Articles | Volume 23, issue 15
https://doi.org/10.5194/bg-23-5607-2026
© 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-23-5607-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
14 Aug 2026
Research article | Highlight paper |
| 14 Aug 2026
| 14 Aug 2026
AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos
Luis Kremer
CORRESPONDING AUTHOR
Chair of Sensor-based Geoinformatics (geosense), University of Freiburg, Freiburg, Germany
Jan Pisek
Tartu Observatory, University of Tartu, Tõravere, Estonia
Ronny Richter
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany
Systematic Botany and Functional Biodiversity, Institute of Biology, Leipzig University, Leipzig, Germany
Julian Frey
Chair of Forest Growth and Dendroecology, University of Freiburg, Freiburg, Germany
Daniel Lusk
Chair of Sensor-based Geoinformatics (geosense), University of Freiburg, Freiburg, Germany
Christiane Werner
Chair of Ecosystem Physiology, University of Freiburg, Freiburg, Germany
Christian Wirth
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany
Systematic Botany and Functional Biodiversity, Institute of Biology, Leipzig University, Leipzig, Germany
Teja Kattenborn
Chair of Sensor-based Geoinformatics (geosense), University of Freiburg, Freiburg, Germany
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Editorial statement
Leaf inclination angle distribution is a key parameter for radiative transfer modeling and for proximal and remote sensing applications, and is an often-overlooked response of plants to stress in the short term (diurnal dynamics). Traditionally, measuring leaf inclination angles is labor-intensive. AngleCamV2 provides a continuous, low-cost system to assess leaf angle distributions that can be used to better constrain the retrieval of plant traits from radiative transfer model inversions, interpret signals such as sun-induced fluorescence that is highly dependent on the canopy architecture, and understand leaf inclination dynamics under water stress.
Leaf inclination angle distribution is a key parameter for radiative transfer modeling and for...
Short summary
To adapt to changing environmental conditions, plants can adjust their leaf angles. We developed AngleCam V2, an AI method that estimates leaf inclination angles from photos taken during day and night. Trained on thousands of images from about 200 species, it monitors daily changes in leaf angle, aligns with laser-scanning data, and detects systematic shifts under water limitation. AngleCam V2 provides an open-source tool for monitoring leaf angle dynamics over time, taxa, and environments.
To adapt to changing environmental conditions, plants can adjust their leaf angles. We developed...
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