北京典型耕作土壤养分的近红外光谱分析研究

以北京郊区试验田采集的72个土壤样品为实验材料,应用傅里叶变换近红外光谱技术,分析了土样的全氮、全钾、有机质和pH值养分含量。采用偏最小二乘法(PLS)对光谱数据与土壤养分实测值进行回归分析,建立预测模型,以模型决定系数(R2)、校正标准差(RMSECV)、预测标准差(RMSEP)和相对分析误差(RPD)作为模型精度的评价指标。分析结果:土壤的全氮模型R2为95.44%,RMSECV为0.014 1,RMSEP为0.016 8,RPD为4.68;有机质模型R2为89.63%,RMSECV为0.37,RMSEP为0.47,RPD为3.11;全钾模型R2为85.62%,RM-SECV为0.173,RMSEP为0.204,RPD为2.64;pH值模型R2为87.33%,RMSECV为0.031,RMSEP为0.053,RPD为2.81。

Analysis of Soil Nutrient Content Based on Near Infrared Reflectance Spectroscopy in Beijing Region

Fourier transform infrared spectroscopy techniques were used to predict total nitrogen,organic matter,total potassium and pH values of soil.With 72 soil samples collected from the experimental field in the suburbs of Beijing,the models were constructed using partial least-squares(PLS) regression based on the spectral data and measured soil nutrient.The model accuracy was evaluated using determination coefficient(R2),adjusted standard deviation(RMSECV),standard deviation of prediction(RMSEP),and residual prediction deviation(RPD).The prediction results showed that,soil total nitrogen model,R2=95.44%,RMSECV=0.0141,RMSEP=0.0168,RPD=4.68;soil organic matter model,R2=89.63%,RMSECV=0.37,RMSEP=0.47,RPD=3.11;soil total K model,R2 =85.62%,RMSECV=0.173,RMSEP=0.204,RPD=2.64;soil pH values model R2=87.33%,RMSECV=0.031,RMSEP=0.053,RPD=2.81.