001048173 001__ 1048173
001048173 005__ 20251118202144.0
001048173 020__ $$a978-3-031-91835-3
001048173 020__ $$a978-3-031-91835-3 (electronic)
001048173 0247_ $$2doi$$a10.1007/978-3-031-91835-3_6
001048173 0247_ $$2ISSN$$a0302-9743
001048173 0247_ $$2ISSN$$a1611-3349
001048173 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04535
001048173 037__ $$aFZJ-2025-04535
001048173 1001_ $$0P:(DE-Juel1)188104$$aBuffat, Jim$$b0$$eCorresponding author
001048173 1112_ $$aECCV 2024 Workshops$$cMilano$$d2024-09-29 - 2024-09-30$$gECCV$$wItaly
001048173 245__ $$aRetrieval of Sun-Induced Plant Fluorescence in the O$_2$-A Absorption Band from DESIS Imagery
001048173 260__ $$aCham$$bSpringer Nature Switzerland$$c2025
001048173 29510 $$aComputer Vision – ECCV 2024 Workshops / Del Bue, Alessio (Editor) ; Cham : Springer Nature Switzerland, 2025, Chapter 6 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-031-91834-6=978-3-031-91835-3 ; doi:10.1007/978-3-031-91835-3
001048173 300__ $$a81 - 100
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001048173 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1763449920_31156
001048173 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
001048173 4900_ $$aLecture Notes in Computer Science$$v15625
001048173 520__ $$aWe provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation (r$^2$ = 0.6) to high-quality airborne estimates of sun-induced fluorescence (SIF)  ̇SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA’s upcoming FLEX mission we develop a method for SIF retrieval in the O2-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based selfsupervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW nm$^{−1}$ sr$^{−1}$ m$^{−2}$
001048173 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001048173 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
001048173 588__ $$aDataset connected to CrossRef Book Series, Journals: juser.fz-juelich.de
001048173 7001_ $$00000-0003-0111-0861$$aPato, Miguel$$b1
001048173 7001_ $$00000-0003-2469-8290$$aAlonso, Kevin$$b2
001048173 7001_ $$00000-0001-9310-2337$$aAuer, Stefan$$b3
001048173 7001_ $$00009-0008-8998-7310$$aCarmona, Emiliano$$b4
001048173 7001_ $$0P:(DE-Juel1)188300$$aMaier, Stefan$$b5$$ufzj
001048173 7001_ $$00000-0002-3288-5814$$aMüller, Rupert$$b6
001048173 7001_ $$0P:(DE-Juel1)162306$$aRademske, Patrick$$b7$$ufzj
001048173 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b8
001048173 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b9
001048173 773__ $$a10.1007/978-3-031-91835-3_6
001048173 8564_ $$uhttps://juser.fz-juelich.de/record/1048173/files/Buffat%20et%20al.%20-%202025%20-%20Retrieval%20of%C2%A0Sun-Induced%20Plant%20Fluorescence%20in%C2%A0the%C2%A0O%24%24_2%24%24-A%20Absorption%20Band%20from%C2%A0DESIS%20Imagery.pdf$$yOpenAccess
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001048173 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188300$$aForschungszentrum Jülich$$b5$$kFZJ
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001048173 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129388$$aForschungszentrum Jülich$$b8$$kFZJ
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001048173 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x1
001048173 9141_ $$y2025
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001048173 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x0
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