Detection of Chlorophyll Content in Water Body Based on Two-Dimensional Correlation Spectroscopy
ZHANG Yao, ZHENG Li-hua*, SUN Hong, LI Min-zan
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
Abstract：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.
张 瑶，郑立华*，孙 红，李民赞 . 基于二维相关光谱的水体叶绿素含量探测 [J]. 光谱学与光谱分析, 2014, 34(02): 389-393.
ZHANG Yao, ZHENG Li-hua*, SUN Hong, LI Min-zan . Detection of Chlorophyll Content in Water Body Based on Two-Dimensional Correlation Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(02): 389-393.
