High-resolution digital soil mapping of multiple soil properties: an alternative to the traditional field survey?

Abstract

Spatial information on soil particle size distribution and soil organic carbon (SOC) are important for land-use management, environmental models and policy-making. Digital soil mapping (DSM) techniques can quantitatively predict these soil properties using minimal resources. However, DSM has not been adequately evaluated at the farm-scale. The aim of this study was to optimise the DSM framework to produce farm-scale soil maps for 366 ha in the Sandspruit catchment, Western Cape, South Africa. Four feature selection techniques and eight predictive models were evaluated on their ability to predict particle size distribution and SOC. A boosted linear feature selection produced the highest accuracy for all but one soil property. The top-performing predictive models were robust linear models for gravel (ridge regression, RMSE 9.01%, R ² 0.75), sand (support vector machine, RMSE 4.69%, R ² 0.67), clay (quantile regression, RMSE 2.38%, R ² 0.52) and SOC (ridge regression, RMSE 0.19%, R ² 0.41). Random forest was the best predictive model for silt content (RMSE 4.12%, R ² 0.53). This approach appears to be robust for farm-scale soil mapping where the number of observations is often small but high-resolution soil data are required.

Keywords:

• digital soil mapping
• farm-scale
• feature selection
• high resolution
• machine learning