28 citations as recorded by crossref.

1. High-resolution field-scale mapping of soil organic matter using multi-temporal Sentinel data and machine learning approach Y. Bouslihim et al.
2. Four Agricultural GHG Emission Mitigation Pathways in Morocco: Roadmaps from 2024 CCPI High-Performers A. Hajib et al.
3. Developing a Spatial Soil Database with Environmental Variables: Experience of the Republic of Bashkortostan A. Suleymanov
4. Mapping soil fertility properties in central Ethiopia at 100 m spatial resolution M. Redi et al.
5. Digital soil mapping in the era of big data and artificial Intelligence L. Yang & X. Li
6. Soil Science-Informed Machine Learning B. Minasny et al.
7. Importance ranking of data and model uncertainties in quantile regression forest-based spatial predictions when data are sparse, imprecise and clustered J. Rohmer
8. Combining proximal and remote sensors for regional soil characterization in rural Haiti A. Nayak et al.
9. Earth observation and machine-learning–based mapping of 0–1 m soil moisture at 10-cm intervals in a permafrost-affected basin Y. Xiao et al.
10. Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids L. Lilburne et al.
11. Mapping Soil Heavy Metal Using an Interpretable Framework With Multi‐Sources Data B. Hu et al.
12. A seventeen-year soil analysis dataset for ecosystem management M. Collinet et al.
13. Four-dimensional modelling reveals decline in cropland soil pH during last four decades in China’s Mollisols region J. Chen et al.
14. Modeling crop suitability for rewetting landscapes in the Netherlands across present and future climate scenarios R. Brouwer et al.
15. Representing soil landscapes from digital soil mapping products – helping the map to speak for itself D. Rossiter & L. Poggio
16. A China dataset of soil properties for land surface modelling (version 2, CSDLv2) G. Shi et al.
17. Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al.
18. 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 T. Hengl et al.
19. Mapping forest fine-grained soil particle size distributions: a holistic GeoAI approach via graph neural networks, LiDAR, and Sentinel-2 O. Abdi et al.
20. Gridded, temporally referenced spatial information on soil organic carbon for Hungary G. Szatmári et al.
21. A RothC-based spatiotemporal analysis of soil organic carbon stocks in agricultural soils of the Netherlands (1986–2022) Y. Lai et al.
22. Path to robust digital mapping of soil C:N ratio: geographic bias and uncertainty gap Y. Hong et al.
23. Using the phosphorus saturation degree as a guide for sustainable phosphorus management balancing crop production and water quality objectives M. van Doorn et al.
24. An Open Framework for Downscaling Soil Carbon and Clay Maps Using Sensor Data: Five Case Studies Across Diverse European Landscapes L. Gomes et al.
25. Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning X. Tian et al.
26. Mapping Swiss soil bulk density at 30 m Resolution: Insights from Machine Learning, environmental Covariates, and national data S. Gupta et al.
27. Three-dimensional mapping of key soil properties with multi-stage validation and big data A. Suleymanov et al.
28. Tracking Quarter-Century Spatio-Temporal Soil Salinization Dynamics in Semi-Arid Landscapes Using Earth Observation and Machine Learning A. Achemrk et al.