| 光谱学与光谱分析 |
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| Comparative Analysis of Soil Organic Matter Content Based on Different Hyperspectral Inversion Models |
| LUAN Fu-ming1, 2, ZHANG Xiao-lei1, 2*, XIONG Hei-gang3, 4, ZHANG Fang4, WANG Fang1, 2 |
1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. College of Art and Science, Beijing Union University, Beijing 100083, China
4. College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China |
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Abstract 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.
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Received: 2012-07-20
Accepted: 2012-09-20
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Corresponding Authors:
ZHANG Xiao-lei
E-mail: yangzp@ms.xjb.ac.cn
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