Toward Pan-European Mapping of Soil Water Retention Curves Using a Physics-informed Machine Learning Approach: Insights from Denmark

The soil water retention curve (SWRC) is a fundamental soil property that provides information about soil structure, soil texture, plant water availability, drainage, and compaction, and is therefore highly linked to soil functions and soil health. Current large-scale digital maps of the SWRC are typically developed indirectly through a two-step process: i) the development of pedotransfer functions (PTFs) that establish relationships between basic soil properties such as textural fractions, bulk density, organic matter content, and parameters of well-known SWRC models, and ii) the application of these PTFs to basic soil property maps at various scales. This presentation introduces a novel, physically constrained machine learning approach for directly mapping the entire SWRC from saturation to oven-dryness. Unlike previous studies, our new approach neither relies on PTFs nor is limited to a specific SWRC model. Instead, it estimates a non-specific form of the SWRC, learned from both measurements and physical constraints. Applying this method to 1,261 soil profiles across Denmark, encompassing 4,747 measured SWRCs, demonstrates its superior performance compared to established methods. The new approach enables the aggregation of datasets with sparse and incomplete SWRC measurements, which are typically unusable with conventional methods. This capability maximizes spatial coverage and reduces uncertainties in the final predicted maps. Additionally, the new approach addresses the commonly observed imbalance between wet and dry-end measurements in large SWRC datasets. Following a detailed report on the results of our approach for Denmark, we discuss ongoing efforts and progress toward applying this method to SWRC mapping at the European scale.