矿产资源对工业和国民经济的发展有重要的作用, 但是随着矿业开采规模的扩大, 资源枯竭、 经营不善而形成的矿业废弃地越来越多。 由于长时间受到采矿的影响, 矿业废弃地土壤中存在大量的重金属元素, 高浓度重金属可能会对环境和人体产生影响。 土地复垦是整治污染、 退化土壤再利用的重要方法, 对重构后的土壤进行重金属含量检测是衡量土地复垦成效的重要指标, 需要长期进行跟踪监测。 传统的化学检测方法效率低、 成本高、 无法实现重金属大范围检测。 高光谱是一种新兴的、 发展潜力巨大的技术, 在环境保护, 资源利用, 区域可持续发展等方面有着广泛的应用。 经过近几十年的快速发展, 仪器精度逐渐提高, 检测方法逐渐成熟, 为实现土壤重金属高效、 便捷检测提供了可能。 正常土壤重金属含量一般相对较低, 采用光谱测量重金属含量较为困难, 但铁矿开采区矿业废弃地由于土壤中的铁元素较多, 会使土壤中的重金属的存在和聚集形式发生变化, 影响重金属对光谱的响应, 从而使土壤光谱反射率与重金属含量之间关系更加明显。 以湖北省大冶市复垦矿区研究区, 采样化学检测方法获取土壤重金属（As, Cr, Zn）含量; 借助于美国ASD公司生产的FieldSpec4地物光谱仪（350～2 500 nm）获取土壤反射率, 应用一阶微分、 倒数对数、 连续统去除法分别对反射率曲线进行预处理, 提取出光谱特征波段, 分析三种重金属元素与光谱特征间的相关性并建立逐步回归模型。 研究表明, 光谱数据预处理可使光谱特征波段更加明显, 其中一阶微分和连续统去除法的效果最为明显。 3种重金属元素的特征波段为495, 545, 675, 995, 1 425, 1 505, 1 935, 2 165, 2 205, 2 275和2 355 nm。 将土壤重金属含量与光谱特征波段之间做相关性分析, 三种重金属都表现出了与光谱曲线的相关性, 相关系数大部分都达到了0.5以上, 最大相关系数为0.663, 由于重金属种类和预处理方式的不同会导致相关性系数存在明显的差异。 利用与土壤重金属相关性最大的特征波段建立三种重金属反演模型, 并以反演模型r大小选择每种重金属的最优反演模型。 由于重金属种类的不同, 模型的选择也有差异, Cr和Zn一阶微分逐步回归为最佳反演模型, 重金属As连续统去除法逐步回归为最佳反演模型。 通过检验, 三种重金属中Cr反演效果最好, RMSE为2.67, 其次是Zn和As。 对比当前不同检测手段可知, 基于土样和光谱数据预处理的土壤重金属含量地物光谱仪高光谱反演是比较理想的。 可为矿业废弃地土壤重金属高光谱反演提供参考。

Mineral resources play an important role in the development of industry and national economy. However, with the expansion of mining scale, more and more abandoned mining land is formed due to resource depletion and poor management. Due to the prolonged mining impact, a large amount of heavy metal elements are present in the soils of mining wastelands. In such contaminated areas, high levels of heavy metals may have an impact on the environment and the human body. Land reclamation is an important method for remediation of contaminated and degraded soils. The detection of heavy metal content in the reconstructed soils is an important indicator of land reclamation efficiency and requires long-term follow-up and monitoring. The traditional chemical detection methods are inefficient and costly, and can not detect a wide range of heavy metals. Hyperspectral technology is a new technology with great potential for development and has a wide range of applications in environmental protection, resource utilization and regional sustainable development. After the rapid development in recent decades, the accuracy of instruments has been gradually increased, and the detection methods have gradually become mature, so as to realize the high efficiency of soil heavy metals. Easy detection provides a new way. Normal soil heavy metal content is generally relatively lower, and the use of spectral techniques to measure heavy metal content is more difficult, but mining iron ore mining area due to the soil more iron, will make the soil heavy metals in the form of existence and aggregation changes, impact the response of heavy metals to the spectra, and make the correlation between soil spectral reflectance and heavy metal content even more pronounced. The contents of heavy metal (As, Cr, Zn) in soils were obtained by sampling chemical detection method in the study area of reclamation mining area in Daye City, Hubei Province. The soil reflectance was obtained by means of FieldSpec4 spectrophotometer (350～2 500 nm) First-order differential, reciprocal logarithm, and continuous unmixing method were used to preprocess the reflectance curve respectively, and the spectral characteristic bands were extracted. The correlations between the three heavy metal elements and spectral features were analyzed and a stepwise regression model was established. The results showed that compared with the general soil, spectral data preprocessing could make spectral characteristic bands more obvious, of which the first-order differential and continuous removal were the most obvious. The characteristic bands of the three heavy metal elements were 495, 545, 675, 995, 1 425, 1 505, 1 935, 2 165, 2 205, 2 275 and 2 355 nm. Correlation analysis between soil heavy metal content and spectral characteristic bands showed that all the three heavy metals showed correlation with spectral curve, and most of the correlation coefficients reached above 0.5 and the maximum correlation coefficient was 0.663, and different heavy metal species and treatment methods led to significant differences in the correlation coefficients. Three heavy metal inversion models were established based on the characteristic bands with the highest correlation with heavy metals in soil. The optimal inversion model for each heavy metal was selected based on the size of inversion model r. Because of different selection of heavy metal species, Cr, Zn First-order differential step-by-step regression was the best inversion model, and heavy metal As continuous removal method gradually regression was the best inversion model. Through the test, Cr in the three kinds of heavy metals was the best, and RMSE is 2.67, followed by Zn, and As is the worst. Comparing the current different detection methods, we can see that hyperspectral inversion of soil heavy metal content spectrometer based on soil samples and spectral data pretreatment is ideal. The related research results can provide reference for the hyperspectral inversion of heavy metals in mining-abandoned soils.