A National High-Resolution Digital Soil Information System for Spain Based on More Than 50,000 Harmonized Soil Observations and Machine Learning

Accurate and harmonized soil information is fundamental for sustainable agricultural management, environmental assessment, and land-use planning. This study developed a national Digital Soil Mapping (DSM) framework for mainland Spain and the Balearic Islands using a harmonized database of more than 50,000 georeferenced legacy soil observations. Seven key soil properties (clay, silt, sand, pH, organic matter, available phosphorus, and exchangeable potassium) were modelled using environmental covariates derived from Sentinel-2 imagery, terrain attributes, climate, and land-use data. Three machine-learning algorithms were evaluated, with Cubist providing the best predictive performance and therefore selected for mapping all soil properties. Model validation showed the highest accuracy for soil pH (R2 = 0.60), sand (R2 = 0.51), and organic matter (R2 = 0.50), whereas moderate performance was obtained for silt and clay. Available phosphorus and exchangeable potassium showed lower predictive accuracy, reflecting their strong dependence on management practices. The resulting maps were generated at 20 m spatial resolution and generalized to 200 m for dissemination, providing a harmonized national soil property dataset for Spain. This operational and reproducible framework supports agricultural and environmental decision-making and can be adapted for soil mapping in other regions using open-access data and open-source tools.