Exploration of Spectral Variation and Element Identification Under
Environmental Geological Data Simulation of Heavy Metal Pollution
HU Lin-zhen1, 3, 6, XIA Tian4, ZHANG Chao1, 2, 3*, YANG Ke-ming5, GAO Xue-zheng1, 3, LI Xiao-lei1, 3, WAN Ming-ming7
1. Development and Research Center, China Geological Survey, Beijing 100037, China
2. School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China
3. National Geological Archives of China, Beijing 100037, China
4. China Centre for Resources Satellite Data and Application, Beijing 100094, China
5. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
6. China University of Geosciences (Wuhan), Wuhan 430074, China
7. Hainan Geological Data Institute, Haikou 570206, China
Abstract：In the interdisciplinary field of modern agriculture and environmental science, the study of changes in the spectral characteristics of crops contaminated with heavy metals is gradually becoming a hot topic. When crops are contaminated with heavy metals, their internal physiological structure and biochemical composition change, which is directly reflected in their spectral characteristics. The variation information generated by spectral changes becomes a crucial basis for monitoring heavy metal pollution. This study conducted pot experiments on maize plants contaminated with different concentrations of heavy metals, specifically copper and lead, in the laboratory. It measured the reflectance spectra of maize leaves under various concentration gradients of copper and lead pollution, as well as key data such as the copper and lead content in maize leaves. A comprehensive, detailed, and specialized dataset was constructed for maize plants contaminated with heavy metals copper and lead. And focusing on the spectrum of maize leaves- from a unique perspective in the frequency domain, we will conduct an in-depth exploration of its Full spectral range and sub-spectral range. By innovatively combining time-frequency analysis methods, a method called leaf-sensitive Spectral Interval Detection Method (SIDM) was proposed. Based on SIDM, spectral Variation Characteristic Parameters (SVCP) for leaf spectra were further proposed, which serve as “biomarkers” for crop contamination status and are of great significance for studying the intrinsic correlation between variation characteristic parameters and leaf heavy metal content. Meanwhile, compare it with conventional spectral indices to explore the spectral range sensitive to copper and lead pollution. On this basis, a leaf Spectral Transformation Method (STM) was ingeniously constructed by combining a nonlinear time-frequency distribution. Through experimental verification, STM can clearly distinguish different types of copper and lead pollution. SIDM has successfully enhanced and accurately extracted weak information on copper and lead pollution in leaves, making the originally weak and difficult-to-detect pollution signals visible. More importantly, a highly specific spectral range for copper and lead pollution has been identified, laying a solid foundation for the development of more accurate and efficient heavy metal pollution monitoring technologies in the future. STM has advantages in distinguishing spectral differences between samples with and without heavy metal pollution, and can intuitively categorize the element types of maize contaminated with copper and lead, effectively promoting the development of spectral technology for monitoring heavy metal pollution in crops.
Key words：Heavy metal pollution; Crops; Leaf spectra; Weak information; Element differentiation
胡麟臻，夏 天，张 超，杨可明，高学正，李晓蕾，万明明. 环境地质资料模拟重金属污染下光谱变异与元素甄别探究[J]. 光谱学与光谱分析, 2025, 45(09): 2658-2665.
HU Lin-zhen, XIA Tian, ZHANG Chao, YANG Ke-ming, GAO Xue-zheng, LI Xiao-lei, WAN Ming-ming. Exploration of Spectral Variation and Element Identification Under
Environmental Geological Data Simulation of Heavy Metal Pollution. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2658-2665.
[1] Li Y, Yang K M, Cheng F, et al. European Journal of Remote Sensing, 2019, 52(1): 632.
[2] ZHANG Zi-peng, DING Jian-li, WANG Jing-zhe(张子鹏, 丁建丽, 王敬哲). Acta Optica Sinica(光学学报), 2019, 39(2): 0228003.
[3] JIANG Xue-qin, YE Qin, LIN Yi, et al(姜雪芹, 叶 勤, 林 怡, 等). Acta Optica Sinica(光学学报), 2017, 37(10): 1028001.
[4] 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.
[5] FU Ping-jie, YANG Ke-ming, LIU Pu-dong(付萍杰, 杨可明, 刘浦东). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(9): 2875.
[6] CHENG Long, YANG Ke-ming, WANG Xiao-feng, et al(程 龙, 杨可明, 王晓峰, 等). China Environmental Science(中国环境科学), 2018, 38(1): 340.
[7] Liu N, Zhao R M, Qiao L, et al. Sensors, 2020, 20(14): 3995.
[8] GAO Wei, YANG Ke-ming, CHEN Gai-ying, et al(高 伟, 杨可明, 陈改英, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(3): 173.
[9] WU Qi, SUN Yang-ming, SANG Jun-wei, et al(邬 奇, 孙扬名, 桑俊伟, 等). Jiangsu Agricultural Sciences(江苏农业科学), 2024, 52(3): 47.
[10] GUO Hui, DAI Zhi-lin, SHI Hai(郭 辉, 戴志林, 石 海). Journal of Anhui Science and Technology University(安徽科技学院学报), 2024, 38(1): 24.
[11] WANG Fei, XU Wei(王 非, 徐 伟). Journal of Electronic Measurement and Instrumentation(电子测量与仪器学报), 2023, 37(10): 32.
[12] GAO Xue-zheng, KONG Zhao-yu, SHANG Yun-tao, et al(高学正, 孔昭煜, 商云涛, 等). Geological Bulletin of China(地质通报), 2024, 43(10): 1881.
[13] FU Ping-jie(付萍杰). Acta Geodaetica et Cartographica Sinica(测绘学报), 2022, 51(2): 312.
[14] Zhang C, Yang K M, Gao W. Remote Sensing Letters, 2020, 11(12): 1147.
[15] Newete S W, Erasmus B F N, Weiersbye I M, et al. International Journal of Remote Sensing, 2014, 35(3): 799.
