A New Copper Stress Vegetation Index NCSVI Explores the Sensitive Range of Corn Leaves Spectral Under Copper Pollution
XIA Tian1*, YANG Ke-ming2, FENG Fei-sheng3, GUO Hui4, ZHANG Chao2
1. China Centre for Resources Satellite Data and Application, Beijing 100094, China
2. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
3. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
4. School of Surveying and Mapping, Anhui University of Science and Technology, Huainan 232001, China
Abstract：At present, heavy metal pollution in the soil is becoming more and more serious in China. Hyperspectral remote sensing has become a hot spot in the research of heavy metal pollution in crops by reason of its characteristics such as high spectral resolution and integrated maps with spectral. The spectral of crops will change slightly after being contaminated by heavy metals, how to explore the sensitive bands in the leaves spectral stresses by heavy metal pollution is a current research direction. In this study, a new copper stress vegetation index (NCSVI) was proposed to explore the sensitive range of corn leaves spectral under copper stress. By designing corn stress experiments with different gradients, the spectral and the contents of Cu2+ in corn leaves under each copper stress concentration were determined. First, the spectral of corn leaves were divided into 11 sub-band intervals, NCSVI were constructed by spectral reflectance corresponded to the middle wavelength of each sub-band interval. Then, the Pearson correlation coefficient and RMSE (Root Mean Square Error) between NCSVI and the contents of Cu2+ in each corn leaves was calculated, combined with three conventional vegetation indexes of water band index (WBI), modified chlorophyll absorption ratio index (MCARI) and normalized water index (NDWI). Finally, the corn leaves spectral which obtained under the same experimental conditions in other year were selected for verification to confirm the stability and effectiveness of NCSVI. The results show that among the 11 sub-band intervals, only the four sub-band intervals of a green peak, red edge, near the valley, and near peak A, the absolute value of the correlation coefficient between NCSVI and Cu2+ contents of corn leaves were higher than 0.9, respectively to -0.94, -0.97, -0.94，-0.96, as for RMSE, the root mean square error were less than 15, reached to 12.57, 8.71, 12.71 and 10.06. However, the highest correlation coefficient of WBI, MCARI and NDWI only reached to 0.75. The smallest RMSE was 24.21. Indicating that NCSVI corresponded to the four subintervals had a better indicator of copper pollution in corn leaves. The above results were verified by corn experiments under the same conditions in a different year, and it was found that among the 11 subintervals, only four subintervals of a green peak, red edge, near the valley, and near peak A had its absolute value of the coefficient R between NCSVI and the contents of Cu2+ in corn leaves were greater than 0.9, respectively to -0.9, -0.97, -0.97 and -0.93, as for RMSE, the root mean square error were less than 1.55, reached to 1.50, 0.85, 0.78 and 1.29, which were higher than WBI, MCARI and NDWI, and with the same sensitive sub-band intervals in the experiment of 2016, indicating that NCSVI could detect the sensitive range of corn leaves spectral stressed by Cu2+, with the characteristics of high efficiency and good stability. The NCSVI index proposed in this paper can be used as a method to monitor copper pollution in corn leaves, and provide some theoretical supports for the research of heavy metal pollution in other crops.
Key words：Hyperspectral remote sensing; Corn leaves; Heavy metal pollution; New copper stress vegetation index; Spectral sensitive interval
夏 天，杨可明，冯飞胜，郭 辉，张 超. 新型铜胁迫植被指数NCSVI探索铜污染下玉米叶片光谱敏感区间[J]. 光谱学与光谱分析, 2021, 41(08): 2604-2610.
XIA Tian, YANG Ke-ming, FENG Fei-sheng, GUO Hui, ZHANG Chao. A New Copper Stress Vegetation Index NCSVI Explores the Sensitive Range of Corn Leaves Spectral Under Copper Pollution. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2604-2610.
[1] Yin C Q, Sun Q B, Zhao X Q. Advanced Materials Research, 2012, 599: 434.
[2] YANG Hai, HUANG Xin, LIN Zi-zeng, et al(杨 海, 黄 新, 林子增, 等). Applied Chemical Industry(应用化工), 2019, 48(6): 1417.
[3] LIANG Ya-ya, YI Xiao-yun, DANG Zhi, et al(梁雅雅, 易筱筠, 党 志, 等). Journal of Agro-Environment Science(农业环境科学学报), 2019, 38(1): 103.
[4] Li Z, Ma Z, van der Kuijp T J, et al. Science of the Total Environment, 2014, 468-469: 843.
[5] LI Ting, LIU Xiang-nan, LIU Mei-ling(李 婷, 刘湘南, 刘美玲). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(12): 176.
[6] Asmaryan S, Warner T A, Muradyan V, et al. Remote Sensing Letters, 2013, 4 (2): 200.
[7] 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.
[8] Rathod P H, Brackhage C, Meer F D V D, et al. European Journal of Remote Sensing, 2015, 48(3): 283.
[9] LIU Cong, YANG Ke-ming, XIA Tian, et al(刘 聪, 杨可明, 夏 天, 等). China Environmental Science(中国环境科学), 2017, 37(10): 3952.
[10] GUO Hui, YANG Ke-ming, ZHANG Chao(郭 辉, 杨可明, 张 超). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(10): 153.
[11] Newete S W, Erasmus B F N, Weiersbye I M, et al. International Journal of Remote Sensing, 2014, 35(3): 799.
[12] Lim J, Yu J, Wang L, et al. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4870.
