Preparation of three-dimensional maps of soil particle size fractions by combining quantile regression forest algorithm and spline depth function in Golestan Province

There is an increasing need for continuous spatial and quantitative soil information for environmental modeling and management, especially at the national scale. This study was conducted to predict the soil particle size fraction (PSF) using the combination of quantile regression forest model (QRF) and spline function in a part of Golestan province. An equal area spline equation was fitted to the data of 105 soil profiles from the database of the Gorgan University of Agricultural Sciences and Natural Resources for estimating PSFs at five soil depths (0-25, 25-50, 50-75, 75-100, and 100-125 cm). The primary auxiliary variables in this research included 22 environmental variables derived from DEM, 15 remote sensing indicators obtained from the Landsat 7 ETM+ images, rainfall and piezometric maps. Based on principal component analysis (PCA), 15 variables were selected and entered into the modeling process of soil texture components (clay, sand, and silt). The efficiency of the quantile regression forest model was evaluated using the coefficient of determination (R2), the root mean squared error (RMSE), and the mean absolute error (MAE). The results indicated that the coefficient of determination for clay, silt, and sand at different depths varied from 0/12 to 0/22, 0/07 to 0/30, and 0/07 to 0/28, respectively. Also, the relative importance of environmental variables showed that rainfall (thirty-year average), piezometry (ten-year average), B3/B7, and valley depth were the most important factors in predicting soil texture components. To improve model performance and validation results, some structural uncertainties in this study should be addressed.