Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland

• Published in: Geoderma Regional Vol. 27; p. e00437
• Main Authors: Baltensweiler, Andri, Walthert, Lorenz, Hanewinkel, Marc, Zimmermann, Stephan, Nussbaum, Madlene
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021
• Subjects: biodiversity; carbon sequestration; clay; data collection; Digital soil mapping; ecosystems; Forest soils; forests; geostatistics; gravel; kriging; landscapes; Machine learning; Model averaging; prediction; Quantile regression forest; regression analysis; sand; soil density; soil depth; soil organic carbon; Switzerland; uncertainty; Uncertainty maps; water purification; Switzerland; Quantile regression forest; Uncertainty maps; Digital soil mapping; Model averaging; Machine learning; Forest soils

Spatial soil information in forests is crucial to assess ecosystem services such as carbon storage, water purification or biodiversity. However, spatially continuous information on soil properties at adequate resolution is rare in forested areas, especially in mountain regions. Therefore, we aimed t...