Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

Abstract: With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e. , Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model ( i.e. , SVMR without SPA) and SPA-PLSR model ( i.e. , SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R

[1]  S. K. Alavipanah,et al.  Estimating soil organic carbon from soil reflectance: a review , 2010, Precision Agriculture.

[2]  Frans van den Berg,et al.  Review of the most common pre-processing techniques for near-infrared spectra , 2009 .

[3]  H. Velthuizen,et al.  Harmonized World Soil Database (version 1.2) , 2008 .

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

[5]  Rasmus Bro,et al.  Variable selection in regression—a tutorial , 2010 .

[6]  Yufeng Ge,et al.  Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. , 2011 .

[7]  Roman M. Balabin,et al.  Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction , 2007 .

[8]  Fei Liu,et al.  Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer. , 2009, Analytica chimica acta.

[9]  Sabine Grunwald,et al.  Modeling of Soil Organic Carbon Fractions Using Visible–Near‐Infrared Spectroscopy , 2009 .

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

[11]  Magdeline Laba,et al.  Alleviating Moisture Content Effects on the Visible Near-Infrared Diffuse-Reflectance Sensing of Soils , 2009 .

[12]  A. Walkley,et al.  AN EXAMINATION OF THE DEGTJAREFF METHOD FOR DETERMINING SOIL ORGANIC MATTER, AND A PROPOSED MODIFICATION OF THE CHROMIC ACID TITRATION METHOD , 1934 .

[13]  Alex B. McBratney,et al.  Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy , 2003 .

[14]  Franco Ajmone-Marsan,et al.  DRIFTS Sensor: Soil Carbon Validation at Large Scale (Pantelleria, Italy) , 2013, Sensors.

[15]  P. Miller,et al.  Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana , 2005 .

[16]  H. Ramon,et al.  Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy , 2010 .

[17]  Zou Xiaobo,et al.  Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.

[18]  D. Massart,et al.  The Radial Basis Functions — Partial Least Squares approach as a flexible non-linear regression technique , 1996 .

[19]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[20]  B. Minasny,et al.  Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon , 2011 .

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

[22]  Maria Fernanda Pimentel,et al.  Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry , 2001 .

[23]  Christoph Emmerling,et al.  Determination of total soil organic C and hot water‐extractable C from VIS‐NIR soil reflectance with partial least squares regression and spectral feature selection techniques , 2011 .

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

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

[26]  Maria Fernanda Pimentel,et al.  Using principal component analysis to find the best calibration settings for simultaneous spectroscopic determination of several gasoline properties , 2008 .

[27]  Tiezhu Shi,et al.  Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy , 2014 .

[28]  B. Stenberg,et al.  Near infrared reflectance spectroscopy compared with soil clay and organic matter content for estimating within-field variation in N uptake in cereals , 2007, Plant and Soil.

[29]  Abdul Mounem Mouazen,et al.  Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction , 2011 .

[30]  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.

[31]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

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

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

[34]  Maria Fernanda Pimentel,et al.  Robust modeling for multivariate calibration transfer by the successive projections algorithm , 2005 .

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

[36]  Weixing Cao,et al.  Laboratory assessment of three quantitative methods for estimating the organic matter content of soils in China based on visible/near-infrared reflectance spectra , 2013 .

[37]  Fei Liu,et al.  Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. , 2009 .

[38]  K. Shepherd,et al.  Global soil characterization with VNIR diffuse reflectance spectroscopy , 2006 .

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

[40]  Roberto Kawakami Harrop Galvão,et al.  A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm , 2008 .

[41]  Yufeng Ge,et al.  Comparison of soil reflectance spectra and calibration models obtained using multiple spectrometers , 2011 .

[42]  Mohammad Hossein Fatemi,et al.  Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds , 2013 .

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

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

[45]  E. Ben-Dor,et al.  Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils , 2007 .

[46]  R. Leardi,et al.  Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data , 2002 .

[47]  G. McCarty,et al.  The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. , 2002, Environmental pollution.

[48]  Dazhou Zhu,et al.  The performance of ν-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy , 2007 .

[49]  David G. Rossiter,et al.  Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique , 2012 .

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

[51]  A. McBratney,et al.  Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils – Critical review and research perspectives , 2011 .

[52]  Luca Montanarella,et al.  Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy , 2013, PloS one.

[53]  Xueguang Shao,et al.  A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. , 2007, Talanta.

[54]  Jinhong Chen,et al.  Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed , 2013 .

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

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

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

[58]  Thomas Cudahy,et al.  Applicability of the Thermal Infrared Spectral Region for the Prediction of Soil Properties Across Semi-Arid Agricultural Landscapes , 2012, Remote. Sens..