基于近红外光谱及 BP 神经网络分析法磁预测森林土壤有机碳含量

为快速测定森林土壤的有机碳含量，从取自小兴安岭带岭林业局东方红林场的120个土壤样品中采集350～2500 nm的土壤近红外光谱数据，对光谱做一定的预处理后，运用主成分分析法压缩提取前8个主成分，结合BP神经网络非线性方法建立土壤有机碳含量的预测模型并进行验证。结果表明，验证集的相关系数为0.78002，均方根误差为0.5002，预测集的相关系数为0.84941，均方根误差为0.4538。应用近红外光谱技术及BP神经网络非线性方法建模可以有效地预测土壤的有机碳含量，为野外大面积快速测定森林土壤碳含量提供了技术依据。

Prediction of Forest Soil Organic Carbon Content based on NIRS and BP Neural Network

To rapidly determine forest SOC content , the spectra of 120 soils samples from Dongfanghong forest farm of Dailing Forestry Bureau located in the northeast Lesser Khingan Mountains were scanned with a vis -NIR spectrometer in the 350 to 2 500 nm after some pretreats , and the first eight principal components were compressed and gained by principal component analysis （ PCA ） . With combination of BP neural network nonlinear method , the prediction model of SOC content were established and validated .The result showed that the correlation coefficient （R） and root mean square error （RMSE） of validation and test set were 0.780 02, 0.849 41 and 0.500 2, 0.453 8 respectively .In this sense , NIR technology and BP neural network nonlinear method could be a good tool in the prediction of SOC content , and could provide a feasibility to determine forest SOC content in the field widely and quickly .