Search

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spat

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial

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.

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.

first_pagesettingsOrder Article Reprints Open AccessArticle Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers by Javier Pacheco-Labrador 1,*ORCID,Andreas Hueni 2ORCID,Laura Mihai 3ORCID,Karolina Sakowska 4,Tommaso Julitta 5,Joel Kuusk 6ORCID,Dan Sporea 3,Luis Alonso 7ORCID,Andreas Burkart 5,M. Pilar Cendrero-Mateo 7,Helge Aasen 8ORCID,Yves Goulas 9 andAlasdair Mac Arthur 10 1 Max Planck Institute for Biogeochemistry, Hanks Knöll Straße 10, D-07745 Jena, Germany 2 Remote Sensing Laboratories, University of Zurich, 8057 Zurich, Switzerland 3 CETAL, Photonic Investigations Laboratory, National Institute for Laser, Plasma and Radiation Physics, Măgurele 77125, Romania 4 Institute of Ecology, University of Innsbruck, 6020 Innsbruck, Austria 5 JB Hyperspectral Devices, 40225 Düsseldorf, Germany 6 Tartu Observatory, University of Tartu, 61602 Tõravere, Estonia 7 Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain 8 Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland 9 LMD/IPSL, CNRS, ENS, PSL Research University, Ecole polytechnique, Université Paris-Saclay, UPMC Univ Paris 06, Sorbonne Universités, 91128 Palaiseau, France 10 School of Geosciences, University of Edinburgh, Edinburgh EH9 3FF, UK * Author to whom correspondence should be addressed. Remote Sens. 2019, 11(8), 960; https://doi.org/10.3390/rs11080960 Submission received: 28 February 2019 / Revised: 11 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019 (This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Growing interest in the proximal sensing of sun-induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Ac

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.

Growing interest in the proximal sensing of sun‐induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition

Chlorophyll molecules absorb photosynthetic active radiation (PAR). The resulting excitation energy is dissipated by three competing pathways at the level of photosystem: (i) photochemistry (and, by extension, photosynthesis); (ii) regulated and constitutive thermal energy dissipation; and (iii) chlorophyll-a fluorescence (ChlF). Because the dynamics of photosynthesis modulate the regulated component of thermal energy dissipation (widely addressed as non-photochemical quenching (NPQ)), the relationship between photosynthesis, NPQ and ChlF changes with water, nutrient and light availability. In this study we characterised the relationship between photosynthesis, NPQ and ChlF when conducting light-response curves of photosynthesis in plants growing under different water, nutrient and ambient light conditions. Our goals were to test whether ChlF and photosynthesis correlate in response to water and nutrient deficiency, and determine the optimum PAR level at which the correlation is maximal. Concurrent gas exchange and ChlF light-response curves were measured for Camelina sativa (L.) Crantz and Triticum durum (L.) Desf plants grown under (i) intermediate light growth chamber conditions, and (ii) high light environment field conditions respectively. Plant stress was induced by withdrawing water in the chamber experiment, and applying different nitrogen levels in the field experiment. Our study demonstrated that ChlF was able to track the variations in photosynthetic capacity in both experiments, and that the light level at which plants were grown was optimum for detecting both water and nutrient deficiency with ChlF. The decrease in photosynthesis was found to modulate ChlF via different mechanisms depending on the treatment: through the action of NPQ in response to water stress, or through the action of changes in leaf chlorophyll concentration in response to nitrogen deficiency. This study provides support for the use of remotely sensed ChlF as a proxy to monitor plant