Hybrid machine learning model and terrain variables for spatial modeling of topsoil physicochemical properties

Abstract

Soil surveys and mapping using conventional methods are time consuming and costly, particularly for large areas, while the demand for detailed soil data continues to grow for applications in land management and precision agriculture. In this study, a hybrid Random Forest Kriging (RFK) model combining the machine learning capabilities of Random Forest (RF) with the spatial prediction strength of the Kriging geostatistical model was employed alongside three other machine learning algorithms: Artificial Neural Networks (ANN), Gradient Boosting Machines (GBM), and K-Nearest Neighbor (KNN). These models were utilized to predict the spatial distribution of five soil physicochemical properties: potential of Hydrogen (pH), Electrical Conductivity (EC), Calcium Carbonate (CaCO₃), Bulk Density (BD), and Available Water Capacity (AWC) in a region located in Central Anatolia, Turkey. The physicochemical properties of 119 soil samples collected from the study area were analyzed. One of the objectives of this study was to evaluate the effectiveness of low-scale terrain variables as predictors for soil property estimation. For this purpose, three auxiliary terrain variables digital elevation model (DEM), slope, and topographic roughness index (TRI) were derived from remote sensing data and analyzed using GIS techniques. The predicted maps were evaluated utilizing three statistical metrics: Concordance Correlation Coefficient (CCC), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the hybrid RFK model outperformed other machine learning models in predicting soil properties, achieving the best CCC values and the lowest RMSE and MAE, while the performance of the other models remained relatively similar and weak. Furthermore, the findings indicated that the hybrid model's ability to delineate the spatial patterns of soil properties was primarily influenced by elevation and slope, despite the moderate effectiveness of low-scale terrain variables as auxiliary inputs in digital soil mapping.