Major Issues of Diffuse Reflectance NIR Spectroscopy in the Specific Context of Soil Carbon Content Estimation. A Review.

Advances in Agronomy

Volumn 123, Issue , 2014, Pages 145-175

  Gobrecht, Alexia ^a   Roger, Jean Michel ^a   Bellon Maurel, Véronique ^a  

^a UMR ITAP   (France)

Author keywords

Beer Lambert law; Data preprocessing; Light scattering; Light matter interaction; Multivariate analysis; Near infrared spectroscopy; Soil carbon

Indexed keywords

EID: 84890326478     PISSN: 00652113     EISSN: None     Source Type: Book Series    
DOI: 10.1016/B978-0-12-420225-2.00004-2     Document Type: Chapter

References (92)

1. Ahrens L.H. The lognormal distribution of the elements (a fundamental law of geochemistry and its subsidiary). Geochim. Cosmochim. Acta 1954, 5(2):49-73.
2. Allen M.P. Assumptions of Ordinary Least-Squares Estimation Understanding Regression Analysis. Springer USA 1997, 180-185.
3. Ballabio C. Spatial prediction of soil properties in temperate mountain regions using support vector regression. Geoderma 2009, 151(3-4):338-350.
4. Barnes R.J., Dhanoa M.S., Lister S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 1989, 43(5):772-777.
5. Bartholomeus H.M., Schaepman M.E., Kooistra L., Stevens A., Hoogmoed W.B., Spaargaren O.S.P. Spectral reflectance based indices for soil organic carbon quantification. Geoderma 2008, 145(1-2):28-36.
6. Bellon-Maurel V. NIR and soil science: a teenage love-story. Pedometron 2009, 28.
7. Bellon-Maurel V., McBratney A. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils-critical review and research perspectives. Soil Biol. Biochem. 2011, 43(7):1398-1410.
8. Bellon-Maurel V., Fernandez-Ahumada E., Palagos B., Roger J.-M., McBratney A. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends Anal. Chem. 2010, 29(9):1073-1081.
9. Ben-Dor E., Chabrillat S., Demattê J.A.M., Taylor G.R., Hill J., Whiting M.L., Sommer S. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 2009, 113(Suppl. 1):S38-S55.
10. Bertran E., Blanco M., Maspoch S., Ortiz M.C., Sánchez M.S., Sarabia L.A. Handling intrinsic non-linearity in near-infrared reflectance spectroscopy. Chemom. Intell. Lab. Syst. 1999, 49(2):215-224.
11. Bertrand D., Dufour E. La Spectroscopie Infrarouge et ses Applications Analytiques 2006, Lavoisier, Paris. second ed.
12. Breiman L. Classification and Regression Trees 1984, Wadsworth International Group, Belmont, CA.
13. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
14. Brejda J.J., Moorman T.B., Smith J.L., Karlen D.L., Allan D.L., Dao T.H. Distribution and variability of surface soil properties at a regional scale. Soil Sci. Soc. Am. J. 2000, 64(3):974-982.
15. Brown D.J. Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed. Geoderma 2007, 140(4):444-453.
16. Brown D.J., Shepherd K.D., Walsh M.G., Dewayne Mays M., Reinsch T.G. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 2006, 132(3-4):273-290.
17. Cecillon L., Barthes B.G., Gomez C., Ertlen D., Genot V., Hedde M., Stevens A., Brun J.J. Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). Eur. J. Soil Sci. 2009, 60(5):770-784.
18. Chen H., Bakshi B.R., Goel P.K. Toward Bayesian chemometrics. A tutorial on some recent advances. Anal. Chim. Acta 2007, 602(1):1-16.
19. Ciani A., Goss K.U., Schwarzenbach R.P. Light penetration in soil and particulate minerals. Eur. J. Soil Sci. 2005, 56(5):561-574.
20. Clark R. Spectroscopy of Rocks and Minerals and Principles of Spectroscopy 1999, John Wiley & Sons, Chichester, UK.
21. Cleveland W.S., Devlin S.J. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 1988, 83(403):596-610.
22. Cogdill R.P., Dardenne P. Least-squares support vector machines for chemometrics: an introduction and evaluation. J. Near Infrared Spectrosc. 2004, 12(2):8.
23. Cook R., Weisberg S. Residuals and Influence in Regression 1982, John Wiley & Sons, New York.
24. Dahm D.J., Dahm D.K. The physics of near-infrared scattering. Near Infrared Technology in Agricultural and Food Industries 2001, 1-17. American Association of Cereal Chemists, St. Paul. P. Williams, K. Norris (Eds.).
25. Daniel K.W., Tripathi N.K., Honda K. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand). Soil Res. 2003, 41(1):47-59.
26. Davies A.M.C., Fearn T. Back to basics: calibration statistics. Spectrosc. Eur. 2006, 18(2):31-32.
27. Davies A.M.C., Fearn T. Quantitative analysis via near infrared databases: comparison analysis using restructured near infrared and constituent data-deux (CARNAC-D). J. Near Infrared Spectrosc. 2006, 14(6):9.
28. Fearn T. Do my data need to be normally distributed?. NIR News 2012, 23(1):20-21.
29. Fearn T., Perez-Marin D., Garrido-Varo A., Guerrero-Ginel J.E. Inverse, classical, empirical and nonparametric calibrations in a Bayesian framework. J. Near Infrared Spectrosc. 2010, 18(1):27-38.
30. Fernández Pierna J.A., Dardenne P. Soil parameter quantification by NIRS as a chemometric challenge at 'Chimiométrie 2006'. Chemom. Intell. Lab. Syst. 2008, 91(1):94-98.
31. Fernandez-Ahumada E., Roger J., Palagos B. A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration. Anal. Chim. Acta 2012, 721:28-34.
32. Fidencio P.H., Poppi R.J., de Andrade J.C. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Anal. Chim. Acta 2002, 453(1):125-134.
33. Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 1991, 19(1):1-67.
34. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann. Stat. 2000, 28(2):337-407.
35. Geladi P., MacDougall D., Martens H. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl. Spectrosc. 1985, 39(3):491-500.
36. Geladi P., Hadjiiski L., Hopke P. Multiple regression for environmental data: nonlinearities and prediction bias. Chemom. Intell. Lab. Syst. 1999, 47(2):165-173.
37. Gogé F., Joffre R., Jolivet C., Ross I., Ranjard L. Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database. Chemom. Intell. Lab. Syst. 2012, 110(1):168-176.
38. Igne B., Reeves J.B., McCarty G.W., Hively W.D., Lund E., Hurburgh C.R. Evaluation of spectral pretreatments, partial least squares, least squares support vector machines and locally weighted regression for quantitative spectroscopic analysis of soils. J. Near Infrared Spectrosc. 2010, 18(3):167-176.
39. Janik L.J., Forrester S.T., Rawson A. The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis. Chemom. Intell. Lab. Syst. 2009, 97(2):179-188.
40. Kleinbaum D.G., Kupper L.L., Nizam A., Muller K.E. Applied Regression Analysis and Other Multivariable Methods 2008, Thomson Brooks/Cole, Belmont, CA.
41. Kowalski B.R., Wold S. Pattern recognition in chemistry. Handbook of Statistics 1982, vol. 2:673-697. Elsevier, Amsterdam.
42. Kuang B., Mahmood H.S., Quraishi M.Z., Hoogmoed W.B., Mouazen A.M., van Henten E.J. Chapter four-sensing soil properties in the laboratory, in situ, and on-line: a review. Advances in Agronomy 2012, vol. 114:155-223. Academic Press, Burlington. D.L. Sparks (Ed.).
43. Kubelka P., Munk F. Ein beitrag zur optik der farbanstriche. Z. Tech. Phys. 1931, 12:593-601.
44. Lal R. Soil carbon sequestration impacts on global climate change and food security. Science 2004, 304(5677):1623-1627.
45. Limpert E., Stahel W.A., Abbt M. Log-normal distributions across the sciences: keys and clues. Bioscience 2001, 51(5):341-352.
46. Martens H., Næs T. Multivariate Calibration 1989, John Wiley & Sons, New York.
47. Martens H., Stark E. Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. J. Pharm. Biomed. Anal. 1991, 9(8):625-635.
48. Martens H., Nielsen J.P., Engelsen S.B. Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Anal. Chem. 2003, 75(3):394-404.
49. Massart D., Vandeginste B.G.M., Buydens L.M.C., De Jong J., Lewi P., Smeyers-Verbeke J. Chapter 8: straight line regression and calibration. Handbook of Chemometrics and Qualimetrics: Part A 1998, 171-230. Elsevier, Amsterdam.
50. McBratney A.B., Mendonça Santos M.L., Minasny B. On digital soil mapping. Geoderma 2003, 117(1-2):3-52.
51. Minasny B., McBratney A.B. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemom. Intell. Lab. Syst. 2008, 94(1):72-79.
52. Minasny B., McBratney A.B., Bellon-Maurel V., Roger J.-M., Gobrecht A., Ferrand L., Joalland S. Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma 2011, 167-168:118-124.
53. Morgan C.L.S., Waiser T.H., Brown D.J., Hallmark C.T. Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy. Geoderma 2009, 151(3-4):249-256.
54. Mouazen A.M., Kuang B., De Baerdemaeker J., Ramon H. 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. Geoderma 2010, 158(1-2):23-31.
55. Naes T., Isaksson T., Kowalski B. Locally weighted regression and scatter correction for near-infrared reflectance data. Anal. Chem. 1990, 62(7):664-673.
56. Næs T., Isaksson T., Fearn T., Davies T. A User-Friendly Guide to Multivariate Calibration and Classification 2002, NIR Publications, Chistester, UK.
57. Osborne J., Waters E. Four assumptions of multiple regression that researchers should always test. Pract. Assessment Res. Eval. 2002, 8(2):1-5.
58. Parkin T.B., Meisinger J.J., Chester S.T., Starr J.L., Robinson J.A. Evaluation of statistical estimation methods for lognormally distributed variables. Soil Sci. Soc. Am. J. 1988, 52(2):323-329.
59. Pérez-Marín D., Fearn T., Guerrero J.E., Garrido-Varo A. Improving NIRS predictions of ingredient composition in compound feeding stuffs using Bayesian non-parametric calibrations. Chemom. Intell. Lab. Syst. 2012, 110(1):108-112.
60. Preys S., Roger J.M., Boulet J.C. Robust calibration using orthogonal projection and experimental design. Application to the correction of the light scattering effect on turbid NIR spectra. Chemom. Intell. Lab. Syst. 2008, 91(1):28-33.
61. Quinlan J. Learning with continuous classes 1992, 343-348.
62. Reeves J.B. Near- versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: where are we and what needs to be done?. Geoderma 2009, 158(1-2):3-14.
63. Reimann C., Filzmoser P. Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data. Environ. Geol. 2000, 39(9):1001-1014.
64. Rinnan A., Berg F.v.d., Engelsen S.B. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal. Chem. 2009, 28(10):1201-1222.
65. Roger J.-M., Chauchard F., Bellon-Maurel V. EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemom. Intell. Lab. Syst. 2003, 66(2):191-204.
66. Roger J.M., Palagos B., Bertrand D., Fernandez-Ahumada E. CovSel: variable selection for highly multivariate and multi-response calibration: application to IR spectroscopy. Chemom. Intell. Lab. Syst. 2011, 106(2):216-223.
67. Sankey J.B., Brown D.J., Bernard M.L., Lawrence R.L. Comparing local vs. global visible and near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C. Geoderma 2008, 148(2):149-158.
68. Savitsky A., Golay M. Smoothing and differentiation of data by simplified least-squares procedures. Anal. Chim. Acta 1964, 36:1627-1639.
69. Seasholtz M.B., Kowalski B.R. The effect of mean centering on prediction in multivariate calibration. J. Chemom. 1992, 6(2):103-111.
70. Shenk J.S., Westerhaus M.O., Berzaghi P. Investigation of a LOCAL calibration procedure for near infrared instruments. J. Near Infrared Spectrosc. 1997, 5(4):223-232.
71. Shepherd K.D., Walsh M.G. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 2002, 66(3):988-998.
72. Stenberg B. Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon. Geoderma 2010, 158(1-2):15-22.
73. Stenberg B., Viscarra-Rossel R.A. Diffuse reflectance spectroscopy for high-resolution soil sensing. Proximal Soil Sensing 2010, 29-47. Springer, Netherlands.
74. Stenberg B., Rossel R.A.V., Mouazen A.M., Wetterlind J. Chapter five-visible and near infrared spectroscopy in soil science. Advances in Agronomy 2010, vol. 107:163-215. Academic Press, Burlington. D.L. Sparks (Ed.).
75. Stevens A., Udelhoven T., Denis A., Tychon B., Lioy R., Hoffmann L., van Wesemael B. Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy. Geoderma 2010, 158(1-2):32-45.
76. Thennadil S.N., Martin E.B. Empirical preprocessing methods and their impact on NIR calibrations: a simulation study. J. Chemom. 2005, 19(2):77-89.
77. Udelhoven T., Schütt B. Capability of feed-forward neural networks for a chemical evaluation of sediments with diffuse reflectance spectroscopy. Chemom. Intell. Lab. Syst. 2000, 51(1):9-22.
78. Vasques G.M., Grunwald S., Sickman J.O. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 2008, 146(1-2):14-25.
79. Verron T., Sabatier R., Joffre R. Some theoretical properties of the O-PLS method. J. Chemom. 2004, 18(2):62-68.
80. Viscarra Rossel R.A. The soil spectroscopy group and the development of a global soil spectral library. NIR News 2008, 20(4):14-15.
81. Viscarra Rossel R.A., Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 2010, 158(1-2):46-54.
82. Viscarra Rossel R.A., Walvoort D.J.J., McBratney A.B., Janik L.J., Skjemstad J.O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006, 131(1-2):59-75.
83. Vistelius A.B. The skew frequency distributions and the fundamental law of the geochemical processes. J. Geol. 1960, 68(1):1-22.
84. Vohland M., Besold J., Hill J., Friind H.-C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma 2011, 166(1):198-205.
85. Webster R. Statistics to support soil research and their presentation. Eur. J. Soil Sci. 2001, 52(2):331-340.
86. Wetterlind J., Stenberg B. Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples. Eur. J. Soil Sci. 2010, 61(6):823-843.
87. Williams P., Norris K. Near-Infrared Technology in the Agricultural and Food Industries 2001, Amer Assn of Cereal Chemists, St. Paul, MN. second ed.
88. Wold S., Antti H., Lindgren F., Ohman J. Orthogonal signal correction of near-infrared spectra. Chemom. Intell. Lab. Syst. 1998, 44(1-2):175-185.
89. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58(2):109-130.
90. Workman J., Springsteen A. Applied Spectroscopy: A Compact Reference for Practitioners 1998, Academic Press, Boston, USA.
91. Zeaiter M., Roger J.M., Bellon-Maurel V. Dynamic orthogonal projection. A new method to maintain the on-line robustness of multivariate calibrations. Application to NIR-based monitoring of wine fermentations. Chemom. Intell. Lab. Syst. 2006, 80(2):227-235.
92. Zhang L., Garcia-Munoz S. A comparison of different methods to estimate prediction uncertainty using Partial Least Squares (PLS): a practitioner's perspective. Chemom. Intell. Lab. Syst. 2009, 97(2):152-158.