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In arid regions, knowledge of the variation in soil texture is crucial for land management because it affects soil physical, chemical, biological and most importantly hydrological properties. The availability of information on soil texture is scarce even though it is required to support land-use management and sustainable development. Because it is costly to obtain information about the individual particle-size fractions (PSFs), we used digital soil mapping methods (DSM) with environmental covariates that are less costly to obtain. Specifically, we explored the use of a digital elevation model and remote sensing data as environmental covariates to predict the vertical (i.e. 0-0.15, 0.15-0.3, 0.3-0.6 and 0.6-1m) and lateral variation in PSFs over a 150-km(2) area in central Iran. We used a combination of equal-area spline depth functions and three data-mining techniques: multiple linear regression (MLR), artificial neural networks (ANN) and the neuro-fuzzy inference system (ANFIS). In addition, we explored the effect of the reduction in dimension of feature space with ant colony optimization (ACO) and correlation-based feature selection (CFS) on the accuracy of prediction of spatial models for each PSF. The results showed that the prediction of clay at 0-0.15-m depth with ACO indicated the importance of including Landsat ETM+, the digital numbers of band 7 of Landsat images (B7) and clay index, whereas at 0.60-1-m depth the wetness index and multi-resolution valley bottom flatness index (MRVBF) were important. Model evaluation by leave-one-out cross-validation with 191 soil observations indicated that the predictions by the ACO-based ANFIS model (RMSE=4.51% and R-2=0.74 for clay at 0-0.15-m) were more accurate than those by MLR and ANN. Spatial prediction was also better for the topsoil (0-0.15-m) than at depth (RMSE=7.1% for clay at 0.6-1m); therefore, we conclude that the environmental covariates tested cannot resolve subsurface variation as accurately. Nevertheless, we recommend prediction by the ACO-based ANFIS model and splines of lateral and vertical distribution of PSFs in other arid regions of Iran with the same agro-ecological conditions.

Digital soil mapping of particle size-fractions (PSF) by adaptive neuro-fuzzy inference and ant colony optimization.

Use of ant colony optimization (ACO) to assist in feature selection of environmental covariates.

Neuro-fuzzy inference system (ANFIS) superior to multiple linear regression (MLR) and artificial neural networks (ANN).

PSF prediction by ACO-based ANFIS model and splines is optimal.