1. College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China 2. Resource and Environmental Science Data Center, Chinese Academy of Sciences, Beijing 100101, China 3. Key Laboratory of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing 100081, China
Abstract：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. Concentrations of soil contaminants and contents of other matter can be determined by reflectance spectroscopy with high spectra resolution, which would provide potent reference for remote sensing monitoring of soil and environmental quality.
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