PDF(Pubmed)
Abstract：
In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.
摘要：
在利用高光谱技术反演土壤多物种重金属元素浓度的研究中,特征波段的选择非常重要。然而,土壤元素之间的相互作用会导致光谱特征的冗余和不稳定性。在这项研究中,重金属元素(Pb，Zn,Mn,和As)在哈尔滨矿区周围的整体中，黑龙江省,中国,被研究过。为了优化光谱指数及其权重的组合，特征波段皮尔逊系数(RCBP)的雷达图用于筛选Pb的三波段光谱指数组合，Zn,Mn,作为元素，而Catboost算法用于反演每种元素的浓度。从浓度和特征带两个角度分析了铁与四种重金属的相关性，同时通过空间分析进一步评估了光谱反演的效果。发现基于优化的光谱指数组合反演Zn元素浓度的回归模型具有最佳拟合,对于测试集，R2=0.8786，其次是Mn(R2=0.8576)，As（R2=0.7916），和Pb(R2=0.6022)。就特征波段而言，铁与铅的最佳相关性，Zn,Mn和As元素分别为0.837、0.711、0.542和0.303。As和Mn元素的光谱反演浓度与实测浓度的空间分布和相关性是一致的,Zn和Pb的测定结果存在一定差异。因此,高光谱技术和Fe元素的分析在重金属浓度的反演中具有潜在的应用，可以提高这些土壤的质量监测效率。
