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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]
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Abstract

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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

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