Fuzzy clustering of Vis–NIR spectra for the objective recognition of soil morphological horizons in soil profiles

Abstract In the past decades the use of Vis–NIR spectra information applied to soil science studies has seen an exponential growth, specially in predicting commonly used soil properties. We used the ability of Vis–NIR for detecting physico-chemical characteristics along with fuzzy clustering techniques to discriminate spectrally homogeneous zones in soil cores and applied a DG to define its boundaries i.e., SPD hor. We tested this methodology in 59 air dried soil cores varying between 85 and 130 cm depth from the HWCPID, NSW, Australia. We observed that SPD hor had great similarity with traditional horizons. The SPD hor were more homogeneous in terms of Vis–NIR spectral variability and also offered more information about the relationship between the different spectral classes. Because of the intrinsic characteristics of the methodology it can be easily applicable with or in conjunction with other proximal sensing devices which can add further detail when recognizing morphological soil horizons.

[1]  O. Spaargaren,et al.  Guidelines for soil description, 4th edition , 2006 .

[2]  R. Fisher.  Statistical methods for research workers , 1927, Protoplasma.

[3]  E. Ben-Dor,et al.  A Novel Method of Classifying Soil Profiles in the Field using Optical Means , 2008 .

[4]  Budiman Minasny,et al.  A conditioned Latin hypercube method for sampling in the presence of ancillary information , 2006, Comput. Geosci..

[5]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[6]  A. Karnieli,et al.  Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel , 2002 .

[7]  P.F.M. van Gaans,et al.  Continuous classification in soil survey: spatial correlation, confusion and boundaries , 1997 .

[8]  Matthew E. Wolak,et al.  Guidelines for estimating repeatability , 2012 .

[9]  James G. Bockheim,et al.  Historical development of key concepts in pedology , 2005 .

[10]  B. Chappell,et al.  X-ray fluorescence spectrometry , 1977 .

[11]  Birl Lowery,et al.  A profile cone penetrometer for mapping soil horizons , 2000 .

[12]  P. Lagacherie,et al.  Detecting, correcting and interpreting the biases of measured soil profile data: A case study in the Cap Bon Region (Tunisia) , 2013 .

[13]  Wolfgang Härdle,et al.  Optimal Median Smoothing , 1995 .

[14]  J. Walker,et al.  Australian Soil and Land Survey Field Handbook , 1984 .

[15]  R. Isbell.  Australian Soil Classification , 1996 .

[16]  Sabine Grunwald,et al.  Profile cone penetrometer data used to distinguish between soil materials , 2001 .

[17]  B. Minasny,et al.  Towards digital soil morphometrics , 2014 .

[18]  F. Nachtergaele.  Soil taxonomy—a basic system of soil classification for making and interpreting soil surveys: Second edition, by Soil Survey Staff, 1999, USDA–NRCS, Agriculture Handbook number 436, Hardbound , 2001 .

[19]  Alex B. McBratney,et al.  The application of fuzzy classification to soil pH profiles in the Lockyer Valley, Queensland, Australia , 1991 .

[20]  Somsubhra Chakraborty,et al.  Enhanced Pedon Horizonation Using Portable X-ray Fluorescence Spectrometry , 2012 .

[21]  Clemens Reimann,et al.  Multivariate outlier detection in exploration geochemistry , 2005, Comput. Geosci..

[22]  J. Demattê,et al.  Spectral behavior of some modal soil profiles from São Paulo State, Brazil , 2012 .

[23]  Budiman Minasny,et al.  Bottom-up digital soil mapping. I. Soil layer classes , 2011 .

[24]  L. Sherwin.  Permian fossils and palaeoenvironments of the northern Sydney Basin, New South Wales , 2012 .