土壤有机质含量田间实时测定方法

Real-time Measurement of Soil Organic Matter Content in Field

为了实现对土壤有机质含量的快速测定,以关中塿土为材料,研究基于光谱分析的土壤有机质含量测定方法。首先用机载便携式近红外频谱仪采集土壤样本在波长900-1 700 nm范围的漫反射光谱,并对异常样本进行判别和剔除以提高建模精度,在比较2种不同样本划分方法对模型影响的基础上,用连续投影算法（SPA）对建模变量进行最优波长选择,然后通过3种线性建模方法对有机质含量预测结果进行分析,探明偏最小二乘法（PLS）方法效果最好,并建立了径向基（RBF）神经网络预测模型。测试集样本实验结果表明,用PLS建立的预测模型有机质含量测定值和预测值之间的决定系数为0.801 9,均方根误差为0.179 4;用RBF神经网络建模的决定系数和均方根误差分别为0.828 1和0.164 6,两种模型均具有较高的精度,可对有机质含量进行快速预测。

In order to achieve rapid measurement of soil organic matter content, Lou soil was prepared to study soil organic matter determination method based on spectral analysis. Firstly, the soil diffusion reflectance spectrum with range of 900 - 1 700 nm was collected by using the portable spectrograph, and the abnormal samples were identified and removed to improve the accuracy. After that, based on the comparison of two different sample dividing methods, the optimal wavelengths of the modeling variables were selected by using successive projections algorithm （SPA）. Then, the effect of prediction results of organic matters was analyzed with three linear modeling methods （MLR, PCR, PLS）. The results indicated that PLS worked better. The RBF neural network was also built. The results of testing sets showed that the coefficient of determination and root mean square error between measured value and predicted value was 0. 801 9 and 0. 179 4 with PLS model, and 0. 828 1 and 0. 164 6 with RBF neural network, respectively. Both of them showed a high accuracy which could be used for the fast prediction of the soil organic matter content.