JWSS - Journal of Water and Soil Science

Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.

Soil classification in a standard system is usually defined based on information obtained from properties and their variations in different map units. The aim of this study was to compare soil genesis and morphological characteristics in different landforms with WRB and Soil Taxonomy (ST) Systems. From nine studied profiles, six profiles were selected as representative profiles and dug in Colluvial fans, Piedmont plain, and Alluvial plain physiographic units, respectively. Then, the soils were classified according to the pattern of the two systems. Also, variation analysis of variance (ANOVA) and comparing means were used to quantify interested soil properties. The results of soil physio-chemical properties at different landform positions were significant based on analysis of variance of the effect of physiographic units and soil depth at the level of 1 %. Soil classification results based on WRB indicated that WRB were recognized four reference soil groups (RSG) included Regosols, Cambisols, Calcisols, and Gleysols at the first level of WRB classification in comparison of ST with recognizing two order Entisols and Inceptisols could separate more soils. The soils were located on the alluvial plain with a high groundwater level in the WRB due to the creation of restrictive conditions for root development in contrast to the ST called “Aquepts” in the suborder level but in a WRB is classified as the “Gleysols” RSG. On the other hand, ST, unlike WRB, used the Shallow criteria at the family level to describe the shallowness of soils and the limitations of root development. Generally, the efficiency of each system varies despite the differences in their structure and depending on the purpose of using them.