Spatial prediction of soil properties from landform attributes derived from a digital elevation model

Abstract

Digital elevation models (DEMs) provide a good way of deriving landform attributes that may be used for soil prediction. The geostatistical techniques of kriging and cokriging are increasingly being applied to predicting soil properties. Whereas ordinary kriging (and universal kriging) utilise spatial correlation to determine the coefficients of the linear predictor, cokriging involves both inter-variable correlation and spatial covariation among variables. Multi-linear regression modelling also offers an alternative to predicting a soil variable by means of covariation. The performance of predicting four soil variables by these methods and two regression-kriging models are compared. The precision and bias of prediction of the six methods were dependent on the soil variable predicted. The mean error of prediction indicates reasonably small bias of prediction for all the soil variables by almost all of the methods. With the exception of topsoil gravel, for which multi-linear regression performed best, the root mean square error showed the two regression-kriging procedures to be best. Further analysis based on the mean ranks of performance by the methods confirmed this. All the kriging methods involving covariables (landform attributes) have a more smoothing effect on the predicted values, thus minimising the influence of outliers on prediction performance. Both the methods of regression-kriging show promise for predicting sparsely located soil properties from dense observations of landform attributes derived from the DEM. Histograms of subsoil clay residuals show outliers in the data set. These outliers are more evident in multi-linear regression, ordinary kriging and universal kriging than regression-kriging. There was a clear advantage in using the regression-kriging methods on those variables which had a small correlation with the landform attributes: root mean square errors for all the soil variables are much smaller than those resulting from any of the multi-linear regression, ordinary kriging, universal kriging or cokriging methods.