【摘要】 为探讨运用土壤光谱估算城市居民区土壤重金属含量的可能性,以上海闵行居民区土壤重金属Cu、Pb、Zn元素为研究对象,通过采集土壤样本,分析土壤光谱信息,构建基于高光谱的土壤重金属多元线性逐步回归(MLSR)和偏最小二乘回归(PLSR)模型。结果表明:通过倒数一阶和对数一阶微分变换能有效增强土壤重金属的光谱特征;土壤Cu、Pb和Zn元素最优波段分别出现在1 042.7 nm、706.84 nm和1404.8 nm处;从模型稳定性和精确性来看,PLSR模型较优于MLSR模型。土壤Cu、Zn元素验证RMSE值仅为研究区该重金属含量均值的10%左右,拟合精度高。与Cu、Zn元素相比,Pb元素决定系数R~2在0.64～0.88,模型稳定性较好。通过对光谱数据的预处理,采用偏最小二乘回归模型可有效提高估算城市居民区土壤重金属含量的精度。更多还原

【Abstract】 To explore the possibility of using soil spectral reflectance to estimate soil heavy metal content in urban residential area,this study chooses 30 soil samples of Cu,Pb and Zn in Minhang Residential area,Shanghai Province.Through the spectral factor transform to highlight its eigenvalues,constructed Multiple Linear Stepwise Regression(MLSR) model and Partial Least Squares Regression(PLSR) model based on spectral reflectance of soil heavy metals.The results show that the reciprocal first-order and the logarithmic first-order differential transformation can effectively enhance the heavy metal soil spectral characteristics.The best characteristic bands of Cu,Pb and Zn are 1042.7 nm、706.84 nm and 1404.8 nm.In terms of model stability and accuracy,PLSR model is better than MLSR model.The RMSE of Cu and Zn were only about 10% of the mean value of heavy metals in the study area,and the accuracy of the model was high.Compared with Cu and Zn,the R~2 of Pb is between 0.64～0.88 which with higher model stability.By preprocessing the spectral data,the partial least-squares regression can effectively improve the accuracy of estimating the heavy metal content in urban residential areas.更多还原