Comparing traditional and digital soil mapping at a district scale using residual maximum likelihood analysis
E. Zare A , M. F. Ahmed A , R. S. Malik B , R. Subasinghe C , J. Huang A and J. Triantafilis A D
+ Author Affiliations
- Author Affiliations
A School of Biological, Earth and Environmental Sciences, UNSW Sydney, Kensington NSW 2052, Australia.
B Department of Agriculture and Food Western Australia, 10 Dore St, Katanning, WA 6317, Australia.
C Department of Agriculture and Water Resources, Canberra, ACT 2601, Australia.
D Corresponding author. Email: j.triantafilis@unsw.edu.au
Soil Research 56(5) 535-547 https://doi.org/10.1071/SR17220
Submitted: 21 August 2017 Accepted: 31 March 2018 Published: 6 July 2018
Abstract
Conventional soil mapping uses field morphological observations to classify soil profiles into predefined classification systems and extrapolates the classified soils to make a map based on aerial photographs and the experience of the surveyor. A criticism of this approach is that the subjectivity of the surveyor leads to non-reproducible maps. Advances in computing and statistical analysis, and an increased availability of ancillary data have cumulatively led to an alternative, referred to as digital soil mapping (DSM). In this research, two agriculturally productive areas (i.e. Warren and Trangie) located in central New South Wales, Australia, were considered to evaluate whether pedoderms and soil profile classes defined according to the traditional approach can also be recognised and mapped using a DSM approach. First, we performed a fuzzy k-means analysis to look for clusters in the ancillary data, which include data from remote-sensed gamma-ray (γ-ray) spectrometry and proximal-sensed electromagnetic (EM) induction. We used the residual maximum likelihood method to evaluate the maps for various numbers of classes (k = 2–10) to minimise the mean square prediction error (σ2p,C) of soil physical (i.e. clay content, field capacity (FC), permanent wilting point (PWP) and available water content (AWC)) and chemical (pH, EC of 1 : 5 soil water extract (EC1:5) and cation exchange capacity (CEC)) properties of topsoil (0–0.3 m) and subsoil (0.6–0.9 m). In terms of prediction, the calculated σ2p,C was locally minimised for k = 8 when accounting for topsoil clay, FC, PWP, pH and CEC, and subsoil FC, EC1:5 and CEC. A comparison of σ2p,C of the traditional (seven pedoderm components) and DSM approach (k = 8) indicated that only topsoil EC1:5 and subsoil pH was better accounted for by the traditional approach, whereas topsoil clay content, and CEC and subsoil clay, EC1:5 and CEC were better resolved using the DSM approach. The produced DSM maps (e.g. k = 3, 6 and 8) also reflected the pedoderm components identified using the traditional approach. We concluded that the DSM maps with k = 8 classes reflected the soil profile classes identified within the pedoderms and that soil maps of similar accuracy could be developed from the EM data independently.
Additional keywords: electromagnetic induction, fuzzy k-means clustering, gamma-ray spectrometry, linear mixed model.
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