| 光谱学与光谱分析 |
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| The Inversion of Soil Alkaline Hydrolysis Nutrient Content with Hyperspectral Reflectance Based on Wavelet Analysis |
| LUAN Fu-ming1, 2, XIONG Hei-gang3*, WANG Fang1, 2, ZHANG Fang4 |
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 One hundred thirty for soil samples of Qitai in Xinjiang were selected, and the first derivative spectrum of the soil sample logarithmic reflectance was decomposed to many layers by using 4 wavelet functions respectively, and PLSR was used to establish the prediction models respectively, and precision values were tested. The results show that: 1～3 layers low-frequency coefficients of wavelet decomposition were better, while the rest were worse. In 6 layers of all function decomposition, the highest accuracy of inversion models constructed by low-frequency coefficients were all ca2, while with increasing the decomposition layers, the precision and significance decreased significantly. In the same scale, there was little accuracy difference between inversion models constructed by 4 wavelet functions low-frequency coefficients, while Bior1.3 was optimal. The best inversion model was ca2 that built by Bior 1.3, with R2 and RMSE being 0.977 and 7.51 mg·kg-1 respectively, reaching to significant level. Upon testing, it can be used to estimate the alkaline hydrolysis nitrogen content quickly and accurately.
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Received: 2013-01-05
Accepted: 2013-04-05
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Corresponding Authors:
XIONG Hei-gang
E-mail: xhg1956@sohu.com
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[1] Shepherd K D, Walsh M G. Soil Science Society of America Journal, 2002, 66: 988.
[2] Sullivan D G, Shaw J N, Rickman D, et al. Soil Science, 2005, 170(12): 954.
[3] PENG Jie, ZHANG Yang-zhu, ZHOU Qing, et al(彭 杰, 张杨珠, 周 清, 等). Chinese Journal of Soil Science(土壤通报), 2009, 40(5): 1204.
[4] LIU Wei, CHANG Qing-rui, GUO Man, et al(刘 炜, 常庆瑞, 郭 曼, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2011, 30(4): 316.
[5] Weng Y L, Gong P, Zhu Z L. Pedosphere, 2010, 20(3): 378.
[6] LUAN Fu-ming, ZHANG Xiao-lei, XIONG Hei-gang, et al(栾福明, 张小雷, 熊黑钢, 等), Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(1): 196.
[7] LI Jun, LI Pei-jun, GUO Jian-cong(李 军, 李培军, 郭建聪). Progress in Natural Science(自然科学进展), 2007, 17(11): 1500.
[8] 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, 41(1): 27.
[9] LIANG Liang, YANG Min-hua, ZANG Zhuo(梁 亮, 杨敏华, 臧 卓). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(12): 248.
[10] LIAO Qin-hong, GU Xiao-he, LI Cun-jun, et al(廖钦洪, 顾晓鹤, 李存军, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(23): 132.
[11] LIU Wei, CHANG Qing-rui, GUO Man, et al(刘 炜, 常庆瑞, 郭 曼, 等). Agricultural Research in the Arid Areas(干旱地区农业研究), 2010, 28(5): 241.
[12] CHEN Hong-yan, ZHAO Geng-xing, LI Xi-can, et al(陈红艳, 赵庚星, 李希灿, 等). Chinese Journal of Applied Ecology(应用生态学报), 2011, 22(11): 2935.
[13] TANG Qi-yi, FENG Ming-guang(唐启义, 冯明光), DPS Data Processing System(DSP数据处理系统). Beijing: Science Press(北京：科学出版社), 2007. 901.
[14] ZHOU Kai-li，KANG Yao-hong(周开利, 康耀红). Neural Network Models and MATLAB Simulation Program 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.
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