CAO Yin 1, 2, YE Yuntao 2, ZHAO Hongli 2, SHI Yubo 3, JIANG Yunzhong 2
1. School of Environmental Science and Engineering, Donghua University, Shanghai 201620, China;
2. Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;
3. Department of Water Resources Management, Ministry of Water Resources, Beijing 100053, China
Abstract: Aiming at the noise in hyperspectral curve and the problems that traditional semi-empirical methods cannot effectively use all effective spectral information, hyperspectral modeling method of suspended solid concentration and turbidity based on the coupling of Haar wavelet transform and partial least squares (Haar wavelet transform) is proposed. Firstly, the original spectral data of Nansi Lake on July 22 to 23, 2014 are compressed to 47 feature variables using wavelet transform with the wavelet function Haar and decompose scale 3. Secondly, according to the reconstruction data of spectra using wavelet transform, Haar WT-PLS inversion models of suspended solid concentration and turbidity are established and verified. The results show that inversion of suspended solid concentration and turbidity using Haar WT-PLS has higher accuracy and the root mean square errors of validation samples are 25.05 mg/L and 20.10 NTU, respectively. The average relative errors are 20.36% and 13.88%, respectively. Through the analysis and comparison of single band model, the first derivative model, band ratio models and Haar WT-PLS model, Haar WT-PLS model proposed in the paper has higher accuracy and stability to retrieve suspended solid concentration and turbidity.
Key words: wavelet transform; partial least squares; hyperspectral; suspended solid; turbidity; Nansi Lake
Published in: Journal of China Institute of Water Resources and Hydropower Research, Vol. 13, No. 3, June 2015