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Sun-induced chlorophyll fluorescence III: benchmarking retrieval methods and sensor characteristics for proximal sensing

Author: Cendrero-Mateo, M.P.
Author: Wieneke, S.
Author: Damm, A.
Author: Alonso, L.
Author: Pinto Espinosa, F.
Author: Moreno, J.
Author: Guanter, L.
Author: Celesti, M.
Author: Rossini, M.
Author: Sabater, N.
Author: Cogliati, S.
Author: Julitta, T.
Author: Rascher, U.
Author: Goulas, Y.
Author: Aasen, H.
Author: Pacheco-Labrador, J.
Author: Mac Arthur, A.
Year: 2019
ISSN: 2072-4292
Abstract: The interest of the scientific community on the remote observation of sun-induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) ≤6% and <5% for F687 and F760, respectively. Furthermore, although the SFM was the least affected by an inaccurate definition of the absorption spectral window (RE = 5%) and/or interpolation strategy (RE = 15–30%), we observed a sensitivity of the SIF retrieval for the simulated training data underlying the SFM model implementation.
Format: PDF
Language: English
Publisher: MDPI
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Type: Article
Place of Publication: Basel (Switzerland)
Pages: art. 962
Issue: 8
Volume: 11
DOI: 10.3390/rs11080962
Agrovoc: SENSORS
Journal: Remote Sensing

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