AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos
5 affiliations
- 1Chair of Sensor-based Geoinformatics (geosense), University of Freiburg, Freiburg, Germany
- 2Tartu Observatory, University of Tartu, Tõravere, Estonia
- 3German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany; Systematic Botany and Functional Biodiversity, Institute of Biology, Leipzig University, Leipzig, Germany
- 4Chair of Forest Growth and Dendroecology, University of Freiburg, Freiburg, Germany
- 5Chair of Ecosystem Physiology, University of Freiburg, Freiburg, Germany
Abstract
Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behavior that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4500 images across 200 globally distributed species to facilitate generalization across taxa. To address the lack of labeled NIR data, a training strategy was developed that uses pseudo-NIR imagery derived from daytime RGB images. The model is based on a vision transformer architecture with mixed-modality training incorporating RGB, grayscale, and pseudo-NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R2=0.62 vs. 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics from multitemporal terrestrial laser scanning confirmed the model's ability to track diurnal leaf movements (R2=0.61–0.75) with a nighttime RMSE of 3.1–3.6° (compared to daytime RMSEs of 3.5 and 7.6°, respectively). Furthermore, the model successfully detected water limitation-induced changes over a 14-d monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
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BibTeX
@article{kremer2026anglecam,
title = {{AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos}},
author = {Luis Kremer and Jan Pisek and Ronny Richter and Julian Frey and Daniel Lusk and Christiane Werner and Christian Wirth and Teja Kattenborn},
journal = {Biogeosciences},
year = {2026},
month = aug,
doi = {10.5194/bg-23-5607-2026},
url = {https://doi.org/10.5194/bg-23-5607-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos
AU - Luis Kremer
AU - Jan Pisek
AU - Ronny Richter
AU - Julian Frey
AU - Daniel Lusk
AU - Christiane Werner
AU - Christian Wirth
AU - Teja Kattenborn
PY - 2026
DA - 2026/08/01
JO - Biogeosciences
DO - 10.5194/bg-23-5607-2026
UR - https://doi.org/10.5194/bg-23-5607-2026
AB - Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behavior that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4500 images across 200 globally distributed species to facilitate generalization across taxa. To address the lack of labeled NIR data, a training strategy was developed that uses pseudo-NIR imagery derived from daytime RGB images. The model is based on a vision transformer architecture with mixed-modality training incorporating RGB, grayscale, and pseudo-NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R2=0.62 vs. 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics from multitemporal terrestrial laser scanning confirmed the model's ability to track diurnal leaf movements (R2=0.61–0.75) with a nighttime RMSE of 3.1–3.6° (compared to daytime RMSEs of 3.5 and 7.6°, respectively). Furthermore, the model successfully detected water limitation-induced changes over a 14-d monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
SN - 1726-4170
SN - 1726-4189
ER -