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The significance of multi‐scale geomorphometric algorithms and third‐order morphometric variables was studied in soil thickness modeling. In this research, the algorithms of Evans–Young, Shary, Zevenbergen–Thorne, and Florinsky were employed to calculate morphometric variables on different neighborhood scales. The results demonstrate that the polynomial model and neighborhood scale can have a remarkable effect on morphometric variables. Based on correlation analysis, we observed that on a given neighborhood scale, not every polynomial model might be appropriate for modeling soil thickness. A geomorphometric algorithm that provides a modest smoothing rate of morphometric variables can be ideal for soil–landscape modeling. The results revealed that Gnn, a third‐order morphometric variable, was the most relevant predictor of soil thickness. The third‐order morphometric variables have the potential to be considered for soil–landscape modeling. Multivariate adaptive regression splines provided a model (R2 = 0.71, RMSE = 0.28 m) for predicting soil thickness based on local morphometric variables that were calculated using two types of polynomial models. Based on the findings of this study, it is recommended that several polynomial models and neighborhood sizes be employed when performing soil–landscape analysis. Consideration of multi‐scale geomorphometric analysis can increase the performance of digital soil modeling.

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In recent decades, water erosion potential has been recognized as a severe threat to soil sustainability and water resources. The present study was conducted to investigate the relation between geomorphometric parameters and soil type to simulate water erosion in the Emamzadeh watershed located in the northeast of Khuzestan Province. The primary and secondary geomorphic parameters, including slope, plan curvature, profile curvature, flow length, flow accumulation, flow direction, and stream power index (SPI) were calculated based on the digital elevation model (DEM). The water erosion was measured using available data and laboratory analyzes, then it was predicted with the water erosion prediction project (WEPP) model. Our results revealed that the measured soil erosion does not show any relation with geomorphic parameters, while some of the geomorphometric parameters depicted a significant relation with WEPP model's predictions. A model with an excellent explanation coefficient was obtained using multivariate linear regression to predict water erosion. The geomorphometric parameters application allows an estimation of erosion based on simple linear models (R 2 : 0.934, sig: 0.000). Moreover, for SPI, the total curvature was -0.794, plan curvature was -0.658, and profile curvature was 0.746. Therefore, there was a relation between curvature and SPI. Our results showed no specific relation between sediment transport index (STI) and water erosion. The low amount of STI represents the sedimentation areas in the watershed. Generally, application of geomorphometric parameters simplify the soil erosion prediction.

This study was aimed to address the importance of neighborhood scale and using bedrock topography in the soil-landscape modeling in a low-relief large region. For this study, local topographic attributes (slopes and curvatures) of the ground surface (DTM) and bedrock surface (DBM) were derived at five different neighborhood sizes (3×3, 9×9, 15×15, 21×21, and 27×27). Afterward, the topographic attributes were used for multivariate adaptive regression splines (MARS) modeling of solum thickness. The results demonstrate that there are statistical differences among DTM and DBM morphometric variables and their correlation to solum thickness. The MARS analyses revealed that the neighborhood scale could remarkably affect the soil–landscape modeling. We developed a powerful MARS model for predicting soil thickness relying on the multi-scale geomorphometric analysis (R2= 83%; RMSE= 12.70 cm). The MARS fitted model based on DBM topographic attributes calculated at a neighborhood scale of 9×9 has the highest accuracy in the prediction of solum thickness compared to other DBM models (R2 = 61%; RMSE = 19cm). This study suggests that bedrock topography can be potentially utilized in soil-related research, but this idea still needs further investigations.