Biogeosciences
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Articles | Volume 23, issue 7
Articles | Volume 23, issue 7
https://doi.org/10.5194/bg-23-2235-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-2235-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 7
Research article
 | 
08 Apr 2026
Research article |  | 08 Apr 2026

Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data

Eya Cherif, Teja Kattenborn, Luke A. Brown, Michael Ewald, Katja Berger, Phuong D. Dao, Tobias B. Hank, Etienne Laliberté, Bing Lu, and Hannes Feilhauer

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Latest update: 20 Jul 2026
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Short summary
Hyperspectral imagery combined with machine learning enables accurate large-scale mapping of plant traits but struggles with uncertainty when facing unfamiliar environmental conditions. This study introduces a distance-based method that measures dissimilarities between new and training data to reliably quantify uncertainty. Results show it effectively identifies uncertain predictions, greatly improving the reliability of global vegetation monitoring compared to traditional methods.
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