Abstract：The environmental problems caused by heavy metal pollution have been particularly prominent in the regions with the rapid industrialization and urbanization development, especially the heavy metal pollution in agriculture is more concerned by the society. Therefore, it is very important to explore some fast and convenient methods on screening and monitoring heavy metal pollution. As a new technology of monitoring heavy metal pollution, the hyperspectral remote sensing has been paid attention and researched deeply by many scholars. A concept and method of inherent wavelength-scale decomposition (IWD) was proposed in the paper, and an IWD-Hankel-SVD model was established for predicting heavy metal pollution degree of vegetation combined with the Hankel matrix and the singular value decomposition (SVD), here the model was divided into single-variable model and multi-variable model. The single-variable model was mainly used to obtain the intrinsic rotation components (PRC) of spectral information of vegetation polluted by heavy metal through IWD processing and to extract the effective characteristic bands of the best PRC, then it could be realized to predict the heavy metal pollution according to the singular entropy of the model acquired by using SVD to decompose the Hankel matrix constructed based on each characteristic band. But the multi-variable model was used to realize the prediction of heavy metal pollution information by taking the relative values of vegetation chlorophyll concentration and the singular entropy acquired by the single-variable model as parameters. According to the data of leaf spectra, measured chlorophyll concentrations and Cu2+ contents in corn leaves polluted by heavy metal Cu2+ under different stress gradients, firstly the spectra of corn leaves stressed by the different Cu2+ concentrations were analyzed by IWD, the best PRC was obtained which could well retain the original spectral information, and some effective characteristic bands were extracted to be 553~680, 681~780, 1 266~1 429, 1 430~1 631, 1 836~1 913 and 1 914~2 111 nm from the PRC, then the Hankel matrix of each characteristic band was constructed and processed by SVD to obtain the singular entropy of the single-variable model, finally through the correlation analysis between the singular entropy of the model corresponding to each characteristic band and the Cu2+ contents in corn leaves, it was found that the determination coefficients R2 of the singular entropy and the Cu2+ contents in the leaves were all about 0.9 computed based on the 1 266~1 429 and 1 836~1 913 nm characteristic bands, the result shows that the two characteristic bands had more advantageous and interpretable for the IWD-Hankel-SVD model on predicting the Cu pollution degrees. At the same time, it was concluded that the multi-variable IWD-Hankel-SVD-model had stronger prediction ability of Cu pollution degrees in corn leaves by using the partial least square regression analysis based on taking the relative values of chlorophyll concentration in corn leaves and the singular entropy of the single-variable model corresponding to 1 266~1 429, 1 836~1 913 nm characteristic bands as parameters, and the determination coefficient R2 reached 0.947 6, so the multi-variable model was proved to be more robustness and steadiness.
Key words：Spectral analysis; Corn leaf; Heavy metal copper pollution; Intrinsic wavelength-scale decomposition; Prediction model
张建红，杨可明，韩倩倩，李艳茹，高 伟. IWD-Hankel-SVD模型下玉米叶片光谱铜污染信息预测[J]. 光谱学与光谱分析, 2021, 41(05): 1505-1512.
ZHANG Jian-hong, YANG Ke-ming, HAN Qian-qian, LI Yan-ru, GAO Wei. Predicting the Copper Pollution Information of Corn Leaves Spectral Based on an IWD-Hankel-SVD Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1505-1512.
[1] YANG Ke-ming, ZHANG Wei, FU Ping-jie，et al（杨可明, 张 伟, 付萍杰, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2019，39（7）：2228.
[2] Toth G, Hermann T, Da Silva MR, et al. Environment International. 2016, 88：299.
[3] Shi T, Wang J, Chen Y, et al. International Journal of Applied Earth Observation and Geoinformation，2016, 52：95.
[4] LI Jing, YANG Chao-yuan, YIN Shou-qiang，et al（李 晶, 杨超元, 殷守强, 等）. Journal of China Coal Society（煤炭学报），2019，44（12）：3676.
[5] Liu M L, Wang T J, Skidmore A K，et al. Science of the Total Environment，2018，637：18.
[6] QIAO Xiao-ying, MA Shao-yang, HOU Hui-fang，et al（乔晓英, 马少阳, 候会芳, 等）. Journal of Safety and Environment（安全与环境学报），2018，18（1）：335.
[7] FU Ping-jie, YANG Ke-ming, WANG Xiao-feng，et al（付萍杰, 杨可明, 王晓峰, 等）. Science Technology and Engineering（科学技术与工程），2018，18（23）：134.
[8] ZHU Ye-qing, QU Yong-hua, LIU Su-hong，et al（朱叶青, 屈永华, 刘素红, 等）. Journal of Remote Sensing（遥感学报），2014，18（2）：335.
[9] Maisto G, Santorufo L, Arena C. Journal of Plant Nutrition and Soil Science，2013, 176：776.
[10] Frei M G, Osorio I. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences，2007, 463（2078）: 321.
[11] WANG Zhi-hong, FAN Yu-gang, HUANG Guo-yong（王之宏, 范玉刚，黄国勇）. Journal of Yunnan University（云南大学学报·自然科学版），2018，40（2）：228.
[12] ZHANG Bo, LI Jian-jun（张 波，李健君）. Journal of Vibration and Shock（振动与冲击），2009，28（2）：162.
[13] LONG Ying, SU Yan-chen, LI Yan-ping，et al（龙 莹, 苏燕辰, 李艳萍, 等）. China Measurement & Test（中国测试），2018，44（5）：24.
[14] Liu S, Liu X, Hou J, et al. Science in China Series E-Technological Sciences，2008, 51：202.
[15] Delegido J, Verrelst J, Alonso L, et al. Sensors，2011, 11：7063.
