中文摘要：

应用可见／短波近红外光谱分析测量土壤碱解氮和速效钾含量。为了提高该分析方法的预测精度，消除无信息建模变量对模型稳定性的影响，原始光谱平滑后采用蒙特卡罗无信息变量消除方法（MC—UVE）对土壤碱解氮和速效钾的建模变量进行筛选，应用偏最小二乘方法（PLS）建立校正模型。对于碱解氮模型，采用MC—UVEPLS方法，建模变量减少为210，相关系数和预测均方差分别为0．84和17，1mg／kg。对于速效钾的预测模型，采用MC—UVE方法后，建模变量减少为150，模型的预测相关系数为0．76，预测均方根误差为15．4mg／kg。

英文摘要：

Visible/near-infrared spectroscopy （Vis/NIRS） was investigated for determination of soil properties, namely, available nitrogen （N） and available potassium （K）. In order to improve the predictive precision and eliminate the influence of uninformative variables for model robustness, Monte Carlo uninformative variables elimination （ MC - UVE） methods were proposed for variable selection in available N and available K NIR spectral modeling. Partial least squares （PLS） models analysis was implemented for calibration models. The modeling variable number was reduced to 210 from 751 for available N calibration model and 150 for available K calibration model. The performance of the model was evaluated by the correlation coefficient （R） , RMSEP. The optimal MC - UVE PLS models were achieved, and R, RMSEP were 0. 84, 17. 1 mg/kg for N and 0.76, 15.4 mg/kg for K, respectively