Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping

Abstract

Reliable information on soil-forming parent materials is crucial for informed decision-making in infrastructure planning, land-use management, environmental assessments, and geohazard mitigation. In the northern landscapes previously affected by glacial processes, these parent materials are predominantly Quaternary deposits. This study explored the potential of machine learning to expedite soil parent material mapping in Sweden. Two Extreme Gradient Boosting models were trained, one using terrain and hydrological indices derived from Light Detection and Ranging data, and the other incorporating additional ancillary map data. Both models were trained on 29,588 soil observations and evaluated against a separate hold-out set of 3500 observations. As a baseline, the existing most detailed maps achieved a Matthews Correlation Coefficient of 0.36. The Extreme Gradient Boosting models achieved higher MCC values of 0.45 and 0.56, respectively. To understand spatial variations in model performance, the second model was evaluated across 28 physiographic regions in Sweden. The results revealed that model performance varied across regions and deposit types, with till and peat exhibiting better performance than sorted sediments. These findings underscore the need for region-specific analyses to optimize the application of machine learning in digital soil mapping.