Distance and similarity-search metrics for use with soil vis-NIR spectra

Geoderma

Volumn 199, Issue , 2013, Pages 43-53

  Ramirez Lopez, L ^a,b   Behrens, T ^a   Schmidt, K ^a   Rossel, R A Viscarra ^c   Dematte J A M ^d   Scholten, T ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

^b UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^c CSIRO   (Australia)

^d UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Compositional similarity; Global soil spectral library; Locally linear embedding; Proximal soil sensing; Soil spectroscopy; Vis NIR similarity

Indexed keywords

COMPOSITIONAL SIMILARITIES; LOCALLY LINEAR EMBEDDING; PROXIMAL SOIL SENSING; SOIL SPECTROSCOPIES; SPECTRAL LIBRARIES; VIS-NIR SIMILARITY;

ALGORITHMS; INFRARED DEVICES; PH; PRINCIPAL COMPONENT ANALYSIS;

SOILS;

ALGORITHM; NEAR INFRARED; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; SOIL ANALYSIS; SOIL CHEMISTRY; SPECTRAL ANALYSIS; SPECTROSCOPY;

EID: 84875685833     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2012.08.035     Document Type: Article

References (47)

1. Abou-Moustafa K., Ferrie F. Regularized minimum volume ellipsoid metric for query-based learning. Proceedings-7th International Conference on Machine Learning and Applications, ICMLA 2008, art. no. 4724974 2008, 188-193.
2. Ben-Dor E., Irons J.R., Epema G.F. Soil reflectance. Remote Sensing for the Earth Sciences: Manual of Remote Sensing 1999, 3:111-188. John Wiley & Sons, New York. N. Rencz (Ed.).
3. Ben-Dor E., Taylor R.G., Hill J., Demattê J.A.M., Whiting M.L., Chabrillat S., Sommer S. Imaging spectrometry for soil applications. Advances in Agronomy 2008, 97:321-392.
4. Brereton R.G. Chemometrics: Data Analysis for the Laboratory and Chemical Plant 2003, Wiley, Chichester, 489pp.
5. 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, 140:444-453.
6. Chang C.I. An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Transactions on Information Theory 2000, 46:1927-1932.
7. Chen J., Liu Y. Locally linear embedding: a survey. Artificial Intelligence Review 2011, 36:29-48.
8. Chen G., Qian S.-E. Dimensionality reduction of hyperspectral imagery using improved locally linear embedding. Journal of Applied Remote Sensing 2007, 1:013509.
9. Christy C.D. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture 2008, 61:10-19.
10. Chu X.-L., Xu Y.-P., Tian S.-B., Wang Z., Lu W.-Z. Rapid identification and assay of crude oils based on moving-window correlation coefficient and near infrared spectral library. Chemometrics and Intelligent Laboratory Systems 2011, 107:44-49.
11. Clark R.N., Roush T.L. Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research 1984, 89:6329-6340.
12. de Gruijter J.J., McBratney A. Sampling for high-resolution soil mapping. Proximal Soil Sensing, Progress in Soil Science 2010, 3-14. Springer Netherlands, Netherlands. R.A. Viscarra Rossel, A.B. McBratney, B. Minasny (Eds.).
13. De Maesschalck R., Jouan-Rimbaud D., Massart D.L. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems 2000, 50:1-18.
14. Demattê J.A.M., Garcia G.J. Alteration of soil properties through a weathering sequence as evaluated by spectral reflectance. Soil Science Society of America Journal 1999, 63:327-342.
15. Demattê J.A.M., Campos R.C., Alves M.C., Fiorio P.R., Nanni M.R. Visible-NIR reflectance: a new approach on soil evaluation. Geoderma 2004, 121:95-112.
16. Farifteh J., Van Der Meer F., Carranza E.J.M. Similarity measures for spectral discrimination of salt-affected soils. International Journal of Remote Sensing 2007, 28:5273-5293.
17. Ge Y., Morgan C.L.S., Thomasson J.A. Using soil spectral libraries in support of proximal soil sensing. The Second Global Workshop on Proximal Soil Sensing, Montreal 2011, http://adamchukpa.mcgill.ca/gwpss/Papers/GWPSS_2011_Morgan.pdf.
18. Gemperline P.J., Kalivas J.H. Sampling theory, distribution functions and the multivariate normal distribution. Practical Guide to Chemometrics 2006, 41-68. CRC Press Taylor & Francis Group, Boca Raton, Florida. Second edition. P.J. Gemperline (Ed.).
19. He Y., Liu D., Yi S. Recursive spectral similarity measure-based band selection for anomaly detection in hyperspectral imagery. Journal of Optics 2011, 13:15401-15402.
20. IUSS Working Group WRB World reference base for soil resources 2006. Rome FAO, World Soil Resources Reports No. 103 2006, 2nd edition.
21. Kennard R.W., Stone L. Computer aided design of experiments. Technometrics 1969, 11:137-148.
22. Knadel M., Thomsen A., Mogens G. Multisensor on-the-go mapping of soil organic carbon content. Soil Science Society of America Journal 2011, 75:1799-1806.
23. Kullback S., Leibler R.A. On information and sufficiency. Annals of Mathematical Statistics 1951, 22:79-86.
24. Laskar R.H., Talukdar F.A., Paul B., Chakrabarty D. Sample reduction using recursive and segmented data structure analysis. Journal of Engineering and Computer Innovations 2011, 2:59-67.
25. Lugassi R., Ben-Dor E., Eshel G. A spectral-based method for reconstructing spatial distributions of soil surface temperature during simulated fire events. Remote Sensing of Environment 2010, 114:322-331.
26. Ma L., Crawford M.M., Tian J. Anomaly detection for hyperspectral images based on robust locally linear embedding. Journal of Infrared, Millimeter, and Terahertz Waves 2010, 31:753-762.
27. Minasny B., McBratney A.B. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences 2006, 32:1378-1388.
28. Pan Y., Ge S.S., Al Mamun A. Weighted locally linear embedding for dimension reduction. Pattern Recognition 2009, 42:798-811.
29. Park B., Windham W.R., Lawrence K.C., Smith D.P. Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosystems Engineering 2007, 96(3):323-333.
30. R Development Core Team R: A Language and Environment for Statistical Computing 2011, R Foundation for Statistical Computing, Vienna.
31. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
32. 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, 148:149-158.
33. Saul L.K., Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifold. Journal of Machine Learning Research 2003, 4:119-155.
34. Shepherd K.D., Palm C.A., Gachengo C.N., Vanlauwe B. Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy. Agronomy Journal 2003, 95:1314-1322.
35. Stenberg B., Viscarra Rossel R.A., Mouazen A.M., Wetterlind J. Visible and near infrared spectroscopy in soil science. Advances in Agronomy 2010, 107:163-215. Academic Press, Burlington. D.L. Sparks (Ed.).
36. 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, 158:32-45.
37. Stoner E.R., Baumgardner M.F. Characteristics variations in reflectance of surface soils. Soil Science Society of America Journal 1981, 45:1161-1165.
38. Terhoeven-Urselmans T., Vagen T.G., Spaargaren O., Shepherd K.D. Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library. Soil Science Society of America Journal 2010, 74:1792-1799.
39. van der Meer F. The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation 2006, 8:3-17.
40. Van Reeuwijk L.P. Procedures for soil analysis. International Soil Reference and Information Centre, Wageningen, and Food and Agricultural Organization of the United Nations, Rome 2002, 6th edition.
41. Varini C., Degenhard A., Nattkemper T.W. ISOLLE: LLE with geodesic distance. Neurocomputing 2006, 69:1768-1771.
42. Viscarra Rossel R. The Soil Spectroscopy Group and the development of a global soil spectral library. NIR News 2009, 20:14-15.
43. Viscarra Rossel R., Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 2010, 158:46-54.
44. Wetterlind J., Stenberg B. Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples. European Journal of Soil Science 2010, 61:823-843.
45. Wold H. Estimation of principal components and related models by iterative least squares. Multivariate Analysis 1966, 391-420. Academic Press, New York. P.R. Krishnaiah (Ed.).
46. World Agroforestry Centre (ICRAF) and ISRIC-World Soil Information ICRAF-ISRIC Soil vis-NIR spectral Library 2010, World Agroforestry Centre (ICRAF), Nairobi, Kenya.
47. Yuhas R.H., Goetz A.F.H., Boardman J.W. Discrimination among semi-arid landscape endmembers using spectral angle mapper (SAM) algorithm. Summaries of the 4th Annual JPL Airborne Geoscience Workshop, JPL Pub-92-14, AVIRIS Workshop 1992, 147-150. Jet Propulsion Laboratory, Pasadena, CA.