Abstract：The use of hyperspectral remote sensing to monitor heavy metal pollution in crops has become an important part of remote sensing research. The difference in the amount of heavy metal content in the contaminated crop leaves mapped to the spectrum is weak, so it is challenging to dig sensitively the value information contained in it. In this paper, based on the spectrum of crop leaves, a pollution detection model of copper pollution vegetation index (CPVI) was proposed by combining multiple spectral feature bands to characterize the pollution degree of heavy metal copper on crops. Firstly, a pot experiment was conducted to add CuSO4·5H2O powder with different concentration gradients to the soil to simulate copper-contaminated soil environment and stress corn growth. The spectra of old, middle and new leaves at the ear of corn were collected, and the Cu2+ content and relative chlorophyll concentration in the leaves were determined. Then, using 58 randomly selected corn leaf spectra as experimental data, the spectral reflectances of the two groups of wavelengths λ1 and λ2 were selected in the wavelength range of 380~900 nm. The Pearson correlation coefficient between CPVI [λ1, λ2] and Cu2+ content in the corresponding leaves was calculated, and the absolute value matrix of correlation characteristics was obtained. Secondly, according to the obtained correlation feature matrix, the characteristic band of 690 and 465 nm with high correlation coefficient was extracted and combined with the band 850 nm to establish the Copper pollution index of maize (CPVIm). After that, CPVIm index was verified based on 26 other groups of data, and Normalized difference vegetation index (NDVI), MERIS terrestrial chlorophyll index (MTCI) and other conventional vegetation indexes were compared to verify the effectiveness and superiority of CPVIm. The results showed that the highest correlation coefficient between NDVI, MTCI, REP, DVI and Cu2+ content in leaves was 0.68, and the lowest residual sum of squares was 70.99. However, CPVIm was significantly negatively correlated with Cu2+ content in leaves. The correlation coefficient was -0.80, and the residual sum of squares was 48.52, which were better than conventional indexes such as NDVI and MTCI. It proved that CPVIm is more sensitive to heavy metal stress. At the same time, the robustness of CPVIm index was verified by using the spectral data of different varieties of maize in different years. The correlation coefficient of CPVIm and Cu2+ content were -0.90 and -0.96, respectively, which were significantly correlated. It shows that CPVIm is still suitable for detecting the pollution degree of different maize varieties. In addition, using Cu2+ content, CPVIm and chlorophyll relative concentration in maize leaves, a three-dimensional analysis model was constructed, which reflected the correlation between them from a spatial point of view. The CPVI detection model based on the combination of spectral characteristic bands can be used as a reference method to evaluate the pollution degree of heavy metals in crops. The CPVIm index based on this method can effectively identify the degree of heavy metal Cu2+ pollution in maize.
程 凤，杨可明，崔 颖，陆天宇，陈立帆，荣坤鹏. 铜污染植被指数的玉米叶片污染程度探测模型[J]. 光谱学与光谱分析, 2020, 40(01): 209-214.
CHENG Feng, YANG Ke-ming, CUI Ying, LU Tian-yu, CHEN Li-fan, RONG Kun-peng. A Model on Detecting the Polluted Degree of Maize Leaves by Cu Pollution Vegetation Index. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(01): 209-214.
[1] 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.
[2] YU Min-da, ZHANG Hui, HE Xiao-song, et al(虞敏达, 张 慧, 何小松, 等). Chinese Journal of Environmental Engineering(环境工程学报) , 2016, 10(3): 1500.
[3] LIU Sha-sha, FU Jian-ping, CAI Xin-de, et al(刘沙沙, 付建平, 蔡信德, 等). Ecology and Environmental Sciences(生态环境学报), 2018, 27(6): 1173.
[4] SONG Wei, CHEN Bai-ming, LIU Lin(宋 伟, 陈百明, 刘 琳). Research of Soil & Water Conservation(水土保持研究), 2013, 20(2): 293.
[5] CHEN Wei-ping, YANG Yang, XIE Tian, et al(陈卫平, 杨 阳, 谢 天, 等). Acta Pedologica Sinica(土壤学报), 2018, 55(2): 261.
[6] Hede A N H, Kashiwaya K, Koike K, et al. Remote Sensing of Environment, 2015, 171: 83.
[7] Rathod P H, Brackhage C, Meer F D V D, et al. European Journal of Remote Sensing, 2015, 48(3): 283.
[8] LIU Mei-ling, LIU Xiang-nan, LI Ting, et al(刘美玲, 刘湘南, 李 婷, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(3): 191.
[9] LI Mi, LIU Xiang-nan, LIU Mei-ling(李 蜜, 刘湘南, 刘美玲). Acta Scientiae Circumstantiae(环境科学学报), 2010, 30(10): 2108.
[10] Gotze C, Glasser C, Jung A. International Journal of River Basin Management, 2016, 14(4): 499.
[11] Schlemmer M, Gitelson A, Schepers J, et al. International Journal of Applied Earth Observations and Geoinformation. 2013, 25(1): 47.
[12] Jay S, Maupas F, Bendoula R, et al. Field Crops Research, 2017, 210: 33.
[13] LIU Yan, NIE Lei, YANG Yun(刘 艳, 聂 磊, 杨 耘). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(9): 182.
[14] Danson F M, Plummer S E. Remote Sensing, 1995, 16(1): 183.
