Spatial distribution of soil class and soil pH in the Thompson-Okanagan region, British Columbia

Soils are facing great threats from climate change and anthropogenic activities. It is essential to understand the characteristics of soils, such as class and pH, especially when it comes to the issue of evaluating soil quality. In the Thompson–Okanagan region, previous soil surveys covered most parts of the region in polygon data form; however, it would be beneficial if soil data were available at a finer resolution and with uniform soil categories. The digital soil mapping (DSM) approach has shown promising results over various landscapes with limited available data. The main objective of this study was to use an ensemble learning approach to map the spatial distribution of soil classes and soil pH at 25-meter resolution in the Thompson-Okanagan region, BC. Random Forest (RF) was used to map 16 soil subgroups. Overall prediction accuracy was 65.4% with an independent validation dataset. The study of spatial patterns of soil pH was tested with a combination of multiple base learners, which included a Multilinear Regression (GLM) learner, Stepwise Regression (STEP) learner, Lasso and Elastic-Net regularized Generalized Linear Regression (GLMNET) learner, a Kernel-based Support Vector Machine (KSVM) learner, and RF. Base learners with higher prediction accuracy were used to develop a Super Learner (SL). The fitted SL was then used to predict soil pH for three depth intervals (0 – 5cm, 5 – 15cm, and 15 – 30cm) at 25–meter resolution. For all three depth intervals, the SL proved to have the lowest MSE value and better prediction accuracy than was obtained from just using one of the base learners.