{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:10:02Z","timestamp":1775103002275,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T00:00:00Z","timestamp":1675468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Research Foundation (DFG)","award":["390727645"],"award-info":[{"award-number":["390727645"]}]},{"name":"German Research Foundation (DFG)","award":["SFB 1070"],"award-info":[{"award-number":["SFB 1070"]}]},{"name":"German Research Foundation (DFG)","award":["subprojects Z, S and B02"],"award-info":[{"award-number":["subprojects Z, S and B02"]}]},{"name":"German Research Foundation (DFG)","award":["EXC 2064\/1"],"award-info":[{"award-number":["EXC 2064\/1"]}]},{"name":"German Research Foundation 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2064\/1"]}]},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u2014New Perspectives for Science\u201d","award":["SCHO 739\/21-1"],"award-info":[{"award-number":["SCHO 739\/21-1"]}]},{"name":"DFG project \u201cMLTRANS\u2014Transferability of machine learning models for digital soil mapping\u201d","award":["390727645"],"award-info":[{"award-number":["390727645"]}]},{"name":"DFG project \u201cMLTRANS\u2014Transferability of machine learning models for digital soil mapping\u201d","award":["SFB 1070"],"award-info":[{"award-number":["SFB 1070"]}]},{"name":"DFG project \u201cMLTRANS\u2014Transferability of machine learning models for digital soil mapping\u201d","award":["subprojects Z, S and B02"],"award-info":[{"award-number":["subprojects Z, S and B02"]}]},{"name":"DFG project \u201cMLTRANS\u2014Transferability of machine learning models for digital soil mapping\u201d","award":["EXC 2064\/1"],"award-info":[{"award-number":["EXC 2064\/1"]}]},{"name":"DFG project \u201cMLTRANS\u2014Transferability of machine learning models for digital soil mapping\u201d","award":["SCHO 739\/21-1"],"award-info":[{"award-number":["SCHO 739\/21-1"]}]},{"name":"Open Access Publishing Fund of the University","award":["390727645"],"award-info":[{"award-number":["390727645"]}]},{"name":"Open Access Publishing Fund of the University","award":["SFB 1070"],"award-info":[{"award-number":["SFB 1070"]}]},{"name":"Open Access Publishing Fund of the University","award":["subprojects Z, S and B02"],"award-info":[{"award-number":["subprojects Z, S and B02"]}]},{"name":"Open Access Publishing Fund of the University","award":["EXC 2064\/1"],"award-info":[{"award-number":["EXC 2064\/1"]}]},{"name":"Open Access Publishing Fund of the University","award":["SCHO 739\/21-1"],"award-info":[{"award-number":["SCHO 739\/21-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R2 = 0.68\/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models.<\/jats:p>","DOI":"10.3390\/rs15040876","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"876","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils"],"prefix":"10.3390","volume":"15","author":[{"given":"Tom","family":"Broeg","sequence":"first","affiliation":[{"name":"Th\u00fcnen Institute of Farm Economics, Bundesallee 63, 38116 Braunschweig, Germany"},{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3042-6195","authenticated-orcid":false,"given":"Michael","family":"Blaschek","sequence":"additional","affiliation":[{"name":"State Authority for Geology, Resources and Mining, Albertstra\u00dfe 5, 79104 Freiburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4911-3906","authenticated-orcid":false,"given":"Steffen","family":"Seitz","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-6624","authenticated-orcid":false,"given":"Ruhollah","family":"Taghizadeh-Mehrjardi","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 Ressource Culture, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7178-0476","authenticated-orcid":false,"given":"Simone","family":"Zepp","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2602","authenticated-orcid":false,"given":"Thomas","family":"Scholten","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 Ressource Culture, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1002\/fes3.96","article-title":"Soil health and carbon management","volume":"5","author":"Lal","year":"2016","journal-title":"Food Energy Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1080\/713610854","article-title":"Global potential of soil carbon sequestration to mitigate the greenhouse effect","volume":"22","author":"Lal","year":"2003","journal-title":"Crit. 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