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Effect of particle size on prediction of soil TN with remote sensing based on NIR spectroscopy

机译:基于近红外光谱的粒度对土壤总氮预报的影响

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It is a feasible method to detect soil total nitrogen (TN) content with remote sensing based on NIR spectroscopy. However, the accuracy of soil TN model was affected seriously by soil particle size. The spectral scanning results showed that at the same soil TN content level, with the decrease of the soil particle size, the reflectance of soil samples was reduced and the trend was not linear relationship. At the short wavelength (760-1100 nm) wave bands, there were a little of differences; while at the long wavelength (1100-2500 nm) wave bands, there were great differences. Two methods were adopted to eliminate the effect of soil particle size. The first method was to establish TN model by the first order differential preconditioning method of the spectral data. The second method was to establish TN model with mixed calibration set of different particle size soil samples after data preprocessing. Through the combination of the two methods, The R_c, R_v, RMSEC, RMSEP and RPD of the model improved from 0.85, 0.31, 0.046, 0.132, 0.866 to 0.92, 0.86, 0.018, 0.091, 2.700 respectively. The results showed that the effect of soil particle size on prediction of soil TN can be eliminated effectively.
机译:基于近红外光谱的遥感检测土壤总氮含量是一种可行的方法。然而,土壤总氮模型的准确性受到土壤粒径的严重影响。光谱扫描结果表明,在相同的土壤总氮含量水平下,随着土壤粒径的减小,土壤样品的反射率降低,且呈非线性关系。在短波长(760-1100 nm)波段,差异很小。而在长波长(1100-2500 nm)波段,差异很大。采用了两种方法来消除土壤粒径的影响。第一种方法是通过光谱数据的一阶微分预处理方法建立TN模型。第二种方法是在数据预处理后,用不同粒径土壤样品的混合校准集建立TN模型。通过两种方法的组合,模型的R_c,R_v,RMSEC,RMSEP和RPD分别从0.85、0.31、0.046、0.132、0.866改进为0.92、0.86、0.018、0.091、2.700。结果表明,可以有效消除土壤粒径对预测土壤总氮的影响。

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