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

Aug 5, 2026

Upload by Asher

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 northern landscapes shaped by glacial processes, parent materials are mainly Quaternary deposits. This study assessed machine learning to accelerate soil parent material mapping in Sweden using Extreme Gradient Boosting with LiDAR-derived terrain and hydrological indices, plus an enhanced version including ancillary map data, validated on hold-out soil observations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Parent materials and their environmental importance

Challenges with existing near-surface representations

Conventional mapping workflows and data integration

LiDAR for geomorphological and geological mapping

Geoderma Regional 40 (2025) e00905 Contents lists available at ScienceDirect Geoderma Regional journal [homepage:](homepage: www.elsevier.com/locate/geodrs)[ www.elsevier.com/locate/geodrs](homepage: www.elsevier.com/locate/geodrs) | Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping
Yiqi Lin a,*, William Lidberg a, Cecilia Karlsson b, Gustav Sohleniusb, Florian Westphal a,c, Johannes Larson a, Anneli M. Ågren a
a Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Skogsmarksgra¨nd 17, 901 83 Umeå, Sweden b Geological Survey of Sweden, Villava¨gen 18, 752 36 Uppsala, Sweden
c Department of Computing, J¨onk¨oping University, Gjuterigatan 5, 553 18 J¨onk¨oping, Sweden | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Digital soil mapping Soil parent materials Airborne laser scanning Machine learning Extreme gradient boosting | | 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. | 1. Introduction Parent materials (PMs) are the initial state of the soil system (Jenny, 1994) and have a major influence on soil properties, which in turn affect nutrient availability, hydrology, and land stability (Anderson, 1988; Richter et al., 2019). Understanding the distribution of PMs is crucial for land-use planning, infrastructure construction, resource exploration, and geohazard recognition, as they carry significant environmental and societal impacts (Bernknopf et al., 1983; McMillan, 2002; H¨aggquist and S¨oderholm, 2015). PM can be broadly categorized as either primary insitu or secondary transported material, such as alluvium, colluvium, aeolian, or glacial deposits (Gray and Murphy, 1999). In temperate regions, most soils have developed on soft rocks, or on unconsolidated sediments formed during the Quaternary period (the past 2.6 million years) through processes like erosion and deposition (Anderson, 1988). During glacial and postglacial times, these processes have created a mosaic of deposits at the surface that are different from the underlying bedrock geology (Lawley and Smith, 2008; Heung et al., 2014). The poor representation of the near-surface materials in existing geological maps often provide an erroneous view of soil PMs, further limiting their usefulness for soil modeling (Lawley and Smith, 2008; Lemercier et al., 2012). Producing PM maps (or often referred to as Quaternary Deposit (QD) maps in previously glaciated regions) is a labor-intensive, iterative process that often involves the collation and synthesis of diverse data sources. This of

FAQs

Why is mapping soil parent materials important?

Soil parent materials strongly influence soil properties that affect nutrient availability, hydrology, and land stability, supporting planning, infrastructure, resource exploration, and geohazard recognition.

How did the study use machine learning for parent material mapping?

Two Extreme Gradient Boosting models were trained, one with LiDAR-derived terrain and hydrological indices and another that added ancillary map data, then evaluated against a separate hold-out set.

What did the results show about model performance?

The machine learning models achieved higher Matthews Correlation Coefficient values than the existing most detailed baseline maps, and performance varied by region and deposit type, with till and peat performing better than sorted sediments.

Aug 5, 2026

Upload by Asher