the transmittance of red band and blue band. Two regression models were built to predict the chlorophyll concentration in water. One is a multiple linear regression model based on the original transmittances at 488 and 676 nm. The other is the linear regression model based on NDWCI. By comparison, the model based on NDWCI was better. The R2 of its training model reached to 0.771 2, and the root mean square error of calibration was 45.509 9 mg·L-1. The R2 of prediction model reached to 0.765 8, and the root mean square error of prediction was 39.503 8 mg·L-1. It reached to a practical level to predict the chlorophyll content in water body rapidly.