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Uncertainty in the spatial prediction of soil texture
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Citations
Cited by
Machine learning for digital soil mapping: Applications, challenges and suggested solutions
Wadoux, A. M. J.-C., Minasny, B., & McBratney, A. B. (2020) · Earth-Science Reviews · doi:10.1016/j.earscirev.2020.103359 · Cited by 731
Significant increase in natural disturbance impacts on European forests since 1950
Patacca, M., Lindner, M., Lucas‐Borja, M. E., Cordonnier, T., Fidej, G., Gardiner, B., Hauf, Y., Jasinevičius, G., Labonne, S., Linkevičius, E., et al. (2023) · Global Change Biology · doi:10.1111/gcb.16531 · Cited by 523
Selecting appropriate machine learning methods for digital soil mapping
Khaledian, Y., & Miller, B. A. (2020) · Applied Mathematical Modelling · doi:10.1016/j.apm.2019.12.016 · Cited by 448
Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. (2016) · Geomorphology · doi:10.1016/j.geomorph.2016.02.012 · Cited by 440
Evaluation of digital soil mapping approaches with large sets of environmental covariates
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., & Papritz, A. (2018) · SOIL · doi:10.5194/soil-4-1-2018 · Cited by 304
Metadata
- Authors
- Ließ, M., Glaser, B., & Huwe, B.
- Venue
- Geoderma
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 170
- Pages
- 70-79
- ISSN
- 0016-7061 (ISSN-L)
- OpenAlex
- W18678914
- Cited by
- 300
- References
- 0
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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How to cite
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- CoChat. (2026). Citation check: Uncertainty in the spatial prediction of soil texture. https://app.cochat.ai/papers/10.1016/j.geoderma.2011.10.010
- The paper
- Ließ, M., Glaser, B., & Huwe, B. (2012). Uncertainty in the spatial prediction of soil texture. Geoderma. https://doi.org/10.1016/j.geoderma.2011.10.010
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
