Mapping Swiss soil bulk density at 30 m Resolution: Insights from Machine Learning, environmental Covariates, and national data

Abstract

Soil bulk density (BD) is a key indicator of soil health and quality, influencing air and water fluxes in the soil, soil biology, plant growth, nutrient availability, and water retention. While BD is typically measured through field and lab methods, these are time-consuming and resource-intensive. Alternatively, researchers use pedotransfer functions and machine learning algorithms for BD prediction. Although several BD maps exist for Europe, Switzerland is often excluded due to its non-European Union member status, creating a data gap known as a “blank spot”. Additionally, existing Swiss BD maps have coarse spatial resolution (∼250 m). To address this, we used the national Swiss Soil Information System NABODAT dataset to produce high-resolution (30 m) BD maps at multiple depths (0, 30, 60, 100 cm) using a Quantile Random Forest algorithm. Using five-fold cross-validation, we obtained a concordance correlation coefficient (CCC) of 0.57 and an R2 of 0.42, while external validation resulted in a CCC of 0.39 and an R2 of 0.36. The maps revealed that croplands had the highest BD, followed by grasslands and forests. Regionally, the Central Plateau and Jura exhibited higher BD compared to the Alps. BD increased with depth, and key predictors were depth, elevation, and temperature. Although we initially expected surface reflectance to be a relevant predictor due to its link with organic carbon, it showed low importance in our model. These maps provide valuable insights for national-scale applications such as soil carbon stock estimation and compaction assessment.