Spatial prediction and uncertainty estimation of crucial GlobalSoilMap properties - A contextual study in the semi-arid area of western Iran

Abstract

Information on the spatial distribution of organic carbon (OC), salinity (EC), and soil pH in the semi-arid region of the Chahardowli Plain in western Iran is limited. Though soil carbon is the most common property mapped in GlobalSoilMap, EC, and pH affect OC content and other soil properties and functions. To study variations in these properties and map their distribution, soil samples were collected at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm. A total of 145 soil samples were collected from 30 profiles. The relationships between soil characteristics and environmental covariates were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. We used a k-fold cross-validation to assess the quality of the predictions. The RF model demonstrated the highest prediction accuracy for all three soil properties. The OC validation results for the RF model show that the R2 value was between 0.80 and 0.98, the R2 value for EC was between 0.74 and 0.98, and the R2 value for pH was between 0.80 and 0.93. The channel network base level (CNBL) was found to be the most crucial covariate in predicting EC, while CNBL and vegetation indices were the most significant covariates in predicting OC. The covariates found to be crucial in predicting pH were the difference in vegetation index (DVI) and slope (S). The bootstrap method was used to compute the prediction uncertainty. The bootstrap method provided reliable estimates of uncertainties associated with these predictions. For all layers and all points, the coverage percentage for OC, EC, and pH was between 80 and 95% at the 95% confidence level. This shows the reliability of the estimated confidence limits. This study indicated that high pH and EC levels are associated with a reduction in soil OC percentage. To provide an accurate representation of OC distribution in any region, it is necessary to report not only an OC map but also maps of EC and pH, as the spatial interrelationship between soil properties highlights the need for estimating pH and EC for better understanding OC variability and soil functioning.