Luan Fu-Ming, Zhang Xiao-Lei, Xiong Hei-Gang, Zhang Fang, Wang Fang
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Jan;33(1):196-200.
The present paper, based on the Qitai county of Xinjiang, selected 40 soil samples, and used two methods respectively, i.e. multiple linear stepwise regression (MLSR) and artificial neural network (ANNs), to establish the inversion and predieting model of soil organic matter (SOM) content and the model test from measured reflectance spectra and relative test were carried through to the models. Through quantitative analysis, the conclusions can be drawn as follows that the precision values of the different models vary from one to another, the model fitting effects order from high to low is that the integrated model for artificial neural networks (ANNs) is best, single artificial neural networks (ANNs) model is better, while stepwise multiple regression (MLSR) models are worse. Artificial neural networks (ANNs) has the strong abilities of linear and nonlinear approximation, while its integrated model for artificial neural networks (ANNs) is an important way to improve the inversion accuracy of soil organic matter (SOM) content, with the correlation coefficient up to 0.938, root mean square error and total root mean square error are minimum, being 2.13 and 1.404 respectively, and the predictive ability of the soil organic matter (SOM) content are very close to the measured spectrum, so the analysis results can achieve a more practical prediction accuracy for the best fitting model.
本文以新疆奇台县为研究区域，采集了40个土壤样本，分别运用多元线性逐步回归（MLSR）和人工神经网络（ANNs）两种方法，建立了土壤有机质（SOM）含量的反演预测模型，并对模型进行了实测光谱模型检验及相关检验。通过定量分析，得出以下结论：不同模型的精度值各不相同，模型拟合效果从高到低依次为：人工神经网络（ANNs）集成模型最佳，单人工神经网络（ANNs）模型次之，逐步多元回归（MLSR）模型较差。人工神经网络（ANNs）具有很强的线性和非线性逼近能力，其人工神经网络（ANNs）集成模型是提高土壤有机质（SOM）含量反演精度的重要途径，相关系数高达0.938，均方根误差和总均方根误差最小，分别为2.13和1.404，土壤有机质（SOM）含量的预测能力与实测光谱非常接近，因此分析结果表明最佳拟合模型能达到更实际的预测精度。
