SOIL
SOIL
SOIL
Articles | Volume 12, issue 1
Articles | Volume 12, issue 1
https://doi.org/10.5194/soil-12-619-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-12-619-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 1
Original research article
 | 
13 May 2026
Original research article |  | 13 May 2026

Estimating soil carbon sequestration potential with mid-IR spectroscopy and explainable machine learning

Yang Hu and Raphael A. Viscarra Rossel

Viewed

Total article views: 5,681 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,113 2,286 282 5,681 24 219 198
  • HTML: 3,113
  • PDF: 2,286
  • XML: 282
  • Total: 5,681
  • Supplement: 24
  • BibTeX: 219
  • EndNote: 198
Views and downloads (calculated since 14 Oct 2025)
Cumulative views and downloads (calculated since 14 Oct 2025)

Viewed (geographical distribution)

Total article views: 5,681 (including HTML, PDF, and XML) Thereof 5,635 with geography defined and 46 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Jun 2026
Download
Short summary
We analysed 482 Australian topsoils to estimate mineral-associated organic carbon (MAOC) and the carbon storage deficit (Cdef). Using mid-infrared spectra with explainable machine learning, we predicted MAOC (R2=0.86) and Cdef (R2=0.89). Model interpretation revealed signals from organic matter and clay minerals were most significant in predicting MAOC and Cdef. Our work provides an accurate, cost-effective means to assess and better understand the drivers of soil carbon sequestration potential.
Read more
Share