Digital mapping of soil properties with optimally scaled predictors

Abstract

Although improved and more effective approaches for predicting the spatial distribution of soils have long been developed, further study in this field is still required. This study proposes an algorithm for generating terrain attributes over several scales and automatically selecting the optimal scale for each predictor using the powerful Random Forests (RF) method, to increase the accuracy of soil property maps. The effectiveness of using optimal scaled predictors to improve the accuracy of soil maps is investigated on nine soil properties (clay, silt, and sand content within the 0-20 cm range, soil porosity within the 0-20 cm range, subsoil porosity, pH within the 0-20 cm range, edaphic volume, humus reserve, and base saturation within the 0-20 cm range). Experiments were carried out in two study areas in western Romania, located along the boundary between the Western Plain and the Western Hills. The first study area contains 96 georeferenced soil profiles, while the second has 92. The initial 12.5 m digital elevation model (DEM) was resampled to 25 m, then in 25 m increments to 1000 m, resulting in 40 coarser versions of the DEM. Each rescaled version of the DEM was used to derive 10 terrain attributes, resulting in 40 rescaled versions of each terrain attribute. Next, a Random Forest (RF) and a linear correlation model with each scaled terrain attribute were created using soil property values. The highest R-squared value and correlation coefficient, respectively, are used by the script to produce two sets of optimally scaled terrain attributes. All multiscale predictors, optimally scaled predictors based on the RF model, optimally scaled predictors based on the correlation coefficient, and original not scaled predictors were the four groups of predictors used to map each soil attribute. The results showed that when the predictors are optimally scaled compared to maps produced with the original unscaled predictors or with all multiscale predictors, more accurate and less uncertain soil property maps are obtained.