TY  - CONF
AU  - Pato, Miguel
AU  - Alonso, Kevin
AU  - Auer, Stefan
AU  - Buffat, Jim
AU  - Carmona, Emiliano
AU  - Maier, Stefan
AU  - Müller, Rupert
AU  - Rademske, Patrick
AU  - Rascher, Uwe
AU  - Scharr, Hanno
TI  - Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant
PB  - IEEE
M1  - FZJ-2024-01185
SP  - 7563-7566
PY  - 2023
AB  - 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
T2  - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
CY  - 16 Jul 2023 - 21 Jul 2023, Pasadena (CA)
Y2  - 16 Jul 2023 - 21 Jul 2023
M2  - Pasadena, CA
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:001098971607156
DO  - DOI:10.1109/IGARSS52108.2023.10281579
UR  - https://juser.fz-juelich.de/record/1022048
ER  -