Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4719-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4719-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Colombian soil texture: building a spatial ensemble model
Viviana Marcela Varón-Ramírez
CORRESPONDING AUTHOR
Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, Mosquera-Cundinamarca, Colombia
Gustavo Alfonso Araujo-Carrillo
Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, Mosquera-Cundinamarca, Colombia
Mario Antonio Guevara Santamaría
Centro de Geociencias – Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, México
University of California, Riverside, Department of Environmental Sciences, Riverside, CA 92507, USA
United States Department of Agriculture, U.S. Soil Salinity National Laboratory, Riverside, CA 92507, USA
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Cited
15 citations as recorded by crossref.
- Efectos de las actividades socioeconómicas sobre las propiedades fisicoquímicas del suelo, El Motilón-Laguna de la Cocha J. Huertas et al. https://doi.org/10.47864/se552025p99116
- Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability S. Díaz-Guadarrama et al. https://doi.org/10.5194/essd-16-1229-2024
- Índice de calidad nutricional en suelos cacaoteros de la subregión cordillera de Nariño D. Cortés Delgadillo et al. https://doi.org/10.29166/siembra.v11i1.6027
- Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape M. Ließ & A. Sakhaee https://doi.org/10.3390/agriculture14081230
- The Use of Agricultural Databases for Crop Modeling: A Scoping Review T. Mthembu et al. https://doi.org/10.3390/su16156554
- SpatialFormer: A Model to Estimate Soil Organic Carbon Content Using Spectral and Spatial Information Z. Tong & L. Liu https://doi.org/10.1007/s42729-025-02333-y
- Effects of forest management practices on carbon dynamics of China's boreal forests under changing climates C. Huang et al. https://doi.org/10.1016/j.jenvman.2023.117497
- Optimizing soil texture spatial prediction in the Brazilian Cerrado: Insights from random forest and spectral data M. Pelegrino et al. https://doi.org/10.1016/j.geodrs.2025.e00922
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. https://doi.org/10.5194/essd-16-2941-2024
- National soil information systems (NSIS) in Africa: Expert perceptions of status, challenges, and stakeholder collaboration G. Kome et al. https://doi.org/10.1016/j.soilad.2026.100105
- Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms F. Kaya et al. https://doi.org/10.1016/j.geodrs.2022.e00584
- Time Lag and Cumulative Effects of Extreme Climate on Coastal Vegetation in China T. Dong et al. https://doi.org/10.3390/rs16030528
- RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization M. Dulce Rubio et al. https://doi.org/10.1145/3648437
- Soil information and soil property maps for the Kurdistan region, Dohuk governorate (Iraq) M. Bellat et al. https://doi.org/10.5194/essd-18-2507-2026
- Mining soil data of Switzerland: New maps for soil texture, soil organic carbon, nitrogen, and phosphorus S. Gupta et al. https://doi.org/10.1016/j.geodrs.2023.e00747
15 citations as recorded by crossref.
- Efectos de las actividades socioeconómicas sobre las propiedades fisicoquímicas del suelo, El Motilón-Laguna de la Cocha J. Huertas et al. https://doi.org/10.47864/se552025p99116
- Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability S. Díaz-Guadarrama et al. https://doi.org/10.5194/essd-16-1229-2024
- Índice de calidad nutricional en suelos cacaoteros de la subregión cordillera de Nariño D. Cortés Delgadillo et al. https://doi.org/10.29166/siembra.v11i1.6027
- Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape M. Ließ & A. Sakhaee https://doi.org/10.3390/agriculture14081230
- The Use of Agricultural Databases for Crop Modeling: A Scoping Review T. Mthembu et al. https://doi.org/10.3390/su16156554
- SpatialFormer: A Model to Estimate Soil Organic Carbon Content Using Spectral and Spatial Information Z. Tong & L. Liu https://doi.org/10.1007/s42729-025-02333-y
- Effects of forest management practices on carbon dynamics of China's boreal forests under changing climates C. Huang et al. https://doi.org/10.1016/j.jenvman.2023.117497
- Optimizing soil texture spatial prediction in the Brazilian Cerrado: Insights from random forest and spectral data M. Pelegrino et al. https://doi.org/10.1016/j.geodrs.2025.e00922
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. https://doi.org/10.5194/essd-16-2941-2024
- National soil information systems (NSIS) in Africa: Expert perceptions of status, challenges, and stakeholder collaboration G. Kome et al. https://doi.org/10.1016/j.soilad.2026.100105
- Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms F. Kaya et al. https://doi.org/10.1016/j.geodrs.2022.e00584
- Time Lag and Cumulative Effects of Extreme Climate on Coastal Vegetation in China T. Dong et al. https://doi.org/10.3390/rs16030528
- RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization M. Dulce Rubio et al. https://doi.org/10.1145/3648437
- Soil information and soil property maps for the Kurdistan region, Dohuk governorate (Iraq) M. Bellat et al. https://doi.org/10.5194/essd-18-2507-2026
- Mining soil data of Switzerland: New maps for soil texture, soil organic carbon, nitrogen, and phosphorus S. Gupta et al. https://doi.org/10.1016/j.geodrs.2023.e00747
Saved (final revised paper)
Latest update: 02 Nov 2026
Short summary
These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
These are the first national soil texture maps obtained via digital soil mapping. We built clay,...
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