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| 001 | 1033637 | ||
| 005 | 20241214210606.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2411.08925 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06509 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06509 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Buffat, Jim |0 P:(DE-Juel1)188104 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption band from DESIS imagery |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1734166697_310 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a We 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 O$_2$-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 self-supervised 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 / sr / m$^2$. |
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| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Computer Vision and Pattern Recognition (cs.CV) |2 Other |
| 650 | _ | 7 | |a Artificial Intelligence (cs.AI) |2 Other |
| 650 | _ | 7 | |a Geophysics (physics.geo-ph) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 650 | _ | 7 | |a FOS: Physical sciences |2 Other |
| 700 | 1 | _ | |a Pato, Miguel |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Alonso, Kevin |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Auer, Stefan |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Carmona, Emiliano |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Maier, Stefan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Müller, Rupert |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Rademske, Patrick |0 P:(DE-Juel1)162306 |b 7 |u fzj |
| 700 | 1 | _ | |a Rascher, Uwe |0 P:(DE-Juel1)129388 |b 8 |u fzj |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 9 |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2411.08925 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1033637/files/main_document.pdf |y OpenAccess |
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