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001018125 0247_ $$2doi$$a10.1109/IGARSS52108.2023.10282828
001018125 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-04569
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001018125 1001_ $$0P:(DE-Juel1)188104$$aBuffat, Jim$$b0$$eCorresponding author
001018125 1112_ $$aInternational Geoscience and Remote Sensing Symposium$$cPasadena$$d2023-06-21 - 2023-06-21$$wUSA
001018125 245__ $$aDEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY
001018125 260__ $$bIEEE$$c2023
001018125 300__ $$a2993 - 2996
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001018125 500__ $$aThis work is funded by the Helmholtz Initiative and Networking Fund, Helmholtz AI, Deutsches Zentrum für Luft- und Raumfahrt and Forschungszentrum Jülich GmbH. The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA [1] at Forschungszentrum Jülich.Jülch Supercomputing Centre, “JURECA: Data Centric and Booster Modules implementing the Modular Supercomputing Architecture at Jülch Supercomputing Centre,” Journal of large-scale research facilities, vol. 7, no. A182, 2021. [Online]. Available: http://dx.doi.org/10.17815/jlsrf-7-182
001018125 520__ $$aThe 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.
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001018125 7001_ $$0P:(DE-HGF)0$$aPato, Miguel$$b1
001018125 7001_ $$0P:(DE-HGF)0$$aAlonso, Kevin$$b2
001018125 7001_ $$0P:(DE-HGF)0$$aAuer, Stefan$$b3
001018125 7001_ $$0P:(DE-HGF)0$$aCarmona, Emiliano$$b4
001018125 7001_ $$0P:(DE-HGF)0$$aMaier, Stefan$$b5
001018125 7001_ $$0P:(DE-HGF)0$$aMüller, Rupert$$b6
001018125 7001_ $$0P:(DE-Juel1)162306$$aRademske, Patrick$$b7
001018125 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b8
001018125 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b9
001018125 773__ $$a10.1109/IGARSS52108.2023.10282828$$y2023
001018125 8564_ $$uhttps://ieeexplore.ieee.org/document/10282828
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