Evaluating diagnostic horizons as indicators of ecosystem services through digital soil mapping in urban Gauteng, South Africa

Urban areas are strongly influenced by the ecosystem services provided by soil, which help mitigate environmental pressures generated by metropolises themselves and protect surrounding natural and agricultural landscapes. However, it remains unclear whether existing soil classification systems adequately capture the complex multifunctionality of soil that produce these services. This study examined the spatial patterns and ecosystem services represented in a numerical and South Africa’s taxonomic (“Blue Book”) soil classification system in Gauteng, South Africa (~ 26.3° S, 28.1° E), a region characterised by extensive urban development. A Gradient Tree Boosting model implemented in Google Earth Engine was used to predict topsoil and subsoil horizons for both systems using identical soil observations. The model achieved high spatial accuracy for topsoil and moderate accuracy for subsoil horizons in both systems. Despite this, the topsoil predictions of both systems (accuracy > 85%) showed limited relevance to ecosystem services across the urban landscape. In contrast, the subsoil predictions (accuracy ~ 54–68%) of the taxonomic system exhibited clear spatial patterns, greater interpretability and stronger ecological relevance. The primary limitation identified was the over-classification of the Ochric topsoil together with the moderate accuracy of the subsoil, obscured functional differences and constrained the interpretation of soil-based ecosystem services across Gauteng. Nevertheless, emerging numerical frameworks that integrate categorical data and dynamic processes, supported by advances in Earth observation and machine learning, are expected to enhance the ability to map soil multifunctionality and its representation of ecosystem services.