Chair of Sensor-based Geoinformatics (geosense), University of Freiburg, Freiburg, Germany
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Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 1,514 (including HTML, PDF, and XML)
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EndNote
1,490
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24
1,514
0
0
HTML: 1,490
PDF: 0
XML: 24
Total: 1,514
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 18 Nov 2025)
Cumulative views and downloads
(calculated since 18 Nov 2025)
Total article views: 1,514 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,490
0
24
1,514
0
0
HTML: 1,490
PDF: 0
XML: 24
Total: 1,514
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 18 Nov 2025)
Cumulative views and downloads
(calculated since 18 Nov 2025)
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 1,514 (including HTML, PDF, and XML)
Thereof 1,487 with geography defined
and 27 with unknown origin.
Total article views: 1,514 (including HTML, PDF, and XML)
Thereof 1,487 with geography defined
and 27 with unknown origin.
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...
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...