Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Representing soil landscapes from digital soil mapping products – helping the map to speak for itself
David G. Rossiter
CORRESPONDING AUTHOR
ISRIC-World Soil Information, Wageningen, the Netherlands
Section of Soil & Crop Sciences, College of Agriculture & Life Sciences, Cornell University, Ithaca, NY 14850, USA
Laura Poggio
ISRIC-World Soil Information, Wageningen, the Netherlands
Related authors
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
Short summary
Short summary
Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
Short summary
This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
Short summary
Short summary
Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
Short summary
This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
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Executive editor
The highlighted paper “Representing soil landscapes from digital soil mapping products – helping the map to speak for itself” demonstrates how operational digital soil map products can enhance decision-making. By shifting focus from traditional point-based accuracy metrics to the spatial realism and visual structure of digital soil maps, this research addresses a gap in soil mapping practice. The approach ensures that soil maps meet statistical criteria but also more faithfully represent soil landscape patterns, making them more valuable and informative for end users such as land planners and environmental managers.
The highlighted paper “Representing soil landscapes from digital soil mapping products –...
Short summary
Soil maps are useful for many applications, e.g., hydrology, agriculture, ecology, and civil engineering. The dominant mapping method is Digital Soil Mapping (DSM), which uses training observations and machine-learning to predict per-pixel. Accuracy is assessed by statistical evaluation at known points, but soils occur in spatial patterns. We present methods for helping the map to "speak for itself" to reveal patterns of the soil landscape.
Soil maps are useful for many applications, e.g., hydrology, agriculture, ecology, and civil...