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RESEARCH ARTICLE

Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia

K. W. Holmes A B C E , E. A. Griffin A and N. P. Odgers D
+ Author Affiliations
- Author Affiliations

A Department of Agriculture and Food Western Australia, 3 Baron-Hay Court, South Perth, WA 6151, Australia.

B CSIRO Sustainable Agriculture Flagship, Brisbane, Qld 4001, Australia.

C Soil Matrix Group, School of Earth and Environment, The University of Western Australia, Crawley, WA 6009, Australia.

D Faculty of Agriculture and Environment, C81 Biomedical Building, The University of Sydney, NSW 2006, Australia.

E Corresponding author. Email: karen.holmes@agric.wa.gov.au

Soil Research 53(8) 865-880 https://doi.org/10.1071/SR14270
Submitted: 29 September 2014  Accepted: 4 March 2015   Published: 2 November 2015

Abstract

Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5 × 106 km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43 000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.

Additional keywords: digital soil mapping, global digital soil map, legacy soil maps, resampled classification trees, scale, uncertainty, validation.


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