摘要
在现代农业与环境科学的交叉领域,受重金属污染的农作物光谱特性变化研究正逐渐成为热点话题。当农作物遭受重金属污染后,其内部的生理结构与生化成分会发生改变,这种改变会直接反映在光谱特征上,光谱变化所产生的变异信息由此成为了重金属污染监测极为关键的依据。本研究通过在实验室室内设置不同污染浓度的重金属铜铅玉米盆栽实验,测定了在不同浓度梯度的铜铅污染环境下玉米叶片的反射率光谱数据,以及玉米叶片中铜铅含量等关键数据,进而构建起一套涵盖全面、数据详实且专属于重金属铜铅污染玉米植株的完整数据集。并且聚焦于玉米叶片光谱,从频率域的独特视角切入,对其全波段以及子波段展开深入探究。通过创新性地结合时频分析方法,提出了一种名为叶片敏感光谱区间探测法(SIDM)。基于SIDM,进一步提出了叶片光谱的变异特征参数(SVCP),这些参数犹如农作物受污染状况的“生物标记”,对于研究变异特征参数与叶片重金属含量之间的内在关联有着重要意义。同时,将其与常规光谱指数对比,探寻对铜铅污染敏感的光谱区间。在此基础上,巧妙地结合非线性时频分布构建了叶片光谱变换方法(STM)。经过实验验证,STM能够清晰地区分不同铜铅元素污染类别。SIDM成功地将叶片铜铅污染弱信息进行有效增强与精准提取,使得原本微弱且难以察觉的污染信号清晰地展现出来。更为重要的是,找到了对铜铅污染具有高度特异性的光谱区间,这为后续开发更为精准高效的重金属污染监测技术奠定了坚实的基础。而STM则在区分有无重金属污染的光谱差异方面具有优势,并且能够直观地将玉米受铜铅污染的元素类别区分开来,有效推动了利用光谱技术进行农作物重金属污染监测领域的发展进程。
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,specifical ly copper and lead,in the laboratory.It measured the reflectance spectra of maize leaves under variou 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.
作者
胡麟臻
夏天
张超
杨可明
高学正
李晓蕾
万明明
HU Lin-zhen;XIA Tian;ZHANG Chao;YANG Ke-ming;GAO Xue-zheng;LI Xiao-lei;WAN Ming-ming(Development and Research Center,China Geological Survey,Beijing 100037,China;School of Earth Science and Resources,China University of Geosciences(Beijing),Beijing 100083,China;National Geological Archives of China,Beijing 100037,China;China Centre for Resources Satellite Data and Application,Beijing 100094,China;College of Geoscience and Surveying Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;China University of Geosciences(Wuhan),Wuhan 430074,China;Hainan Geological Data Institute,Haikou 570206,China)
出处
《光谱学与光谱分析》
北大核心
2025年第9期2658-2665,共8页
Spectroscopy and Spectral Analysis
基金
海南省地质资料数据专题服务试点项目([2024]407)
地质资料集成研究与社会化服务(DD20240100)
国家自然科学基金项目(41971401)
国家重点研发计划项目(2019YFC1904304)资助。
关键词
重金属污染
农作物
叶片光谱
弱信息
元素区分
Heavy metal pollution
Crops
Leaf spectra
Weak information
Element differentiation
作者简介
胡麟臻,1984年生,中国地质调查局发展研究中心高级工程师,e-mail:3774629918@qq.com;通讯作者:张超,e-mail:1581006343@qq.com。
