Assessment of soil organic carbon at local scale with spiked NIR calibrations: Effects of selection and extra-weighting on the spiking subset

European Journal of Soil Science

Volumn 65, Issue 2, 2014, Pages 248-263

  Guerrero, C ^a   Stenberg, B ^b   Wetterlind, J ^b   Viscarra Rossel, R A ^c   Maestre, F T ^d   Mouazen, A M ^e   Zornoza, R ^f   Ruiz Sinoga, J D ^g   Kuang, B ^e  

^a MIGUEL HERNÁNDEZ UNIVERSITY   (Spain)

^b SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^c CSIRO   (Australia)

^d UNIVERSIDAD REY JUAN CARLOS   (Spain)

^e CRANFIELD UNIVERSITY   (United Kingdom)

^f TECHNICAL UNIVERSITY OF CARTAGENA   (Spain)

^g UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY ASSESSMENT; ASSESSMENT METHOD; CALIBRATION; NUMERICAL MODEL; SOIL IMPROVEMENT; SOIL ORGANIC MATTER; WEIGHT;

EID: 84895896294     PISSN: 13510754     EISSN: 13652389     Source Type: Journal    
DOI: 10.1111/ejss.12129     Document Type: Article

References (34)

1. Bellon-Maurel, V. & McBratney, A. 2011. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - critical review and research perspectives. Soil Biology&Biochemistry, 43, 1398-1410.
2. Bricklemyer, R.S. & Brown, D.J. 2010. On-the-go VisNIR: potential and limitations for mapping soil clay and organic carbon. Computers and Electronics in Agriculture, 70, 209-216.
3. Brown, D.J. 2007. Using a global VNIR soil-spectral library for local soil characterization and landscape modelling in a 2nd-order Uganda watershed. Geoderma, 140, 444-453.
4. Brown, D.J., Bricklemyer, R.S. & Millar, P.R. 2005. Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana. Geoderma, 129, 251-267.
5. Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D. & Reinsch, T.G. 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, 132, 273-290.
6. Capron, X., Walczak, B., de Noord, O.E. & Massart, D.L. 2005. Selection and weighting of samples in multivariate regression model updating. Chemometrics and Intelligent Laboratory Systems, 76, 205-214.
7. Christy, C.D. 2008. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture, 61, 10-19.
8. D'Acqui, L.P., Pucci, A. & Janik, L.J. 2010. Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid-infrared diffuse reflectance spectroscopy. European Journal of Soil Science, 61, 865-876.
9. Genot, V., Colinet, G., Bock, L., Vanvyve, D., Reusen, Y. & Dardenne, P. 2011. Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility. Journal of Near Infrared Spectroscopy, 19, 117-138.
10. Gogé, F., Joffre, R., Jolivet, C., Ross, I. & Ranjard, L. 2012. Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database. Chemometrics and Intelligent Laboratory Systems, 110, 168-176.
11. Grinand, C., Barthès, B.G., Brunet, D., Kouakoua, E., Arrouays, D., Jolivet, C. et al. 2012. Prediction of soil organic and inorganic carbon contents at a national scale (France) using mid-infrared reflectance spectroscopy (MIRS). European Journal of Soil Science, 63, 141-151.
12. Guerrero, C., Zornoza, R., Gómez, I. & Mataix-Beneyto, J. 2010. Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy. Geoderma, 158, 66-77.
13. Isaksson, T. & Naes, T. 1990. Selection of samples for calibration in near-infrared spectroscopy. Part II: selection based on spectral measurements. Applied Spectroscopy, 44, 1152-1158.
14. Janik, L.J., Skjemstad, J.O., Sheperd, K.D. & Spouncer, L.R. 2007. The prediction of soil carbon fractions using mid-infrared-partial least square analysis. Australian Journal of Soil Research, 45, 73-81.
15. Kennard, R.W. & Stone, L.A. 1969. Computer aided design of experiments. Technometrics, 11, 137-148.
16. Kusumo, B.H., Hedley, C.B., Hedley, M.J., Hueni, A., Tuohy, M.P. & Arnold, G.C. 2008. The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils. Australian Journal of Soil Research, 46, 623-635.
17. Minasny, B., Tranter, A.B., Brough, D.M. & Murphy, B.W. 2009. Regional transferability of mid-infrared diffuse reflectance spectroscopic prediction for soil chemical properties. Geoderma, 153, 155-162.
18. Pérez-Marín, D., Garrido-Varo, A. & Guerrero, J.E. 2007. Non-linear regression method in NIRS quantitative analysis. Talanta, 72, 28-42.
19. Puchwein, G. 1988. Selection of calibration samples for near-infrared spectrometry by factor analysis of spectra. Analytical Chemistry, 60, 569-573.
20. Reeves, J.B. III, McCarty, G.W. & Meisinger, J.J. 1999. Near infrared reflectance the analysis of agricultural soils. Journal of Near Infrared Spectroscopy, 7, 179-193.
21. Rodionova, O.Y. & Pomerantsev, A.L. 2008. Subset selection strategy. Journal of Chemometrics, 22, 674-685.
22. Sankey, J.B., Brown, D.J., Bernard, M.L. & Lawrence, R.L. 2008. Comparing local vs. global visible and near-infared (VisNIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C. Geoderma, 148, 149-158.
23. Shenk, J.S. & Westerhaus, M.O. 1991. Population definition, sample selection, and calibration procedures for near-infrared reflectance spectroscopy. Crop Science, 31, 469-474.
24. Shepherd, K.D. & Walsh, M.G. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal, 66, 988-998.
25. Shetty, N., Rinnan, A. & Gislum, R. 2012. Selection of representative calibration sample sets for near-infrared reflectance spectroscopy for predict nitrogen concentration in grasses. Chemometrics and Intelligent Laboratory Systems, 111, 59-65.
26. Stenberg, B., Nordkvist, E. & Salomonsson, L. 1995. Use of near infrared reflectance spectra of soils for objective selection of samples. Soil Science, 159, 109-114.
27. Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M. & Wetterlind, J. 2010. Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107, 163-215.
28. Stork, C.L. & Kowalski, B.R. 1999. Weighting schemes for updating regression models - a theoretical approach. Chemometrics and Intelligent Laboratory Systems, 48, 151-166.
29. Viscarra Rossel, R.A., Cattle, S.R., Ortega, A. & Fouad, Y. 2009. In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy. Geoderma, 150, 253-266.
30. Viscarra Rossel, R.A., Jeon, Y.S., Odeh, I.O.A. & McBratney, A.B. 2008. Using a legacy soil sample to develop a mid-IR spectral library. Australian Journal of Soil Research, 46, 1-16.
31. Viscarra Rossel, R.A. & Webster, R. 2012. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. European Journal of Soil Science, 63, 848-860.
32. Walkley, A. & Black, I.A. 1934. An examination of the Degtjareff method for determining soil O.M. and a proposed modification of the chromic acid titration method. Soil Science, 37, 29-38.
33. Wetterlind, J. & Stenberg, B. 2010. Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples. European Journal of Soil Science, 61, 823-843.
34. Wetterlind, J., Stenberg, B. & Söderström, M. 2010. Increased sample point density in farm soil mapping by local calibration of visible and near infrared prediction models. Geoderma, 156, 152-160.