Spatial Prediction of Soil Saturated Hydraulic Conductivity by Integrating Soil Properties and Environmental Covariates

Articles in Press

Document Type : Research Paper

Authors

1 Department of Soil Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Department of Soil Sciences, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

Abstract

Soil hydraulic properties, particularly saturated hydraulic conductivity (Ks), play a crucial role in addressing problems related to soil and water management in agricultural, ecological, and environmental systems. Direct measurement of these properties is often difficult, costly, and time-consuming; hence, indirect estimation methods are commonly employed. In this study, the efficiency of multiple linear regression (MLR), decision tree (DT), and artificial neural network (ANN) methods was evaluated for estimating and mapping the spatial distribution of Ks in parts of the Cherdawel and Chamshir sub-basins (Ilam Province, Iran), using readily measurable soil properties along with environmental covariates (terrain attributes and remote sensing data). For this purpose, Ks was measured at 95 sampling points using a Guelph permeameter, and several readily measurable soil properties along with environmental covariates were also obtained at the same locations. The validity of the derived models for Ks estimation was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean error (ME). The results demonstrated that the ANN model outperformed both MLR and DT models in estimating Ks. While the MLR and DT tended to underestimate Ks, the ANN model produced more accurate and reliable predictions. Furthermore, the spatial variability map of Ks could be successfully generated by integrating soil properties with environmental covariates, suggesting the usefulness of this approach for developing agro-hydrological models in data-limited regions. Overall, the findings suggest that incorporating environmental covariates alongside soil properties can significantly enhance the accuracy of Ks estimation, particularly when using the ANN model.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 02 June 2026

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