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| 005 | 20220103172039.0 | ||
| 024 | 7 | _ | |a 10.3390/rs13214368 |2 doi |
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| 100 | 1 | _ | |a Morata, Miguel |0 0000-0002-0537-6803 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer |
| 260 | _ | _ | |a Basel |c 2021 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grewto maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yetfull-spectrum estimation methods such as the spectral fitting method (SFM) are computationallyexpensive. To bypass this computational load, this work aims to approximate the SFM-based SIFretrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogatemodels of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulationconcept is applied to experimental data. We evaluated the possibility of approximating the SFM-likeSIF output directly based on radiance data while minimizing the loss in precision as opposed to SFMbasedSIF. To do so, we implemented a double principal component analysis (PCA) dimensionalityreduction, i.e., in both input and output, to achieve emulation of multispectral SIF output basedon hyperspectral radiance data. We then evaluated systematically: (1) multiple machine learningregression algorithms, (2) number of principal components, (3) number of training samples, and(4) quality of training samples. The best performing SIF emulator was then applied to a HyPlantflight line containing at sensor radiance information, and the results were compared to the SFM SIFmap of the same flight line. The emulated SIF map was quasi-instantaneously generated, and a goodagreement against the reference SFM map was obtained with a R2 of 0.88 and NRMSE of 3.77%.The SIF emulator was subsequently applied to 7 HyPlant flight lines to evaluate its robustness andportability, leading to a R2 between 0.68 and 0.95, and a NRMSE between 6.42% and 4.13%. EmulatedSIF maps proved to be consistent while processing time was in the order of 3 min. In comparison, theoriginal SFM needed approximately 78 min to complete the SIF processing. Our results suggest thatemulation can be used to efficiently reduce computational loads of SIF retrieval methods. |
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| 700 | 1 | _ | |a Siegmann, Bastian |0 P:(DE-Juel1)172711 |b 1 |
| 700 | 1 | _ | |a Morcillo-Pallarés, Pablo |0 0000-0002-0970-9492 |b 2 |
| 700 | 1 | _ | |a Rivera-Caicedo, Juan Pablo |0 0000-0003-3188-1448 |b 3 |
| 700 | 1 | _ | |a Verrelst, Jochem |0 0000-0002-6313-2081 |b 4 |
| 773 | _ | _ | |a 10.3390/rs13214368 |g Vol. 13, no. 21, p. 4368 - |0 PERI:(DE-600)2513863-7 |n 21 |p 4368 - |t Remote sensing |v 13 |y 2021 |x 2072-4292 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/902156/files/remotesensing-13-04368.pdf |y OpenAccess |
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