Estimating model of soil total nitrogen content based on near-infrared spectroscopy analysis

Zhang Juanjuan; Tian Yongchao; Yao Xia; Cao Weixing; Ma Xinming; Zhu Yan

Volume 28 Issue 12

Jun. 2012

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Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2012 > 28(12): 183-188.

Zhang Juanjuan, Tian Yongchao, Yao Xia, Cao Weixing, Ma Xinming, Zhu Yan. Estimating model of soil total nitrogen content based on near-infrared spectroscopy analysis[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(12): 183-188.

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Estimating model of soil total nitrogen content based on near-infrared spectroscopy analysis

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Received Date: June 21, 2011
Revised Date: May 13, 2012
Published Date: June 14, 2012

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Abstract

Abstract

Soil total nitrogen content is regarded as an important index for soil fertility diagnosis and crop growth management direction, the rapid monitoring model and establishing optimum bands on soil nitrogen content is the key to swiftly obtain soil nutrient information and develop precision agriculture. Five types of soil (Paddy soil, Fluvo-aquic soil, Salinized fluvo-aquic soil, Saline soil, Dark soil with lime concretion) collected from the middle and eastern of China were measured in the near-infrared region (4 000-10 000 cm-1). According to molecular vibration characteristics of full spectrum in the near infrared region, 8 wave bands (the whole region, combination region, the first overtone region, the second overtone region including the combination of bands referred above, and the protein functional group bands) were designed for establishing calibration models adopting multiplicative scatter correction(MSC) method. The calibration models for nitrogen content in soil samples were established by Partial Least Square (PLS) regression. The results showed that the prediction effect using MSC spectra in the combination region of 4 000-5 500 cm-1 was the best, the coefficient of determination and the root mean square error of cross-validation (RMSECV) for the calibration model were 0.90 and 0.16, respectively. Testing of the monitoring models with independent data of different types of soil samples indicated that determination coefficient and RMSE of validation were 0.91 and 0.15, respectively; RPD was 3.40. So, near-infrared diffuse reflection spectroscopy analysis technology can swiftly estimate total nitrogen content, and taking the combination band (4 000-5 500 cm-1) as modeling regional can get better calibration effect.
- soils,
- nitrogen,
- near infrared spectroscopy,
- models,
- partial least squares

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References

[1]	李生秀. 关于土壤供氮指标的研究：I．对几种测定土壤供氮能力方法的评价[J]. 土壤学报，1990，27(3)：233－240.Li Shengxiu. Studies on the indices of soil nitrogen supplying capacities[J]. Acta Pedologica Sinica, 1990, 27(3): 233－240. (in Chinese with English abstract)
[2]	郑立华，李民赞，安晓飞，等. 基于近红外光谱和支持向量机的土壤参数预测[J]. 农业工程学报，2010，26(14)：81－87.Zheng Lihua, Li Minzan, An Xiaofei, et al. Forecasting soil parameters based on NIR and SVM[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(14): 81－87. (in Chinese with English abstract)
[3]	王静，刘湘南，黄方，等. 基于ANN技术和高光谱遥感的盐渍土盐分预测[J]. 农业工程学报，2009，25(12)：161－165．Wang Jing, Liu Xiangnan, Huang Fang, et al. Salinity forecasting of saline soil based on ANN and hyperspectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(12): 161－165. (in Chinese with English abstract)
[4]	石吉勇，邹小波，赵杰文，等. 近红外光谱技术快速无损诊断黄瓜植株氮、镁元素亏缺[J]. 农业工程学报，2011，27(8)：283－287.Shi Jiyong, Zhou Xiaobo, Zhao Jiewen, et al. Rapid and non-destructive diagnostics of nitrogen and magnesium deficiencies in cucumber plants by near-infrared spectroscopy[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(8): 283－287. (in Chinese with English abstract)
[5]	Chang C, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Science, 2002, 167(2): 110－116.
[6]	Reeves III J B, McCarty G W, Mimmo T. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils[J]. Environmental Pollution, 2002, 116(1): 277－284.
[7]	Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry[J]. Soil Science Society of America Journal, 1986, 50: 120－123.
[8]	Bernard G, Barthe`s. Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples[J]. Soil Biology and Biochemistry, 2008, 40(6): 1533－1537.
[9]	Brunet D, Bernoux M, Barthe`s B G. Comparison between predictions of C and N contents in tropical soils using a Vis-NIR spectrometer including a fibre-optic probe versus a NIR spectrometer including a sample transport module[J]. Biosystems engineering, 2008, 100: 448－452.
[10]	Reeves III J B, McCarty G W, Reeves V B. Mid-infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural soils[J]. Food Chemistry, 2001, 49(2): 766－772.
[11]	卢艳丽，白由路，王磊，等. 黑土土壤中全氮含量的高光谱预测分析[J]. 农业工程学报，2010，26(1)：256－261.Lu Yanlil, Bai Youlu, Wang Lei, et al. Determination for total nitrogen content in black soil using hyperspectral data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(1): 256－261. (in Chinese with English abstract)
[12]	Brunet D, Barthe`s B G, Chotte J L, et al. Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity[J]. Geoderma, 2007, 139(1/2): 106－117.
[13]	Ludwig B, Khanna P K, Bauhus J, et al. Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability[J]. Forest Ecology and Management, 2002, 171(1/2): 121－132.
[14]	张娟娟，田永超，朱艳，等. 一种预测土壤有机质含量的近红外光谱参数[J]. 应用生态学报，2009，20(8)：1896－1904.Zhang Juanjuan, Tian Yongchao, Zhu Yan, et al. A novel spectral index for estimating soil organic matter content using near-infrared spectroscopy[J]. C hinese Journal of Applied Ecology, 2009, 20(8): 1896－1904. (in Chinese with English abstract)
[15]	张娟娟，田永超，朱艳，等. 不同类型土壤的光谱特征及其有机质含量预测研究[J]. 中国农业科学，2009，42(9)：3154－3163.Zhang Juanjuan, Tian Yongchao, Zhu Yan, et al. Spectral characteristics and estimating of organic matter contents of different soil types[J]. Scientia Agricultura Sinica, 2009, 42(9): 3154－3163. (in Chinese with English abstract)
[16]	田永超，张娟娟，姚霞，等. 基于近红外光声光谱的土壤有机质含量定量建模方法[J]. 农业工程学报，2012，28(1)：145－152.Zhang Juanjuan, Tian Yongchao, Yao Xia, et al. Quantitative modeling method of soil organic matter content based on near-infrared photoacoustic spectroscopy[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(1): 145－152. (in Chinese with English abstract)
[17]	张娟娟，田永超，姚霞，等. 基于高光谱的土壤全氮含量估测[J]. 自然资源学报，2011，26(5)：881－889.Zhang Juanjuan, Tian Yongchao, Yao Xia, et al. Estimating soil total nitrogen content based on hyperspectral analysis technology[J]. Journal of Natural Resources, 2011, 26(5): 881－889. (in Chinese with English abstract)
[18]	Savitzky A, Golay M J E. Smoothing and differentiation of data by simplified least-squares procedures[J]. Analytical Chemistry, 1964, 36(8): 1627－1639.
[19]	赵强，张工力，陈星旦. 多元散射校正对近红外光谱分析定标模型的影响[J]. 光学精密工程，2005，13(1)：53－58.Zhao Qiang, Zhang Gongli, Chen Xingdan. Effects of multiplicative scatter correction on a calibration m odel of near infrared spectral analysis[J]. Optics and Precision Engineering, 2005, 13(1): 53－58. (in Chinese with English abstract)
[20]	丁海泉，卢启鹏，朴仁官，等. 土壤有机质近红外光谱分析组合波长的优选[J]. 光学精密工程，2007，15(12)：1946－1951.Ding Haiquan, Lu Qipeng, Piao Renguan, et al. Optimum choice of combination wavelengths in near infrared analysis for soil organic matter[J]. Optics and Precision Engineering, 2007, 15(12): 1946－1951. (in Chinese with English abstract)
[21]	Chang C W, Laird D A, Mausbach M J, et al. Near-infrared reflectance spectroscopy principal components regression analysis of soil properties[J]. Soil Science Society of America Journal, 2001, 65(2): 480－490.
[22]	Lee W S, Sanchez J F, Mylavarapu R S, et al. Estimating chemical properties of Florida soil using spectral reflectance[J]. Transactions of the ASAE, 2003, 46(5): 1443－1453.