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
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.
[1] Alabbas A H，Swain P H，Baumgardner M F. Soil Sci.，1972，114(6)：477. [2] LU Yan-li，BAI You-lu, YANG Li-ping(卢艳丽, 白由路, 杨俐苹). Plant Nutrition and Fertilizer Science(植物营养与肥料学报)，2011, 17(2): 456. [3] ZHANG Fa-sheng, QU Wei, YIN Guang-hua(张法升，曲 威，尹光华，等). Chinese Journal of Applied Ecology(应用生态学报)，2010，21(4): 883. [4] ZHANG Liang-jun，CAO Jing，JIANG Shi-zhong(张良均，曹 晶，蒋世忠). The Practical Tutorial in Artificial Neural Network. Beijing: China Machine Press(北京：机械工业出版杜)，2007. 64，99. [5] XU Yong-ming，LIN Qi-zhong，WANG Lu，et al(徐永明，蔺启忠，王 璐，等). Acta Pedologica Sinica(土壤学报)，2006，43(5): 709. [6] LI Wei，ZHANG Shu-hui，ZHANG Qian， et al(李 伟，张书慧，张 倩，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)， 2007，23(1): 55. [7] LIU Huan-jun， ZHANG Bai，ZHAO Jun， et al(刘焕军，张 柏，赵 军，等). Acta Pedologica Sinica(土壤学报)，2007，44(1): 27. [8] Chang C W，Laird D A. Near-infrared Reflectance Spectroscopic Analysis of Soil C and N. Soil Science，2002，167: 110. [9] HAN Li-qun(韩力群). A Course in Artificial Neural Network(人工神经网络教程). Beijing: Beijing University of Posts and Telecommunications Press(北京: 北京邮电大学出版社)，2006. 85. [10] ZHOU Kai-li，KONG Yao-hong(周开利，康耀红). Artificial Neural Network Model and MATLAB Simulative Programme Design(神经网络模型及其MATLAB仿真程序设计). Beijing: Tsinghua University Press(北京：清华大学出版社)，2005. 68，158.
