Soil property maps for the Swiss forest

We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.

A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the GlobalSoilMap.Net specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.

The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.

The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:

Funding Information:

This work was supported by:
  • WSL
  • BAFU

Related Publications

Baltensweiler A., Walthert L., Hanewinkel M., Zimmermann S., Nussbaum M. (2021) Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland Geoderma Reg. 27, e00437 (13 pp.). doi:10.1016/j.geodrs.2021.e00437

Baltensweiler A., Walthert L., Zimmermann S., Nussbaum M. (2022) Hochauflösende Bodenkarten für den Schweizer Wald Schweiz. Z. Forstwes. 173(6), 288-291. doi:10.3188/szf.2022.0288


Baltensweiler, Andri; Walthert, Lorenz; Hanewinkel, Marc; Zimmermann, Stephan; Nussbaum, Madlene (2024). Soil property maps for the Swiss forest. EnviDat. doi:10.16904/envidat.484.

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Data and Resources


Field Values
DOI 10.16904/envidat.484
Publication State Published
  • Email: andri.baltensweilerfoo(at) ORCID: 0000-0003-1933-6535 Given Name: Andri Family Name: Baltensweiler Affiliation: WSL
  • Email: lorenz.walthertfoo(at) ORCID: 0000-0002-1790-8563 Given Name: Lorenz Family Name: Walthert Affiliation: Swiss Federal Institute for Forest Snow and Landscape Research WSL, Zuercherstrasse 111, 8903 Birmensdorf, Switzerland
  • Email: marc.hanewinkelfoo(at) ORCID: 0000-0003-4081-6621 Given Name: Marc Family Name: Hanewinkel Affiliation: Albert-Ludwigs-Universität Freiburg
  • Email: stephan.zimmermannfoo(at) ORCID: 0000-0002-8904-9024 Given Name: Stephan Family Name: Zimmermann Affiliation: WSL
  • Email: m.nussbaumfoo(at) ORCID: 0000-0002-6808-8956 Given Name: Madlene Family Name: Nussbaum Affiliation: Utrecht University
Contact Person Given Name: Andri Family Name: Baltensweiler Email: andri.baltensweilerfoo(at)
Publication Publisher: EnviDat Year: 2024
  • Type: Created Date: 2021-08-29 End Date: 2021-08-29
Version 1.0
Type dataset
General Type Dataset
Language English
Location Switzerland
Content License Creative Commons Attribution Share-Alike (CC-BY-SA)    [Open Data]
Last Updated February 26, 2024, 11:55 (UTC)
Created February 23, 2024, 11:31 (UTC)