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Remote Sensing Technology and Application
    
Using Visible and Near-infrared Reflectance Spectroscopy to Estimate Total Nitrogen Contents for Different Soil Types in the Sanjiangyuan Regions
Gao Xiaohong,Yang Yang,Zhang Wei,Jia Wei,Li Jinshan,Tian Chengming,Zhang Yanjiao,Yang Lingyu,He Linhua
(School of Life and Geographical Sciences, Key Laboratory of Physical Geography and Environment Process in Qinghai Province,Key Laboratory of Education Ministry on Environment and Resources in Qinghai-Tibet Plateau,Qinghai Normal University,Xining,810008,China)
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Abstract  

Visible and Near-Infrared Reflectance Spectroscopy (VNIRS) has extensively been used to estimate soil total nitrogen (TN) concentration,and can provide a rapid,convenient method for quantitatively obtaining soil TN content in a wide range of areas.In this study,we evaluated the prediction ability of Visible and Near-Infrared Reflectance Spectroscopy (VNIRS) for estimating soil TN in the Sanjiang Yuan regions of Qinghai province.Firstly,we collected about 146 surface soil samples (0~30 cm),including four soil types during the period from August 7 to 17 of 2012 in Yushu and Maduo counties;secondly,we respectively measured soil reflectance spectrum by ASD FieldSpec 4 portable spectrometer (Analytical Spectral Devices,Inc.,Boulder Colorado,2012) with the spectral range of 350~2 500 nm,and soil TN by using Vario EL Ⅲ element analyzer of ELEMENTAR Inc.in the laboratory;and then we respectively adopted PLSR and BPNN models to relate soil TN to raw spectral reflectance and its four pre-processing transformations for the overall soil samples and each soil types samples.The results showed that the average coefficients of determination(R2) of calibration and validation for BPNN are respectively 0.87 and 0.81 with the mean RPDval of 2.28,whereas those of PLSR model are 0.75,0.72 and 1.95 respectively,which suggest that BPNN has a better prediction ability than PLSR as a whole;The combination of BPNN and the raw reflectance spectrum (REF) and its all pre-processing transformations performed a good or closer good prediction ability for different and overall soil types;whereas the combination of PLSR model and REF,Log(1/R),BD produced a rough or good prediction ability for estimating TN,however,FDR and SDR with poor prediction ability,especially SDR (R2 cal<0.5,R2val<0.5,RPDval=1.10~1.27) hasnt the ability to predict soil TN;As a whole,TN estimating from the overall soil samples can produce more stability prediction accuracies than single soil types,whereas that from single soil type samples can reflect the difference among soil types;BPNN model accuracies are superior to those of PLSR model,but PLSR has stronger operability,and can show the difference among soil types,and different among transformation indicators as well as.

Key words:  Soil total nitrogen(TN)      Visible-near infrared reflectance spectroscopy(VNIRS)      Partial least square regression(PLSR)      Back propagation neural network(BPNN)      Sanjiangyuan Regions      Yushu Counties      Maduo counties     
Received:  17 April 2014      Published:  08 December 2015
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Articles by authors
Gao Xiaohong
Yang Yang
Zhang Wei
Jia Wei
Li Jinshan
Tian Chengming
Zhang Yanjiao
Yang Lingyu
He Linhua

Cite this article: 

Gao Xiaohong,Yang Yang,Zhang Wei,Jia Wei,Li Jinshan,Tian Chengming,Zhang Yanjiao,Yang Lingyu,He Linhua. Using Visible and Near-infrared Reflectance Spectroscopy to Estimate Total Nitrogen Contents for Different Soil Types in the Sanjiangyuan Regions. Remote Sensing Technology and Application, 2015, 30(5): 849-859.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2015.5.0849      OR     http://www.rsta.ac.cn/EN/Y2015/V30/I5/849

[1]Li Mingfeng,Dong Yunshe,Qi Yuchun,et al.Effect of Land-Use Change on the Contents of C & N in Temperate Grassland Soils[J].Grassland of China,2005,27(1):1-6.[李明峰,董云社,齐玉春,等.温带草原土地利用变化对土壤碳氮含量的影响[J].中国草地,2005,27(1):1-6.]

[2]Chen Kaihua,Yin Hengxia,Liu Junying,et al.The Dynamics of Soil Total Nitrogen Content of Different Vegetation Types on Alpine Kobersiameadow[J].Ecology and Environmental Sciences,2009,18(6):2321-2325.[陈开华,殷恒霞,刘俊英,等.高寒草甸不同植被类型土壤全氮含量变化动态分析[J].生态环境学报,2009,18(6):2321-2325.]

[3]Gu Zhenkuan,Du Guozhen,Zhu Weixin,et al.Distribution Pattern of Soil Nutrients in Different Grassland Types and Soil Depths in the Eastern Tibetan Plateau[J].Pratacultural Science,2012,29(4):507-512.[顾振宽,杜国祯,朱炜歆,等.青藏高原东部不同草地类型土壤养分的分布规律[J].草业科学,2012,29(4):507-512.]

[4]Viscarra Rossel R A,McBratney A B.Soil Chemical Analytical Accuracy and Costs:Implications from Precision Agriculture[J].Australian Journal of Experimental Agriculture,1998,38:765-775.

[5]Volkan B A,Van Es H M,Akbas F,et al.Visible-near Infrared Reflectance Spectroscopy for Assessment of Soil Properties in a Semi-arid Area of Turkey[J].Journal of Arid Environments,2010,74:229-238.

[6]Chang C W,Laird D A,Mausbach M J,et al.Near-infrared Reflectance Spectroscopy-principal Components Regression Analyses of Soil Properties[J].Soil Science Society of America Journal,2001,65:480-490.

[7]Peng Jie,Xiang Hongying,Zhou Qing,et al.Prediction on Total Nitrogen Content in Different Type Soils based on Hyperspectrum[J].Chinese Agricultural Science Bulletin,2013,29(9):105-111.[彭杰,向红英,周清,等.不同类型土壤全氮含量的高光谱预测研究[J].中国农学通报,2013,29(9):105-111.]

[8]Viscarra Rossel R A,Walvoort D J J,McBratney A B,et al.Visible,Near infrared,Mid Infrared or Combined Diffuse Reflectance Spectroscopy for Simultaneous Assessment of Various Soil Properties[J].Geoderma,2006,131:59-75.

[9]Vasques G M,Grunwald S,Harris W G.Spectroscopic Models of Soil Organic Carbon in Florida,USA[J].Journal of Environmenal Quality,2010,39:923-934.

[10]McBratney A B,Minasny B,Viscarra Rossel R A,Spectral Soil Analysis and Inference Systems:A powerful Combination for Solving the Soil Data Crisis[J].Geoderma,2006,136:272-278.

[11]Xie H T,Yang X M,Drury C F,et al.Predicting Soil Organic Carbon and Total Nitrogen Using Mid-and Near-infrared Spectra for Brookston Clay Loam Soil in Southwestern Ontario,Canada[J].Canadian Journal of Soil Science,2011,91:53-63.

[12]Vasques G M,Grunwald S,Sickman J O.Comparison of Multivariate Methods for Inferential Modeling of Soil Carbon Using Visible/Near-infrared Spectra[J].Geoderma,2008,146:14-25.

[13]Xu Yongming,Lin Qizhong,Wang Lu,et al.Model for Estimating Soil Nutrient Elements based on High Resoulution Reflectance Spectra[J].Acta Pedologica Sinica,2006,43(5):709-716.[徐永明,蔺启忠,王璐,等.基于高分辨率反射光谱的土壤营养元素估算模型[J].土壤学报,2006,43(5):709-716.]

[14]Ren Hongyan,Shi Xuezheng,Zhuang Dafang,et al.Effects on Estimating Soil Nitrogen Content and Ratio of Carbon to Nitrogen Using Hyperspectal Reflectance[J].Remote Sensing Technology and Application,2012,27(3):372-379.[任红艳,史学正,庄大方,等.土壤全氮含量与碳氮比的高光谱反射估测影响因素研究[J].遥感技术与应用,2012,27(3):372-379.]

[15]Shen Runping,Ding Guoxiang,Wei Guoshuan,et al.Retrieval of Soil Organic Matter Content from Hyper-spectrum based on ANN[J].Acta Pedologica Sinica,2009,46(3):391-397.[沈润平,丁国香,魏国栓,等.基于人工神经网络的土壤有机质含量高光谱反演[J].土壤学报,2009,46(3):391-397.]

[16]Zheng Lihua,Li Minzan,Pan Luan,et al.Estimating of Soil Organic Matter and Soil Total Nitrogen based on NIR Spectroscopy and BP Neural Network[J].Spectroscopy and Spectral Analysis,2008,28(5):1150-1164.[郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测[J].光谱学与光谱分析,2008,28(5):1160-1164.]

[17]Li Shuo,Wang Shanqin,Zhang Meiqin.Comparison among Principal Component Regression,Partial Least Squares Regression and Back Propagation Neural Network for Prediction of Soil Nitorgen with Visible-near Infrared Spectroscopy[J].Acta Optica Sinica,2012,32(8):0830001-1-5.[李硕,汪善勤,张美琴.基于可见-近红外光谱比较主成分回归、偏最小二乘回归和反向传播网络对土壤氮的预测研究[J].光学学报,2012,32(8):0830001-1-5.]

[18]Zhang Juanjuan,Tian Yongchao,Yao Xia,et al.Estimating Soil Total Nitrogen Content baesd on Hyperspectral Analysis Technology[J].Journal of Natural Resources,2011,26(5):881-890.[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):881-890.]

[19]Lei Zhidong,Yang Shixiu,Xie Senchuan.Soil Water Dynamics[M].Beijing:Tsinghua University Press,1988:321-366.[雷志栋,杨诗秀,谢森传.土壤水动力学[M].北京:清华大学出版社,1988:321-366.]

[20]Yang Yang,Gao Xiaohong,Jia Wei,et al.Hyperspectral Retrival of Soil Organic Matter for Different Soil Types in the Three-river Headwaters Region[J].Remote Sensing Technology and Application,2015,30(1):186-198.[杨扬,高小红,贾伟,等.三江源区不同土壤类型有机质含量高光谱反演[J].遥感技术与应用,2015,30(1):186-198.]

[21]He T,W J.Spectral Feature of Soil Organic Matter[J].GEO-Spatial Information Science,2009,1(12):33-40.

[22]Liu Lei,Shen Runping,Ding Guoxiang.Studies on the Estimation of Soil Organic Matter Content based on Hyper-spectrum[J].Spectroscopy and Spectral Analysis,2011,31(3):762-766.[刘磊,沈润平,丁国香.基于高光谱的土壤有机质含量估算研究[J].光谱学与光谱分析,2011,31(3):762-766.]

[23]Wang Wei,Shen Runping,Ji Caoxiang.Study on Heavy Metal Cu based on Hyperspectral Remote Sensing[J].Remote Sensing Technology and Application,2011,26(3):348-353.[王维,沈润平,吉曹翔.基于高光谱的土壤重金属铜的反演研究[J].遥感技术与应用,2011,26(3):348-354.]

[24]Shi Zhou.Soil Ground Hyperspectral Remote Sensing Principle and Methods[M].Beijing:Science Press,2014:42-44.[史舟.土壤地面高光谱遥感原理与方法[M].北京:科学出版社,2014:42-44.]

[25]Wang Huiwen.Partial Least-squares Regression Method and Applications[M].Beijing:National Defense Industry Press,2006:150-169.[王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社,2006:150-169.]

[26]Zhang Wenge,Wu Zening,Lu Hongbo.Improvement and Application to BP Neural Network[J].Hennan Science,2003,21(2):202-206.[张文鸽,吴泽宁,逯洪波.BP神经网络的改进及应用[J].河南科学,2003,21(2):202-206.]

[27]Ji Wenjun,Li Xi,Li Chengxue,et al.Using Different Data Mining Algorithms to Predict Soil Organic Matter based on Visible-near Infrared Spectroscopy[J].Spectroscopyand Spectral Analysis,2012,32(9):2393-2398.[纪文君,李曦,李成学,等.基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[J].光谱学与光谱分析,2012,32(9):2393-2398.]

[28]Xu Binbin.Research on Soil Reflectance Spectral of China[J].Journal of Remote Sensing,1991,6(1):61-71.[徐彬彬.我国土壤光谱线之研究[J].环境遥感,1991,6(1): 61-71.]

[29]Liu Wei,Chang Qingrui,Guo Man,et al.Analysis on Derivative Spectrum Feature for SOM under Different Scales of Differential Window[J].Journal of Infrared and Millimeter Waves,2011,30(4):316-321.[刘炜,常庆瑞,郭曼,等.不同尺度的微分窗口下土壤有机质的一阶导数光谱响应特征分析[J].红外与毫米波学报,2011,30(4):316-321.]〖ZK)


 


 

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