This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2023-04569 in citations.
Please use the identifier: http://dx.doi.org/10.1109/IGARSS52108.2023.10282828 in citations.
DEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY
DEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY
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...
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Personal Name(s): | Buffat, Jim (Corresponding author) |
---|---|
Pato, Miguel / Alonso, Kevin / Auer, Stefan / Carmona, Emiliano / Maier, Stefan / Müller, Rupert / Rademske, Patrick / Rascher, Uwe / Scharr, Hanno | |
Contributing Institute: |
Datenanalyse und Maschinenlernen; IAS-8 Pflanzenwissenschaften; IBG-2 |
Published in: | 2023 |
Imprint: |
IEEE
2023
|
Physical Description: |
2993 - 2996 |
DOI: |
10.34734/FZJ-2023-04569 |
DOI: |
10.1109/IGARSS52108.2023.10282828 |
Conference: | International Geoscience and Remote Sensing Symposium, Pasadena (USA), 2023-06-21 - 2023-06-21 |
Document Type: |
Contribution to a conference proceedings |
Research Program: |
Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups Agro-biogeosystems: controls, feedbacks and impact |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1109/IGARSS52108.2023.10282828 in citations.
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. |