Mapping Plant Functional Traits with Convolutional Neural Networks Trained on Spectral Images of Discrete Anisotropic Radiative Transfer Model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00645151" target="_blank" >RIV/86652079:_____/25:00645151 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/xpl/conhome/11242230/proceeding" target="_blank" >https://ieeexplore.ieee.org/xpl/conhome/11242230/proceeding</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IGARSS55030.2025.11242993" target="_blank" >10.1109/IGARSS55030.2025.11242993</a>

Result language
angličtina
Original language name
Mapping Plant Functional Traits with Convolutional Neural Networks Trained on Spectral Images of Discrete Anisotropic Radiative Transfer Model
Original language description
In this study, we demonstrated how spectral images of vegetation canopies generated by physically-based radiative transfer modeling can be used for machine learning of convolutional neural networks (CNN) capable of mapping plant biochemical functional traits. Results of ResNet50 CNN, applied on multispectral reflectance images of maize and sugar beet crop canopies simulated with the Discrete Anisotropic Radiative Transfer (DART) model, indicated acceptable accuracies of leaf chlorophyll a+b content estimates for early growth stages of maize and sugar beet crops (R2 = 0.83, RMSE = 6.9 and 6.6 μg.cm-2). Estimations for the later and across different growth stages of both crops were, however, inaccurate. U-Net CNN, trained on DART reflectance images of a tall eucalyptus forest and applied on a drone-based hyperspectral image, retrieved content of leaf chlorophylls comparably to a random forest, but estimates of carotenoids and anthocyanin were underestimated.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
IEEE International Symposium on Geoscience and Remote Sensing (IGARSS 2025)
ISBN
979-8-3315-0810-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
355-359
Publisher name
IEEE
Place of publication
Brisbane
Event location
Brisbane
Event date
Aug 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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