Articles | Volume 18, issue 2
https://doi.org/10.5194/essd-18-989-2026
https://doi.org/10.5194/essd-18-989-2026
Data description paper
 | 
06 Feb 2026
Data description paper |  | 06 Feb 2026

OpenLandMap-soildb: global soil information at 30 m spatial resolution for 2000–2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations

Tomislav Hengl, Davide Consoli, Xuemeng Tian, Travis W. Nauman, Madlene Nussbaum, Mustafa Serkan Isik, Leandro Parente, Yu-Feng Ho, Rolf Simoes, Surya Gupta, Alessandro Samuel-Rosa, Taciara Zborowski Horst, José L. Safanelli, and Nancy Harris

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Cited articles

Aroca-Fernandez, J. M., Diez-Pastor, J. F., Latorre-Carmona, P., Elvira, V., Camps-Valls, G., Pascual, R., and Garcia-Osorio, C.: A Collaborative Platform for Soil Organic Carbon Inference Based on Spatiotemporal Remote Sensing Data, arXiv [preprint], https://doi.org/10.48550/ARXIV.2504.13962, 2025. a
Awood, T. B., Connolly, R. M., Almahasheer, H., Carnell, P. E., Duarte, C. M., Ewers Lewis, C. J., Irigoien, X., Kelleway, J. J., Lavery, P. S., Macreadie, P. I., Serrano, O., Sanders, C. J., Santos, I., Steven, A. D. L., and Lovelock, C. E.: Global patterns in mangrove soil carbon stocks and losses, Nature Climate Change, 7, 523–528, https://doi.org/10.1038/nclimate3326, 2017. a
Batjes, N. H., Calisto, L., and de Sousa, L. M.: Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023), Earth System Science Data, 16, 4735–4765, https://doi.org/10.5194/essd-16-4735-2024, 2024. a, b
Bauer-Marschallinger, B., Sabel, D., and Wagner, W.: Optimisation of global grids for high-resolution remote sensing data, Computers & Geosciences, 72, 84–93, https://doi.org/10.1016/j.cageo.2014.07.005, 2014. a, b
Behrens, T., Schmidt, K., MacMillan, R. A., and Rossel, R. V.: Multiscale contextual spatial modelling with the Gaussian scale space, Geoderma, 310, 128–137, https://doi.org/10.1016/j.geoderma.2017.09.015, 2018. a
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Short summary
We used satellite data and thousands of soil samples to create detailed global maps showing how soil changes over time. These maps reveal important patterns in soil health, such as a significant global loss of soil carbon in the past 25 years. Our results help track land degradation and support better land restoration efforts. This work provides a new global tool for understanding and protecting soil, a key resource for food, water, and climate.
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