Including soil spatial neighbor information for digital soil mapping Article Swipe
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.geoderma.2024.117072
Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.geoderma.2024.117072
- OA Status
- gold
- Cited By
- 2
- References
- 19
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4403779710Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.geoderma.2024.117072Digital Object Identifier
- Title
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Including soil spatial neighbor information for digital soil mappingWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
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2024-10-25Full publication date if available
- Authors
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Zhongxing Chen, Zheng Wang, Xi Wang, Zhou Shi, Songchao ChenList of authors in order
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https://doi.org/10.1016/j.geoderma.2024.117072Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.geoderma.2024.117072Direct OA link when available
- Concepts
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Digital soil mapping, Soil map, Soil survey, Spatial analysis, Soil science, Environmental science, Geology, Computer science, Remote sensing, Soil waterTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.challenges. | 22 |
| abstract_inverted_index.coordinates | 128 |
| abstract_inverted_index.disciplines | 176 |
| abstract_inverted_index.exploration | 181 |
| abstract_inverted_index.information | 17, 51 |
| abstract_inverted_index.integration | 24 |
| abstract_inverted_index.neighboring | 37, 157 |
| abstract_inverted_index.open-access | 67 |
| abstract_inverted_index.overlooked. | 44 |
| abstract_inverted_index.underscores | 144 |
| abstract_inverted_index.information. | 158 |
| abstract_inverted_index.transforming | 5 |
| abstract_inverted_index.applicability | 173 |
| abstract_inverted_index.environmental | 21, 26 |
| abstract_inverted_index.incorporating | 78 |
| abstract_inverted_index.decision-making | 166 |
| abstract_inverted_index.high-resolution | 14 |
| abstract_inverted_index.spatial–temporal | 15 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.65563789 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |