Study on Hyperspectral Inversion Model of Soil Heavy Metals in Typical Lead-Zinc Mining Areas
WU Yan-hua1, ZHAO Heng-qian1, 2*, MAO Ji-hua1, JIN Qian3, 4, WANG Xue-fei3, 4, LI Mei-yu1
1. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
2. State Key Laboratory of Coal Resources and Safe Mining (China University of Mining and Technology), Beijing 100083, China
3. Hebei Provincial Geological Experiment and Testing Center, Baoding 071051, China
4. Hebei Provincial Key Laboratory of Mineral Resources and Ecological Environment Monitoring, Baoding 071051, China
Abstract：Soil heavy metal pollution caused by mining in mining areas seriously affects crop yield and causes human diseases. It is necessary to prevent soil heavy metal pollution from damaging health. Hyperspectral remote sensing can rapidly and dynamically acquire continuous spectra signals of ground objects, which provides a new idea for developing soil heavy metal content monitoring based on remote sensing. Aiming at the typical lead-zinc mining area in Laiyuan County, Hebei Province, soil samples from the mining area and surrounding areas are collected on-site, and the reflectance spectra of soil were obtained using SVC HR-1024i spectrometer (350~2 500 nm). Through the spectral data smoothing, first derivative (FD), multivariate scattering correction (MSC), standard normal variate transform (SNV), first derivative after multivariate scattering correction (MSC+FD), and first derivative after standard normal variatetransform (SNV+FD), six kinds of spectral transformations were performed. The difference index (DI), ratioindex (RI), and normalizeddifference index (NDI) methods were used to extract the spectral indices from the six pretreated data. The contents of heavy metals cadmium (Cd), lead (Pb) and zinc (Zn) in soil were obtained through laboratory chemical testing and analysis. Different spectral transformation methods pretreated different heavy metals. The optimal spectral transformation methods for heavy metal elements were obtained. The difference index, ratio index, and normalized vegetation index were used to extract the optimal band combination under different spectral indices to get the optimal independent variables for modeling different heavy metals. The inversion models of heavy metal elements were constructed based on random forest and partial least square method. The research indicated that the noise could be effectively reduced, and the spectral characteristics were enhanced by pretreatment of spectral data. The results showed that the correlation between the spectral data and the heavy metal content was improved after the pretreatment. The optimal independent variables for different heavy metal elements were selected to increase the practical features of inversion modeling. Random forest algorithm and partial least squares regression method were used to establish prediction models for three heavy metals: cadmium (Cd), lead (Pb), and zinc (Zn). The R2 of the optimal models reached 0.90, 0.91, and 0.84, respectively, which confirmed the validity of this research method. This study can provide a basis for the inversion modeling of soil heavy metal content in lead-zinc mining areas and a method reference for detecting soil heavy metal content in mining areas.
Key words：Mining area; Heavy metal pollution; Spectral transformation; Spectral index; Inversion model
吴艳花，赵恒谦，毛继华，金 倩，王雪飞，李美钰. 典型铅锌矿区土壤重金属含量高光谱反演模型研究[J]. 光谱学与光谱分析, 2024, 44(06): 1740-1750.
WU Yan-hua, ZHAO Heng-qian, MAO Ji-hua, JIN Qian, WANG Xue-fei, LI Mei-yu. Study on Hyperspectral Inversion Model of Soil Heavy Metals in Typical Lead-Zinc Mining Areas. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1740-1750.
[1] Li G, Li M, Zhang X, et al. International Journal of Mining Science and Technology, 2022，32(3): 563.
[2] YANG Li，BAI Zong-xu，BO Wen-hao，et al(杨 雳，白宗旭，薄文浩，等). Environmental Science(环境科学), 2023，doi:10.13227/j.hjkx.202305253.
[3] DAI Heng-mei，YANG Li，GUO Zi-yu，et al(代恒美，杨 力，郭子毓，等). Journal of Environment and Health（环境与健康杂志）, 2023, https://link.cnki.net/urlid/12.1095.r.20230907.1344.004.
[4] Zhao R, Du S H. Remote Sensing, 2022, 14(9).
[5] ZHENG Miao，WANG Xiang，LI Si-jia，et al(郑 淼，王 翔，李思佳，等). Scientia Geographica Sinica(地理科学), 2022, 42(8): 1336.
[6] XU Yi，SHAO Guang-cheng，DING Ming-ming，et al(徐 艺，邵光成，丁鸣鸣，等). Journal of Drainage and Irrigation Machinery Engineering(排灌机械工程学报), 2022，40(11): 1159.
[7] YE Miao，ZHU Lin，LIU Xu-dong，et al(叶 淼，朱 琳，刘旭东，等). Environmental Science(环境科学), 2023, https://doi.org/10.13227/j.hjkx.202304100.
[8] CHEN Wei，XU Zhan-jun，GUO Qi(陈 玮，徐占军，郭 琦). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2022，38(8): 98.
[9] NI Bin，HUANG Zhao-qiang，JIANG Miao，et al(倪 斌，黄照强，江 淼，等). Geology and Prospecting(地质与勘探), 2022, 58(6): 1307.
[10] GUO Yun-kai，CAO Xiao，XIE Xiao-feng，et al(郭云开，曹 骁，谢晓峰，等). Bulletin of Surveying and Mapping(测绘通报), 2022，(1): 44.
[11] ZHONG Liang, QIAN Jia-wei, CHU Xue-yuan, et al(钟 亮，钱家炜，储学远，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2023，39(5)：265.
[12] Su Y, Guo B, Lei Y Z, et al. Water, 2022, 14(18): 2784.
[13] LIU Hong-jun, NIU Teng, YU Qiang，et al(刘泓君，牛 腾，于 强，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(11): 3552.
[14] DUAN Shi-xuan，LI Yan-jun，CAO Yu-yuan，et al(段仕轩，李艳军，曹愈远，等). Acta Armamentarii(兵工学报), 2022, 43(8): 1779.
[15] Yang H F, Xu H, Zhong X N. Bulletin of Environmental Contamination and Toxicology, 2022, 108(4): 745.
[16] Cheng G, Zhang H R, Li H, et al. Journal of Earth Science, 2023, 34(4): 1068.
[17] NI Zhen, HU Chang-qin, FENG Fang(尼 珍，胡昌勤，冯 芳). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2008, 28(5): 824.
[18] ZHOU Yao, CHENG Yong-sheng，WANG Dan-ping，et al(周 瑶，成永生，王丹平，等). The Chinese Journal of Nonferrous Metals(中国有色金属学报), 2023, https://link.cnki.net/urlid/43.1238.tg.20230822.1745.001.
[19] LIU Hao，YANG Xi-zhen，ZHANG Bei，et al(刘 浩，杨锡震，张 蓓，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2023, 39(13): 131.
[20] Rhodes J S, Cutler A, Moon K R, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(9): 10947.
[21] Hou Y Y, Li J, Chen X B, et al. Laser Physics Letters, 2022, 19: 095204.
[22] Guan T Y, Lin Z H, Groves K, et al. Statistics and Computing, 2022, 32: 30.
[23] CHEN Rui-hua，WANG Yi-jing，ZHANG Jun-hua，et al(陈睿华，王怡婧，张俊华，等). Chinese Journal of Ecology(生态学杂志), 2023, 42(9): 2296.
