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Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-553-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-553-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
Original research article
 | 
22 Jul 2025
Original research article |  | 22 Jul 2025

Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models

Yin-Chung Huang, José Padarian, Budiman Minasny, and Alex B. McBratney

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Short summary
Uncertainty quantification plays a crucial role in reporting machine learning models in soil spectroscopy. This study introduces Monte Carlo conformal prediction (MC-CP), a novel method for uncertainty quantification in deep-learning soil spectral models. MC-CP outperformed two established methods, providing the most reliable results. Its efficiency and robustness make it a practical choice for implementing soil spectral models in decision making.
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