14 citations as recorded by crossref.

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3. Overview of the Mineralosphere and research progress on its environmental effects M. Feng et al. 10.1007/s11430-024-1520-1
4. Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density T. Ekundayo et al. 10.1016/j.envpol.2022.120734
5. Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: A Meta-Analysis A. Chinilin et al. 10.1134/S1064229323601841
6. Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: Meta-Analysis A. Chinilin et al. 10.31857/S0032180X23600695
7. Soil microbial community construction under revegetation in newly created land S. Feng et al. 10.1016/j.scitotenv.2024.176496
8. Characterization of clay and nanoclay extracted from a semi-arid Vertisol and investigation of their carbon sequestration potential N. Sadri et al. 10.1007/s10661-023-12246-x
9. Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation Z. Shen et al. 10.5194/soil-8-467-2022
10. A progressive knowledge strategy for monitoring soil microbiological activity by microscale spectroscopic detection H. Rodríguez-Albarracín et al. 10.1016/j.soilad.2025.100058
11. Deep transfer learning of global spectra for local soil carbon monitoring Z. Shen et al. 10.1016/j.isprsjprs.2022.04.009
12. An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. 10.1016/j.earscirev.2024.104797
13. A curated soil fungal dataset to advance fungal ecology and conservation research in Australia and Antarctica L. Florence et al. 10.1038/s41597-025-04598-5
14. Improving the accuracy of NIR detection of moldy core in apples using different diameter correction methods H. Li et al. 10.1016/j.postharvbio.2024.113279