中文摘要：

利用在实验室获取的矿区农田土壤可见-近红外反射光谱与土壤As污染浓度、Fe和有机质含量数据构建了As和Fe浓度及有机质含量的PCR与PLSR预测模型;为提高模型的稳定性和预测能力,对原始光谱数据进行了预处理即,一和二阶微分（F/SD）、基线校正（B）、变量标准规一化（SNV）、多次散射校正（MSC）和连续统去除（CR）。研究表明：变量标准规一化（SNV）、多次散射校正（MSC）和连续统去除（CR）分别对As,Fe和OM的PCR模型的预测能力有明显的改善（各PCR模型的因子数、相对RMSEP和R2分别5,0.3040,0.3685;3,0.1443,0.4762和3,0.1712,0.408 4）。预测As和Fe浓度及有机质含量的最优PCR模型使用了一些共同的波段：450,1000,1400,1900,2050,2200,2250,2400和2470 nm。因此,可以通过遥感技术来检测土壤污染物浓度及其他物质含量,从而为土壤环境质量的遥感监测提供参考。

英文摘要：

Soil samples in the depth from 0 to 20 cm were scooped from agricultural region beside mines and prepared for determination of As concentration,Fe concentrations and organic matter content.At the same time they were scanned by mobile hyperspectral radiometer for visible and near-infrared spectra.Savitzky-Golay filter was used to smooth noises in spectrum curve because of some low signal-to-noise ratios in some regions of visible and near-infrared light,and all the spectra were resampled with the spectral interval of 10 nm.Before principal component regression and partial least square regression models were constructed for predicting As concentration,Fe concentrations and OM content,several spectral preprocessing techniques like first/second derivative（F/SD）,baseline correction（B）,standard normalized variate（SNV）,multiplicative scatter correction（MSC） and continuum removal（CR） were used for promotion of models＇ robustness and predicting performance.For limited samples,cross validation was carried out by repeated leave-one-out procedure,and root mean square error of prediction（RMSEP） was used for validating the prediction ability of constructed models.In this study principal component regression models behave better than partial least square regression models in representing regressing ability,reducing risk of over-fitting with less factors and ensuring models＇ accuracy and pertinences（relative RMSEP and R2）.Preprocessing techniques of SNV,MSC and CR improve obviously the prediction ability of models for As concentration,Fe concentrations and OM content with relative RMSEP equal to 0.304 0,0.144 3 and 0.171 2,with number of factors equal to 5,3 and 3,respectively.The analysis of regression vectors of selected optimal PCR models shows that several important wavelengths are simultaneously taken and helpful for prediction performance： 450,1 000,1 400,1 900,2 050,2 200,2 250,2 400 and 2 470 nm.Application of the calibrated models to soil contamination of croplands is promising.Concentr