%0 Conference Paper
%A Pato, Miguel
%A Alonso, Kevin
%A Auer, Stefan
%A Buffat, Jim
%A Carmona, Emiliano
%A Maier, Stefan
%A Müller, Rupert
%A Rademske, Patrick
%A Rascher, Uwe
%A Scharr, Hanno
%T Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant
%I IEEE
%M FZJ-2024-01185
%P 7563-7566
%D 2023
%X 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
%B IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
%C 16 Jul 2023 - 21 Jul 2023, Pasadena (CA)
Y2 16 Jul 2023 - 21 Jul 2023
M2 Pasadena, CA
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U <Go to ISI:>//WOS:001098971607156
%R 10.1109/IGARSS52108.2023.10281579
%U https://juser.fz-juelich.de/record/1022048