重金属污染是土壤环境污染中亟待解决的问题之一, 重金属通过土壤向植物富集, 危及人体健康, 对生态环境产生巨大隐患。 传统的土壤污染监测以化学方法为主, 不仅费时费力且监测范围有限, 而基于植被高光谱技术的土壤重金属监测方法能够快速准确地获取土壤重金属含量, 突破植被屏障, 提高土壤重金属监测效率。 近年来, 国内外许多学者致力于使用盆栽实验定量研究土壤污染物对植物光谱特征影响, 而野外环境下的实验研究相对缺少, 因此建立合适准确的野外土壤重金属预测模型具有重要意义, 为改善耕地土壤质量提供参考。 以北京市优势经济果树桃树为研究对象, 在研究区均匀设置了50个采样点, 利用FieldSpec 4便携式地物波谱仪测量桃树叶片光谱数据, 同时采集土壤样本带回实验室检测分析获得土壤重金属含量数据。 通过分析不同污染下桃树叶片在重金属胁迫下的叶片光谱特征, 计算不同土壤重金属与叶片光谱之间的相关关系, 确定土壤As元素与光谱反射率相关性更大, 因此选择土壤As元素计算其与不同植被指数之间的相关系数, 并用合适的植被指数构建土壤As元素预测模型。 结果表明: 污染区桃叶光谱反射率总体上比背景区的光谱反射率更高, 其中760~1 300 nm波长范围内对土壤重金属更加敏感, 土壤重金属对叶片红、 蓝、 黄边位置干扰不明显, 对红、 蓝、 黄边斜率敏感, 且均呈正向相关性。 光谱反射率与土壤Cr, Cu和Hg元素相关性较弱, 与As, Pb和Cd元素在某些波段范围内达到0.1级显著相关, 且总体相关曲线趋势相同, 相关性大小依次排序为As>Pb>Cd。 以相关性更强的土壤As元素与植被指数进行相关分析表明, 土壤As元素与PRI1和PRI3均显著相关。 使用SPSS数据分析软件以PRI1和PRI3为自变量, 土壤As元素为因变量分别进行回归分析, 检测结果发现, PRI3的指数预测模型( y=e^{43.644 x-39.386}, R²=0.937, RMSE=0.161)效果最好且具有更好的稳定性。

Heavy metal pollution constitutes one of the most urgent problems in soil environmental pollution, as plants become enriched in heavy metals through the soil, which endangers human health and poses a great potential danger to the ecological environment. The monitoring over heavy metal pollution in soil by traditional chemical methods is time-consuming and laborious and limited in scope. However, the method for monitoring heavy metal in soil leveraging hyperspectral vegetation technology is capable of quickly and accurately obtaining the heavy metal content in the soil, breaking through the vegetation barrier, and making the monitoring more efficient. Providing an important reference for the monitoring over and early warning of heavy metal elements in soil, this method matters for achieving the goal of constructing ecological civilization into a higher level and improving the quality of arable land. In this study, peach trees, the dominant economic fruit tree in Beijing, were research targets. 50 sampling points were evenly set up in the study area, and the spectral data of peach tree leaves were measured by using FieldSpec 4 portable ground wave spectrometers, while soil samples were collected and brought back to the laboratory for testing and analysis to obtain the data of heavy metal content in the soil. Efforts were made to analyse the leaf spectral characteristics of peach tree leaves under the stress of heavy metals in soil in different kinds of pollution and investigate how different soil heavy metals are correlated with leaf spectra through calculation. It was determined that element As in soil had a higher correlation with spectral reflectance. As a result, we calculated the correlation coefficients between element As in soil and vegetation indices, and construct a prediction model for elements As in soil using the appropriate vegetation indices. The results show that the spectral reflectance of peach leaves in the polluted area was generally higher than that in the background area and was more sensitive to heavy metals in soil in the wavelength range of 760~1 300 nm. The heavy metals in soil did not interfere considerably with the position of the red, blue and yellow edges of the leaves and were sensitive to the slope of the red, blue and yellow edges, and all of them were positively correlated. Spectral reflectance was weakly correlated with elements Cr, Cu and Hg in soil, and 0.1 level of significant correlation was reached with elements As, Pb and Cd in some wavelength ranges. The overall correlation curve trend was the same, with the correlation magnitude ranked as As>Pb>Cd in order. According to the above studies, it is found that As elements in soil have the strongest correlation. Therefore, we performed correlation analysis using As elements in soil and vegetation index, which showed that As elements were significantly correlated with both PRI1 and PRI3. The regression analysis was performed using SPSS data analysis software with PRI1 and PRI3 as independent variables and As in soil as a dependent variable. The test results show that the index prediction model of PRI3 ( y=e^{43.644 x}-39.386, R²=0.937, RMSE=0.161) rendered the best results and was more stable.