Supporting the EU Soil Monitoring Law Through Digital Soil Mapping: Evaluating Random Forest-Based Approaches in Tuscany (Italy)

Digital soil mapping (DSM) supports the implementation of the EU Soil Monitoring Law by providing spatially explicit information on key soil properties over large areas. Despite widespread use of machine learning techniques, the influence of modelling strategy and covariate quality on predictive performance remains unclear in heterogeneous regions. This study evaluated three Random Forest-based approaches, Random Forest (RF), Quantile Random Forest (QRF), and a regime-based model (RF-REGIME5), to predict topsoil properties (0–30 cm) across Tuscany (23,000 km2). Two predictor sets were tested: BASE (environmental covariates only) and AUGMENTED (with legacy soil data). Within the three RF-based approaches examined, the inclusion of legacy soil-derived predictors produced more consistent improvements in predictive performance than changes in modelling strategy. Soil-derived covariates improved predictive performance, while differences among modelling approaches were generally limited and property-dependent. QRF performed comparably and additionally quantified prediction uncertainty, especially for skewed variables like soil organic carbon. In contrast, the regime-based approach did not consistently outperform global models, as potential benefits from reduced environmental heterogeneity were offset by smaller sample sizes within individual domains. Overall, improving predictor datasets and expanding soil observation networks appear more effective than increasing model complexity for enhancing regional DSM applications and supporting future soil monitoring efforts.