Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy

AimsThis study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored.MethodsThe TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets.ResultsResults showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing.ConclusionsPLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.

[1]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[2]  Sabine Grunwald,et al.  Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra , 2008 .

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  D. Cozzolino,et al.  Determination of potentially mineralizable nitrogen and nitrogen in particulate organic matter fractions in soil by visible and near-infrared reflectance spectroscopy , 2004, The Journal of Agricultural Science.

[5]  Min-Zan Li,et al.  [Estimation of soil organic matter and soil total nitrogen based on NIR spectroscopy and BP neural network]. , 2008, Guang pu xue yu guang pu fen xi = Guang pu.

[6]  José Alexandre Melo Demattê,et al.  Visible–NIR reflectance: a new approach on soil evaluation , 2004 .

[7]  Yong He,et al.  Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy , 2011 .

[8]  K. Shepherd,et al.  Development of Reflectance Spectral Libraries for Characterization of Soil Properties , 2002 .

[9]  T. Jarmer,et al.  Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study , 2003, Plant and Soil.

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[12]  Marvin H. Hall,et al.  Carbon and Nitrogen Analysis of Soil Fractions Using Near-Infrared Reflectance Spectroscopy , 1991 .

[13]  Cheng-Wen Chang,et al.  NEAR-INFRARED REFLECTANCE SPECTROSCOPIC ANALYSIS OF SOIL C AND N , 2002 .

[14]  Jichun Liu,et al.  A comparison study on the melt crystallization kinetics of long chain branched and linear isotactic polypropylenes , 2008 .

[15]  M. Vohland,et al.  Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy , 2011 .

[16]  L. Buydens,et al.  Comparing support vector machines to PLS for spectral regression applications , 2004 .

[17]  Mia Hubert,et al.  LIBRA: a MATLAB library for robust analysis , 2005 .

[18]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[19]  Andreas Hueni,et al.  The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils , 2008 .

[20]  Thomas Kemper,et al.  Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. , 2002, Environmental science & technology.

[21]  William D. Berry,et al.  Multiple regression in practice , 1985 .

[22]  D. F. Malley,et al.  Use of Near-Infrared Reflectance Spectroscopy in Prediction of Heavy Metals in Freshwater Sediment by Their Association with Organic Matter , 1997 .

[23]  Keith D. Shepherd,et al.  Prediction of carbon mineralization rates from different soil physical fractions using diffuse reflectance spectroscopy , 2006 .

[24]  L. Hoffmann,et al.  Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy , 2010 .

[25]  R. Sanderson,et al.  The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra , 1994 .

[26]  M. Aldenderfer,et al.  Cluster Analysis. Sage University Paper Series On Quantitative Applications in the Social Sciences 07-044 , 1984 .

[27]  Shenglu Zhou,et al.  Spatial distribution and sources of soil heavy metals in the outskirts of Yixing City, Jiangsu Province, China , 2008 .

[28]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[29]  R. V. Rossel,et al.  Using data mining to model and interpret soil diffuse reflectance spectra. , 2010 .

[30]  R. Sahoo,et al.  Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS-NIR-SWIR) region , 2009 .

[31]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[32]  D. W. Nelson,et al.  Total Nitrogen Analysis of Soil and Plant Tissues , 1980 .

[33]  G. Fystro.  The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods , 2002, Plant and Soil.

[34]  C. Feller,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 , 2007 .

[35]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .

[36]  Raphael A. Viscarra Rossel,et al.  ParLeS: Software for chemometric analysis of spectroscopic data , 2008 .

[37]  R. V. Rossel,et al.  Visible and near infrared spectroscopy in soil science , 2010 .

[38]  Sabine Grunwald,et al.  Spectroscopic models of soil organic carbon in Florida, USA. , 2010, Journal of environmental quality.

[39]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[40]  W. R. Horwath,et al.  NIR and DRIFT-MIR spectrometry of soils for predicting soil and crop parameters in a flooded field , 2003, Plant and Soil.

[41]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[42]  James B. Reeves,et al.  Near Infrared Reflectance Spectroscopy for the Analysis of Agricultural Soils , 1999 .

[43]  R. V. Rossel,et al.  Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy , 2006 .

[44]  Philippe Lagacherie,et al.  Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements , 2008 .

[45]  R. Henry,et al.  Simultaneous Determination of Moisture, Organic Carbon, and Total Nitrogen by Near Infrared Reflectance Spectrophotometry , 1986 .

[46]  Zhou Qing,et al.  Effect of geometric conditions on soil hyperspectral data scatter characteristic in laboratory test , 2005 .