%0 Conference Paper
%A Buffat, Jim
%A Pato, Miguel
%A Alonso, Kevin
%A Auer, Stefan
%A Carmona, Emiliano
%A Maier, Stefan
%A Müller, Rupert
%A Rademske, Patrick
%A Rascher, Uwe
%A Scharr, Hanno
%T Deep Learning Based Prediction of Sun-Induced Fluorescence from HyPlant Imagery
%I University of Bonn
%M FZJ-2024-06602
%D 2024
%Z Poster presented as part of the Nectar Track
%X 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.
%B German Conference on Pattern Recognition 2024
%C 10 Sep 2024 - 13 Sep 2024, Munich (Germany)
Y2 10 Sep 2024 - 13 Sep 2024
M2 Munich, Germany
%F PUB:(DE-HGF)24
%9 Poster
%R 10.34734/FZJ-2024-06602
%U https://juser.fz-juelich.de/record/1033761