Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-23T23:26:15.232Z Has data issue: false hasContentIssue false
  • English
  • Français

Mapping Soil Particle-Size Fractions Using Additive Log-Ratio (ALR) and Isometric Log-Ratio (ILR) Transformations and Proximally Sensed Ancillary Data

Published online by Cambridge University Press:  01 January 2024

Muddassar Muzzamal ,
Jingyi Huang ,
Rod Nielson ,
Michael Sefton  and
John Triantafilis
Muddassar Muzzamal
Affiliation:
School of Biological, Earth and Environmental Sciences, UNSW Sydney, Kensington, NSW 2052, Australia
Jingyi Huang
Affiliation:
School of Biological, Earth and Environmental Sciences, UNSW Sydney, Kensington, NSW 2052, Australia
Rod Nielson
Affiliation:
Herbert Cane Productivity Services Pty Ltd, Ingham, Qld 4850, Australia
Michael Sefton
Affiliation:
Herbert Cane Productivity Services Pty Ltd, Ingham, Qld 4850, Australia
John Triantafilis*
Affiliation:
School of Biological, Earth and Environmental Sciences, UNSW Sydney, Kensington, NSW 2052, Australia
*
*E-mail address of corresponding author: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Together, the three particle size fractions (PSFs) of clay, silt, and sand are the most fundamental soil properties because the relative abundance influences the physical, chemical, and biological activities in soil. Unfortunately, determining PSFs requires a laboratory method which is time-consuming. One way to add value is to use digital soil mapping, which relies on empirical models, such as multiple linear regression (MLR), to couple ancillary data to PSFs. This approach does not account for the special requirements of compositional data. Here, ancillary data were coupled, via MLR modelling, to additive log-ratio (ALR) or isometric log-ratio (ILR) transformations of the PSFs to meet these requirements. These three approaches (MLR vs. ALR-MLR and ILR-MLR) were evaluated along with the use of different ancillary data that included proximally sensed gamma-ray spectrometry, electromagnetic induction, and elevation data. In addition, how the prediction might be improved was examined using ancillary data that was measured on transects and was compared to data interpolated from transects spaced far apart. Although the ALR-MLR approach did not produce significantly better results, it predicted soil PSFs that summed to 100 and had the advantage of interpreting the ancillary data relative to the original coordinates (i.e. clay, silt, and sand). For the prediction of PSFs at various depths, all ancillary data were useful. Elevation and gamma-ray data were slightly better for topsoil and elevation and electromagnetic (EM) data were better for subsoil prediction. In addition, a smaller transect spacing (26 m) and number of samples (9–16) might be adopted for mapping soil PSFs and soil texture across the study field. The ALR-MLR approach can be applied elsewhere to map the spatial distribution of clay minerals.

Keywords

Additive Log-ratio TransformationDigital Soil MappingElectromagnetic InductionGamma-ray SpectrometryMultiple-linear RegressionTexture
Type
Article
Information
Clays and Clay Minerals , Volume 66 , Issue 1 , February 2018 , pp. 9 - 27
Copyright
Copyright © Clay Minerals Society 2018

References

Aitchison, J. (1990) Relative variation diagrams for describing patterns of compositional variability. Mathematical Geology, 22, 487–511.CrossRefGoogle Scholar
Brus, D.J., 2015 Balanced sampling: a versatile sampling approach for statistical soil surveys Geoderma 253 111–121.CrossRefGoogle Scholar
Brus, D.J. and Saby, N.P.A., 2016 Approximating the variance of estimated means for systematic random sampling, illustrated with data of the French Soil Monitoring Network Geoderma 279 77–86.CrossRefGoogle Scholar
Buchanan, S. Triantafilis, J. Odeh, I.O.A. and Subansinghe, R., 2012 Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data Geophysics 77 WB201–WB211.CrossRefGoogle Scholar
Carroll, Z.L. and Oliver, M.A., 2005 Exploring the spatial relations between soil physical properties and apparent electrical conductivity Geoderma 128 354–374.CrossRefGoogle Scholar
Cockx, L. Van Meirvenne, M. Vitharana, U.W.A. Verbeke, L.P.C. Simpson, D. Saey, T. and Van Coillie, F.M.B., 2009 Extracting topsoil information from EM38DD sensor data using a neural network approach Soil Science Society of America Journal 73 2051–2058.CrossRefGoogle Scholar
De Benedetto, D. Castrignano, A. Sollitto, D. Modugno, F. Buttafuoco, G. and lo Papa, G., 2012 Integrating geophysical and geostatistical techniques to map the spatial variation of clay Geoderma 171 53–63.CrossRefGoogle Scholar
De Gruijter, J.J. Walvoort, D.J.J. and van Gaans, P.F.M., 1997 Continuous soil maps — a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models Geoderma 77 169–195.CrossRefGoogle Scholar
Domsch, H. and Giebel, A., 2004 Estimation of soil textural features from soil electrical conductivity recorded using the EM38 Precision Agriculture 5 389–409.CrossRefGoogle Scholar
Doolittle, J.A. and Brevik, E.C., 2014 The use of electromagnetic induction techniques in soils studies Geoderma 223 33–45.CrossRefGoogle Scholar
Egozcue, J.J. Barceló-Vidal, C. Martin-Fernández, J.A. Jarauta-Bragulet, E. Díaz-Barrero, J.L. Mateu-Figueras, G., Pawlowsky-Glahn, V. and Buccianti, A., 2011.Elements of simplicial linear algebra and geometry Composition Data Analysis: Theory and ApplicationsCrossRefGoogle Scholar
Egozcue, J.J. Pawlowsky-Glahn, V. Mateu-Figueras, G. and Barceló-Vidal, C., 2003 Isometric log ratio transformations for compositional data analysis Mathematical Geology 35 279–300.CrossRefGoogle Scholar
Filzmoser, P. Hron, K. and Reimann, C., 2009 Principal component analysis for compositional data with outliers Environmetrics 20 621–632.CrossRefGoogle Scholar
Grasty, R.L. Holman, P.B. and Blanchard, Y.B., 1991 Transportable calibration pads for ground and airborne gamma-ray spectrometers Geological Survey of Canada 90 23.Google Scholar
Huang, J. Lark, R.M. Robinson, D.A. Lebron, I. Keith, A.M. Rawlins, B. and Triantafilis, J., 2014 Scope to predict soil properties at within-field scale from small samples using proximally sensed γ-ray spectrometer and EM induction data Geoderma 232 69–80.CrossRefGoogle Scholar
Hedley, C.B. Yule, I.J. Eastwood, C.R. Shepherd, T.G. and Arnold, G., 2004 Rapid identification of soil textural and management zones using electromagnetic induction sensing of soils Australian Journal of Soil Research 42 389–400.CrossRefGoogle Scholar
Heil, K. and Schmidhalter, U., 2012 Characterisation of soil texture variability using the apparent soil electrical conductivity at a highly variable site Computational Geosciences 39 98–110.CrossRefGoogle Scholar
Huang, J. Subasinghe, R. and Triantafilis, J., 2014 Mapping particle-size fractions as a composition using additive log-ratio transformation and ancillary data Soil Science Society of America Journal 78 1967–1976.CrossRefGoogle Scholar
Huang, J. Barrett-Lennard, E.G. Kilminster, T. Sinnott, A. and Triantafilis, J., 2015 An error budget for mapping field-scale soil salinity at various depths using different sources of ancillary data Soil Science Society of America Journal 79 1717–1728.CrossRefGoogle Scholar
Hwang, S.I. Lee, K.P. Lee, D.S. and Powers, S.E., 2002 Models for estimating soil particle-size distributions Soil Science Society of America Journal 66 1143–1150.CrossRefGoogle Scholar
Isbell, R., 2002 The Australian Soil Classification Australia CSIRO Publishing.CrossRefGoogle Scholar
Jung, W.K. Kitchen, N.R. Sudduth, K.A. and Anderson, S.H., 2006 Spatial characteristics of claypan soil properties in an agricultural field Soil Science Society of America Journal 70 1387–1397.CrossRefGoogle Scholar
Lark, R.M. and Bishop, T.F.A., 2007 Cokriging particle size fractions of the soil European Journal of Soil Science 58 763–774.CrossRefGoogle Scholar
Lark, R.M. Cullis, B.R. and Welham, S.J., 2006 On spatial prediction of soil properties in the presence of a spatial trend: The empirical best linear unbiased predictor (E-BLUP) with REML European Journal of Soil Science 57 787–799.CrossRefGoogle Scholar
Lin, L.I.K., 1989 A condordance correlation coefficient to evaluate reproducibility Biometrics 45 255–268.CrossRefGoogle Scholar
Minasny, B. and McBratney, A.B., 2006 Latin hypercube sampling as a tool for digital soil mapping Developments in Soil Science 31 153–606.CrossRefGoogle Scholar
Minasny, B. McBratney, A.B. and Whelan, B.M., 1999 Computer Program for Variogram Estimation and Spatial Prediction with Error Australia Australian Centre for Precision Agriculture Sydney.Google Scholar
Odeh, I.O.A. Todd, A.J. and Triantafilis, J., 2003 Spatial prediction of soil particle-size fractions as compositional data Soil Science 168 501–515.CrossRefGoogle Scholar
Pawlowsky-Glahn, V. and Egozcue, J.J., 2016 Spatial analysis of compositional data: A historical review Journal of Geochemical Exploration 164 28–32.CrossRefGoogle Scholar
Petersen, H. Wunderlich, T. Attia al Hagrey, S. and Rabbel, W., 2012 Characterization of some middle European soil textures by gamma spectrometry Journal of Plant Nutrition and Soil Science 175 651–660.CrossRefGoogle Scholar
Pracilio, G. Adams, M.L. Smettem, K.R.J. and Harper, R.J., 2006 Determination of spatial distribution patterns of clay and plant available potassium contents in surface soils at the farm scale using high resolution gamma ray spectrometry Plant and Soil 282 67–82.CrossRefGoogle Scholar
Rayment, G.E. and Higginson, F.R., 1992 Australian Laboratory Handbook of Soil and Water Chemical Methods Port Melbourne, Australia Inkata Press.Google Scholar
Spadoni, M. and Voltaggio, M., 2013 Contribution of gamma ground spectrometry to the textural characterization and mapping of floodplain sediments Journal of Geochemical Exploration 125 20–33.CrossRefGoogle Scholar
Sudduth, K.A. Kitchen, N.R. Myers, D.B. and Drummond, S.T., 2010 Mapping depth to argillic soil horizons using apparent electrical conductivity Journal of Environmental & Engineering Geophysics 15 135–146.CrossRefGoogle Scholar
Sumner, M.E., 1999 Handbook of Soil Science Boca Raton, Florida, USA CRC Press.Google Scholar
Triantafilis, J. Huckel, A.I. and Odeh, I.O.A., 2001 Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables Soil Science 166 415–427.CrossRefGoogle Scholar
Triantafilis, J. Gibbs, I. and Earl, N., 2013 Digital soil pattern recognition in the lower Namoi valley using numerical clustering of gamma-ray spectrometry data Geoderma 192 407–421.CrossRefGoogle Scholar
Viscarra Rossel, R.A. Taylor, H.J. and McBratney, A.B., 2007 Multivariate calibration of hyperspectral γ-ray energy spectra for proximal soil sensing European Journal of Soil Science 58 343–353.CrossRefGoogle Scholar
Vitharana, U.W.A. Van Meirvenne, M. Simpson, D. Cockx, L. and De Baerdemaeker, J., 2008 Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area Geoderma 143 206–215.CrossRefGoogle Scholar
Webster, R. and Oliver, M.A., 1992 Sample adequately to estimate variograms of soil properties Journal of Soil Science 43 177–192.CrossRefGoogle Scholar
Webster, R. and Oliver, M.A., 2007 Geostatistics for Environmental Scientists New Jersey, USA John Wiley & Sons.CrossRefGoogle Scholar
Williams, B.G. and Hoey, D., 1987 The use of electromagnetic induction to detect the spatial variability of the salt and clay contents of soils Australian Journal of Soil Research 25 21–27.CrossRefGoogle Scholar
Wilson, P.R. and Baker, D.E., 1990.Soils and agricultural land suitability of the wet tropical coast on north Queensland: Ingham area Land Resources BulletinGoogle Scholar