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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2026
Research article |
| 08 Apr 2026
| 08 Apr 2026
Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data
Eya Cherif
CORRESPONDING AUTHOR
Institute for Earth system Science and Remote Sensing, Leipzig University, Leipzig, 04103, Germany
Center for scalable data analytics and artificial intelligence (ScaDS.AI), Leipzig University, 04105, Leipzig, Germany
Teja Kattenborn
German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, Leipzig, Germany
Sensor-based Geoinformatics (geosense), University of Freiburg, 79116, Freiburg, Germany
Luke A. Brown
School of Science, Engineering & Environment, University of Salford, Manchester, M5 4WT, UK
Department of Geography, King's College London, London, WC2R 2LS, UK
Michael Ewald
Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
Katja Berger
GFZ Helmholtz Centre for Geosciences, Potsdam, 14473, Germany
Phuong D. Dao
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA
Tobias B. Hank
Department of Geography, Faculty of Geosciences, Ludwig-Maximilians-Universität München (LMU), 80333, Munich, Germany
Etienne Laliberté
Département de Sciences Biologiques et Institut de Recherche en Biologie Végétale, Université de Montréal, Montréal, H1X 2B2, Canada
Bing Lu
Department of Geography, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Hannes Feilhauer
Institute for Earth system Science and Remote Sensing, Leipzig University, Leipzig, 04103, Germany
Center for scalable data analytics and artificial intelligence (ScaDS.AI), Leipzig University, 04105, Leipzig, Germany
German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, Leipzig, Germany
Helmholtz-Centre for Environmental Research (UFZ), 04318, Leipzig, Germany
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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.
Hyperspectral imagery combined with machine learning enables accurate large-scale mapping of...
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