Contribution to a conference proceedings

FZJ-2024-01185

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant

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

2023
IEEE

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Please use a persistent id in citations: doi:10.1109/IGARSS52108.2023.10281579

Abstract: In many remote sensing applications the measured radi-ance needs to be corrected for atmospheric effects to studysurface properties such as reflectance, temperature or emis-sion features. The correction often applies radiative transferto simulate atmospheric propagation, a time-consuming stepusually done offline. In principle, an efficient machine learn-ing (ML) model can accelerate the simulation step. This is thegoal pursued here in the context of solar-induced fluorescence(SIF) emitted by vegetation around the O2-A band using thespaceborne DESIS and airborne HyPlant spectrometers. Wepresent an ML simulator of at-sensor radiances trained onsynthetic spectra and describe its performance in detail. Thesimulator is fast and accurate, constituting a promising alter-native to a full-fledged, lengthy radiative transfer code for SIFretrieval in the O2-A band with DESIS and HyPlant.Index Terms— solar-induced fluorescence, hyperspectralsensors, radiative transfer, machine learning

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2023

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