Abstract：In order to understand the spectral responses and spectral characteristics of crop polluted by heavy metal, the cultivated experiments of corns were implemented based on the pot soil stressed by different CuSO4·5H2O concentrations, and the Hilbert marginal spectral characteristics and the predicting pollution degree method were researched by the Hilbert-Huang transform (HHT) according to the measured data of corn leaves' reflectance spectra and the Cu2+ contents in leaves under the different stress gradients. The characteristic changes of marginal spectra were analyzed on different Cu2+ pollution levels of corn leaves by constructing some marginal spectral characteristic parameters such as marginal spectrum entropy (MSE), marginal spectral amplitude (MSA), slope of marginal spectral slope (MSSS) and marginal spectral enclosing area (MSEA), at the same time, some exponential models on predicting the Cu2+ contents in corn leaves were put forward based on the correlation analysis and stepwise regressing statistics on the marginal spectral characteristic parameter values and the Cu2+ contents in leaves because of the heavy metal pollution. The results show that Hilbert marginal spectra of corn leaf under different Cu2+ stress gradients was distributed within 100Hz frequency continuously, the MSE values showed a variation trend of negative correlation with Cu2+ contents in leaves, but the MSA, MSSS and MSEA values showed a variation trend of positive correlation with the Cu2+ contents. And the MSEA can be used as the best index to measure or predict heavy metal pollution in crops due to its best correlation between MSEA values and Cu2+ contents in leaves. The application results of the exponential models, which were built by the MSE、MSA、MSSS and MSEA values for predicting the Cu2+ contents in corn leaves, were compared and proved that the MSEA exponential model had the best predicting ability.
程龙, 杨可明, 王晓峰, 张伟, 孙彤彤. 作物重金属铜污染的HHT边际谱特征与污染预测模型[J]. 中国环境科学, 2018, 38(1): 340-347.
CHENG Long, YANG Ke-ming, WANG Xiao-feng, ZHANG Wei, SUN Tong-tong. Characteristic changes of HHT marginal spectra and pollution predicting models on crop polluted by the heavy metal copper. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(1): 340-347.
Kloiber S M, Brezonik P L, Olmanson L G, et al. A procedure for regional lake water clarity assessment using Landsat multispectral data[J]. Remote Sensing of Environment, 2002,82(1):38-47.
[12]
Feng W, Yao X, Tian Y, et al. Monitoring leaf pigment status with hyperspectral remote sensing in wheat[J]. Australian Journal of Agricultural Research, 2008,59(8):748-760.
Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences, 1998, 454(1971):903-995.
Zhang J, Tan X, Zheng P. Non-Destructive Detection of Wire Rope Discontinuities from Residual Magnetic Field Images Using the Hilbert-Huang Transform and Compressed Sensing[J]. Sensors, 2017,17(3):608.
[20]
Fu K, Qu J, Chai Y, et al. Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals[J]. Biomedical Signal Processing & Control, 2015,18:179-185.
[21]
Yang Z, Ling W K, Bingham C. Trend extraction based on separations of consecutive empirical mode decomposition components in Hilbert marginal spectrum[J]. Measurement, 2013, 46(8):2481-2491.
