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On February 26, 2024 at 10:13:38 AM UTC, Gravatar Administrator:

  • Set maintainer of Soil property maps for the Swiss forest to {"affiliation": "", "email": "andri.baltensweiler@wsl.ch", "given_name": "Andri", "identifier": "", "name": "Baltensweiler"} (previously {"email":"andri.baltensweiler@wsl.ch","given_name":"Andri","name":"Baltensweiler"})


  • Set author of Soil property maps for the Swiss forest to [{"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "email": "andri.baltensweiler@wsl.ch", "given_name": "Andri", "identifier": "0000-0003-1933-6535", "name": "Baltensweiler"}, {"affiliation": "Swiss Federal Institute for Forest Snow and Landscape Research WSL, Zuercherstrasse 111, 8903 Birmensdorf, Switzerland", "affiliation_02": "", "affiliation_03": "", "email": "lorenz.walthert@wsl.ch", "given_name": "Lorenz", "identifier": "0000-0002-1790-8563", "name": "Walthert"}, {"affiliation": "Albert-Ludwigs-Universit\u00e4t Freiburg ", "affiliation_02": "", "affiliation_03": "", "email": "marc.hanewinkel@ife.uni-freiburg.de", "given_name": "Marc", "identifier": "0000-0003-4081-6621", "name": "Hanewinkel"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "email": "stephan.zimmermann@wsl.ch", "given_name": "Stephan", "identifier": "0000-0002-8904-9024", "name": "Zimmermann"}, {"affiliation": "Utrecht University", "affiliation_02": "", "affiliation_03": "", "email": "m.nussbaum@uu.nl", "given_name": "Madlene", "identifier": "0000-0002-6808-8956", "name": "Nussbaum"}] (previously [{"given_name": "Andri", "name": "Baltensweiler", "email": "andri.baltensweiler@wsl.ch", "data_credit": [], "identifier": "0000-0003-1933-6535", "affiliation": "WSL"}, {"given_name": "Lorenz", "name": "Walthert", "email": "lorenz.walthert@wsl.ch", "data_credit": [], "identifier": "0000-0002-1790-8563", "affiliation": "Swiss Federal Institute for Forest Snow and Landscape Research WSL, Zuercherstrasse 111, 8903 Birmensdorf, Switzerland"}, {"given_name": "Marc", "name": "Hanewinkel", "email": "marc.hanewinkel@ife.uni-freiburg.de", "data_credit": [], "identifier": "0000-0003-4081-6621", "affiliation": "Albert-Ludwigs-Universit\u00e4t Freiburg "}, {"given_name": "Stephan", "name": "Zimmermann", "email": "stephan.zimmermann@wsl.ch", "data_credit": [], "identifier": "0000-0002-8904-9024", "affiliation": "WSL"}, {"given_name": "Madlene", "name": "Nussbaum", "email": "m.nussbaum@uu.nl", "data_credit": [], "identifier": "0000-0002-6808-8956", "affiliation": "Utrecht University"}])


  • Updated description of Soil property maps for the Swiss forest from

    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](https://www.isric.org/) 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:** [www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps)
    to
    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](https://www.isric.org/) 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:** [www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).


  • Changed the version of Soil property maps for the Swiss forest to 1.0


  • Changed value of field publication to {"publication_year": "2024", "publisher": "EnviDat"} in Soil property maps for the Swiss forest


  • Changed value of field funding to [{"grant_number": "", "institution": "WSL", "institution_url": ""}, {"grant_number": "", "institution": "BAFU", "institution_url": ""}] in Soil property maps for the Swiss forest


  • Changed value of field date to [{"date": "2021-08-29", "date_type": "created", "end_date": "2021-08-29"}] in Soil property maps for the Swiss forest


  • Changed value of field related_publications to 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](https://doi.org/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](https://doi.org/10.3188/szf.2022.0288) in Soil property maps for the Swiss forest