TY  - CONF
AU  - Buffat, Jim
AU  - Pato, Miguel
AU  - Alonso, Kevin
AU  - Auer, Stefan
AU  - Carmona, Emiliano
AU  - Maier, Stefan
AU  - Müller, Rupert
AU  - Rademske, Patrick
AU  - Rascher, Uwe
AU  - Scharr, Hanno
TI  - Deep Learning Based Prediction of Sun-Induced Fluorescence from HyPlant Imagery
PB  - University of Bonn
M1  - FZJ-2024-06602
PY  - 2024
N1  - Poster presented as part of the Nectar Track
AB  - The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.
T2  - German Conference on Pattern Recognition 2024
CY  - 10 Sep 2024 - 13 Sep 2024, Munich (Germany)
Y2  - 10 Sep 2024 - 13 Sep 2024
M2  - Munich, Germany
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2024-06602
UR  - https://juser.fz-juelich.de/record/1033761
ER  -