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  - IEEE
M1  - FZJ-2023-04569
SP  - 2993 - 2996
PY  - 2023
N1  - This work is funded by the Helmholtz Initiative and Networking Fund, Helmholtz AI, Deutsches Zentrum für Luft- und Raumfahrt and Forschungszentrum Jülich GmbH. The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA [1] at Forschungszentrum Jülich.Jülch Supercomputing Centre, “JURECA: Data Centric and Booster Modules implementing the Modular Supercomputing Architecture at Jülch Supercomputing Centre,” Journal of large-scale research facilities, vol. 7, no. A182, 2021. [Online]. Available: http://dx.doi.org/10.17815/jlsrf-7-182
AB  - The retrieval of sun-induced fluorescence (SIF) from hyper- spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method com- bining semi-supervised deep learning with an existing spec- tral fitting method. A validation study with in-situ SIF mea- surements 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  - International Geoscience and Remote Sensing Symposium
CY  - 21 Jun 2023 - 21 Jun 2023, Pasadena (USA)
Y2  - 21 Jun 2023 - 21 Jun 2023
M2  - Pasadena, USA
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:001098971603050
DO  - DOI:10.1109/IGARSS52108.2023.10282828
UR  - https://juser.fz-juelich.de/record/1018125
ER  -