Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-559-2022
https://doi.org/10.5194/soil-8-559-2022
Original research article
 | 
05 Sep 2022
Original research article |  | 05 Sep 2022

How well does digital soil mapping represent soil geography? An investigation from the USA

David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova

Related authors

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
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

Related subject area

Pedometrics
Accuracy of regional-to-global soil maps for on-farm decision-making: are soil maps “good enough”?
Jonathan J. Maynard, Edward Yeboah, Stephen Owusu, Michaela Buenemann, Jason C. Neff, and Jeffrey E. Herrick
SOIL, 9, 277–300, https://doi.org/10.5194/soil-9-277-2023,https://doi.org/10.5194/soil-9-277-2023, 2023
Short summary
Shapley values reveal the drivers of soil organic carbon stock prediction
Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin
SOIL, 9, 21–38, https://doi.org/10.5194/soil-9-21-2023,https://doi.org/10.5194/soil-9-21-2023, 2023
Short summary

Cited articles

Araujo-Carrillo, G. A., Varón-Ramírez, V. M., Jaramillo-Barrios, C. I., Estupiñan-Casallas, J. M., Silva-Arero, E. A., Gómez-Latorre, D. A., and Martínez-Maldonado, F. E.: IRAKA: The First Colombian Soil Information System with Digital Soil Mapping Products, CATENA, 196, 104940, https://doi.org/10.1016/j.catena.2020.104940, 2021. a
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J., d. L. Mendonca-Santos, M., Minasny, B., Montanarella, L., Odeh, I. O., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap: Towards a Fine-Resolution Global Grid of Soil Properties, Adv. Agron., 125, 93–134, 2014. a, b, c
Arrouays, D., McBratney, A., Bouma, J., Libohova, Z., Richer-de-Forges, A. C., Morgan, C. L., Roudier, P., Poggio, L., and Mulder, V. L.: Impressions of Digital Soil Maps: The Good, the Not so Good, and Making Them Ever Better, Geoderma Reg., 20, e00255, https://doi.org/10.1016/j.geodrs.2020.e00255, 2020. a, b, c
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised Soil Profile Data to Support Global Mapping and Modelling (WoSIS Snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020. a
Beaudette, D.: ncss-tech/compare-psm: PSM Comparison Code v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.5512626, 2021. a
Download
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.