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Remote Sens. 2019, 11(8), 927; https://doi.org/10.3390/rs11080927

Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level

1
Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
2
Laboratory for Earth Observation, Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Spain
3
Finnish Meteorological Institute, Erik Palmenin Aukio 1, P.O. Box 501, 00101 Helsinki, Finland
4
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain
5
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
6
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
7
Ecole polytechnique, Centre National de la Recherche Scientifique, Sorbonne Université, Ecole Normale Supérieure de Paris, Laboratoire de Météorologie Dynamique, LMD, F-91128 Palaiseau, Paris, France
8
Centre of Excellence PLECO (Plants and Ecosystems), Department of Biology, University of Antwerp, Wilrijk, Belgium
9
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
10
Geography and Spatial Sciences, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Private Bag 76, 7001 Hobart, Australia
11
Laboratory for Earth Observation, Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Spain
12
Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, D-07745 Jena, Germany
13
Laboratory for Earth Observation, Image Processing Laboratory, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Spain
14
Faculty of Science and Technology, Free University of Bozen/Bolzano. Universitätsplatz 1—piazza Università 1, 39100 Bozen-Bolzano, Italy
15
JB Hyperspectral Devices, Düsseldorf, Germany
16
Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
17
Institute of Bio- and Geosciences (IBG-2): Plant Sciences, Forschungszentrum Jülich GmbH, Leo-Brandt-Str., Jülich, Germany
18
GeoSciences University of Edinburgh, King’s Buildings, West Mains Road, Edinburgh EH9 3FF, UK
*
Authors to whom correspondence should be addressed.
Received: 8 March 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 16 April 2019
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Abstract

Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals.
Keywords: sun-induced fluorescence; spectroradiometer; spectrometer; vegetation; radiance; reflectance; remote sensing; FLEX sun-induced fluorescence; spectroradiometer; spectrometer; vegetation; radiance; reflectance; remote sensing; FLEX
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Aasen, H.; Van Wittenberghe, S.; Medina, N.S.; Damm, A.; Goulas, Y.; Wieneke, S.; Hueni, A.; Malenovský, Z.; Alonso, L.; Pacheco-Labrador, J.; Cendrero-Mateo, M.P.; Tomelleri, E.; Burkart, A.; Cogliati, S.; Rascher, U.; Arthur, A.M. Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level. Remote Sens. 2019, 11, 927.

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