Poster (After Call)

FZJ-2024-06602

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep Learning Based Prediction of Sun-Induced Fluorescence from HyPlant Imagery

Buffat, J. (Corresponding author)FZJ* ; Pato, M. ; Alonso, K. ; Auer, S. ; Carmona, E. ; Maier, S. ; Müller, R. ; Rademske, P.FZJ* ; Rascher, U.FZJ* ; Scharr, H.FZJ*

2024

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Please use a persistent id in citations: doi:10.34734/FZJ-2024-06602

Abstract: The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements 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.

Note: Poster presented as part of the Nectar Track

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)
2. Pflanzenwissenschaften (IBG-2)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) (POF4-217)

Appears in the scientific report 2024

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Database coverage:

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