Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing

Cendrero-Mateo, M. Pilar; Wieneke, Sebastian; Damm, Alexander; Alonso, Luis; Pinto, Francisco; Moreno, Jose; Guanter, Luis; Celesti, Marco; Rossini, Micol; Sabater, Neus; Cogliati, Sergio; Julitta, Tommaso; Rascher, Uwe; Goulas, Yves; Aasen, Helge; Pacheco-Labrador, Javier; Mac Arthur, Alasdair

doi:10.3390/rs11080962

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Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing

M. Pilar Cendrero-Mateo

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¹¹,

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Javier Pacheco-Labrador

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Alasdair Mac Arthur

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Laboratory for Earth Observation, Image Processing Laboratory, University of Valencia, C/Catedrático José Beltrán, 2, 46980 Paterna, Spain

Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Leo-Brandt-Str., 52425 Jülich, Germany

Centre of Excellence PLECO (Plants and Ecosystems), Department of Biology, University of Antwerp, 2610 Wilrijk, Belgium

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Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

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Department of Surface Waters—Research and Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland

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Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco 56237, Mexico

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Centro de Tecnologías Físicas, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain

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Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, 48 P.zza della Scienza 1, 20126 Milano, Italy

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Finnish Meteorological Institute, Erik Palmenin Aukio 1, P.O. Box 501, FI-00101 Helsinki, Finland

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JB Hyperspectral Devices, Am Botanischen Garten 33, 40225 Düsseldorf, Germany

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Laboratory of Dynamics Meteorology, Ecole Polytechnique, F-91128 Palaiseau, France

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Crop Science, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland

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Max Planck Institute for Biogeochemistry, Hanks Knöll Straße 10, D-07745 Jena, Germany

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GeoSciences University of Edinburgh, King’s Buildings, Edinburgh, Scotland 53 EH9 3FF, UK

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Remote Sens. 2019, 11(8), 962; https://doi.org/10.3390/rs11080962

Received: 1 March 2019 / Revised: 16 April 2019 / Accepted: 17 April 2019 / Published: 22 April 2019

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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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. View Full-Text

Keywords: sun-induced chlorophyll fluorescence; ground spectrometers; retrieval methods

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MDPI and ACS Style

Cendrero-Mateo, M.P.; Wieneke, S.; Damm, A.; Alonso, L.; Pinto, F.; Moreno, J.; Guanter, L.; Celesti, M.; Rossini, M.; Sabater, N.; Cogliati, S.; Julitta, T.; Rascher, U.; Goulas, Y.; Aasen, H.; Pacheco-Labrador, J.; Mac Arthur, A. Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing. Remote Sens. 2019, 11, 962. https://doi.org/10.3390/rs11080962

AMA Style

Cendrero-Mateo MP, Wieneke S, Damm A, Alonso L, Pinto F, Moreno J, Guanter L, Celesti M, Rossini M, Sabater N, Cogliati S, Julitta T, Rascher U, Goulas Y, Aasen H, Pacheco-Labrador J, Mac Arthur A. Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing. Remote Sensing. 2019; 11(8):962. https://doi.org/10.3390/rs11080962

Chicago/Turabian Style

Cendrero-Mateo, M. P., Sebastian Wieneke, Alexander Damm, Luis Alonso, Francisco Pinto, Jose Moreno, Luis Guanter, Marco Celesti, Micol Rossini, Neus Sabater, Sergio Cogliati, Tommaso Julitta, Uwe Rascher, Yves Goulas, Helge Aasen, Javier Pacheco-Labrador, and Alasdair Mac Arthur. 2019. "Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing" Remote Sensing 11, no. 8: 962. https://doi.org/10.3390/rs11080962

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Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing

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