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The emission of solar-induced chlorophyll fluorescence (F) is a pivotal process to infer vegetation health and functioning that can be monitored by remote sensing. However, most of the current remote sensing methods retrieve only F at top-of-canopy level, therefore making the link with physiological processes occurring at photosystem level not trivial. In this study, we develop a novel machine learning Fourier (phasor)-based algorithm to retrieve F both at canopy level and after considering the reabsorption (i.e. photosystem level), consistently with relevant biophysical variables, exploiting the canopy apparent reflectance spectra (Rapp). In particular, Rapp is divided in consecutive spectral windows, where the Discrete Fourier Transform (DFT) is computed. Then, the DFT results in each window are exploited to estimate the fluorescence spectra and biophysical parameters, together with their uncertainties, by means of a supervised machine learning algorithm coupled to a statistical-based retrieval pipeline. The algorithm has been trained through synthetic Rapp spectra, obtained from simulations based on a Radiative Transfer (RT) model. As a proof of concept, the theoretical approach is then applied to experimental data, acquired both from crops and forests, at close and high soil-sensor distance respectively, to evaluate the retrieval accuracy of biophysical and F parameters. In particular, for the first time Rapp is used to extract the temporal evolution of F at canopy and photosystem levels and its quantum efficiency together with different biophysical variables, during the growing season of two agricultural crops. Furthermore, tower-based solar-induced fluorescence measurements in a deciduous forest are exploited to evaluate the performance of our algorithm when the atmospheric reabsorption and scattering are not negligible. The reliability of the proposed method is evaluated through a comparison with F spectra extracted from the state of the art SpecFit retrieval algorithm. This work promises a substantial advance toward a new accurate retrieval method for fluorescence signals and biophysical parameters at canopy and photosystem levels.

Over the last two decades, sun-induced fluorescence (SIF), a part of energy absorbed by chlorophyll and reemitted at longer wavelengths (red and far-red) than for excitation, has been studied by many researchers, considered a proxy for light-use efficiency (LUE) and functioning of photosynthesis machinery. Although many studies support the use of SIF as a tool for monitoring of photosynthetic activity, SIF measurements are still a challenging task. The anisotropy of SIF emission was not yet fully characterised, which could potentially help to relate SIF to photosynthetic processes and refine the influence of plant traits (e.g. leaf structure) on SIF signal, therefore, we focused on defining experimental design requirements for characterisation of SIF bidirectional behaviour

The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.

International audience; Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIFnadir) on GPP, compared to total SIF emitted by all leaves (SIFtotal) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIFtotal is better correlated to GPP than raw SIF observations (SIFobs). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIFobs, SIFnadir and SIFtotal) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIFobs, SIFnadir and SIFtotal in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. The results indicate that diurnal SIFnadir has stronger correlations to GPP than SIFobs for field measurements, with the increase of mean coefficient of determination (R2) by 0.05-0.07 for far-red band and 0.11-0.20 for red band. SIFnadir shows comparable performance with SIFtotal for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIFnadir and SIFobs, the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIFnadir and SIFtotal in estimating GPP. TROPOMI-based analysis further confirms that SIFnadir overall has higher correlations to AmeriFlux GPP than SIFobs for different plant functional types and shows similar performance with SIFtotal. Compared to SIFtotal, the estimation of SIFnadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

Leaf nitrogen content (LNC), an indicator for the amount of photosynthetic proteins, plays an important role to understand plant function and status. In previous studies, vegetation indices (VIs) have been demonstrated to monitor LNC non-destructively, but which is influenced by backgrounds, and lacks specificity for nitrogen stress. In this study, sun-induced chlorophyll fluorescence (SIF), a novel technique related to plant physiology state, was proposed to estimate area-based and mass-based LNC at both leaf and canopy scales. In addition, SIF indices were evaluated to retrieve photosynthesis nitrogen use efficiency (PNUE), an important trait of leaf economics and physiology, based on the relationships between SIF, photosynthesis, and LNC. This study was conducted on two field experiments of winter wheat with different nitrogen regimes in Rugao, Jiangsu Province, China during 2016-2017 and 2017-2018 growing seasons. We took measurements of SIF, reflectance, biochemical and growth structural parameters at the leaf and canopy scales. The SIF signal was collected using ASD (Analytical Spectral Devices, Boulder, CO, USA) and QEpro (Ocean Optics, Dunedin, FL, USA) spectrometers at the two observational scales, with a full width at half maximum (FWHM) of 1.4 nm and 0.13 nm, respectively. SIF indices were calculated based on the SIF signal extracted at two oxygen absorption bands. Our results demonstrated that area-based LNC was better related to SIF indices and VIs than mass-based LNC. SIF ratio index (SIFR) and normalized SIF index (SIFN), defined as SIF761/SIF687 and (SIF761-SIF687)/(SIF761+SIF687) separately, performed better in monitoring area-based LNC at the two observation scales than CIred edge, which performed best in VIs group. Compared with CIred edge, the best estimation accuracy of SIF indices for area-based LNC increased by 0.08 and 0.02 at the leaf and canopy scales, separately. And when using SIFR and SIFN to monitor area-based LNC, there is no saturation phenomenon, which occurs using traditional VIs. From the whole range of data, area-based LNC was closely related to several plant traits (leaf: area-based leaf chlorophyll content (LCC) (LCCarea), leaf mass per area (LMA); canopy: area-based canopy LCC (CCCarea), leaf area index (LAI), leaf dry weight (LDW) per unit soil area, and LMA), which was consistent with previous studies. However, in specific group with fixed area-based LCC value, although area-based LNC almost wasn’t significantly correlated with these traits, SIFR and SIFN were instead always highly correlated with area-based LNC in each small datasets on two observation scales (leaf scale: R2>0.50, R2>0.46; canopy scale: R2>0.41, R2>0.42). Thus, the contribution of SIFR and SIFN to estimate area-based LNC wasn’t only the plant traits listed, but also other internal characters, like nitrogen allocation and proportion. Moreover, SIFR and SIFN were proved to be potential detectors to retrieve PNUE. These findings would provide us a new perspective for understanding plant nitrogen status from remote sensing observations, detecting plant function and managing precise agriculture.

Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) have improved the capabilities of monitoring large-scale Gross Primary Productivity (GPP). However, SIF observations are subject to directional effects which can lead to considerable uncertainties in various applications. Practical approaches for normalizing directional SIF observations to nadir viewing, to minimize the directional effects, have not been well studied. Here we developed two practical and physically-solid approaches for removing the directional effects of anisotropic SIF observations: one is based on near-infrared or red reflectance of vegetation (NIRv and Redv), and the other is based on the kernel-driven model with multi-angular SIF measurements. The first approach uses surface reflectance while the second approach directly leverages multi-angular SIF measurements. The performance of the two approaches was evaluated using a dataset of multi-angular measurements of SIF and reflectance collected with a high-resolution field spectrometer over different plant canopies. Results show that the relative mean absolute errors between the normalized nadir SIF and the observed SIF at nadir decrease by 3–6% (far-red) and 6–8% (red) for the first approach, and by 7–13% and 6–11% for the second approach, compared to the original data, respectively. The effectiveness and simplicity of our proposed approaches provide great potential to generate long-term and consistent SIF data records with minimized directional effects.

Chlorophyll fluorescence (ChlF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChlF (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called 'Piccolo-Doppio' and 'AirSIF' systems, respectively. Furthermore, JB Hyperspectral Devices' ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: 'FloXPlane'. We present technical details and preliminary results obtained from each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed.

Plant functional diversity, defined as the range of plant chemical, physiological and structural properties within plants, is a key component of biodiversity which controls the ecosystem functioning and stability. Monitoring its variations across space and over time is critical in ecological studies. So far, several reflectance-based metrics have been tested to achieve this objective, yielding different degrees of success. Our work aimed at exploring the potential of a novel metric based on far-red sun-induced chlorophyll fluorescence (F760) to map the functional diversity of terrestrial ecosystems. This was achieved exploiting high-resolution images collected over a mixed forest ecosystem with the HyPlant sensor, deployed as an airborne demonstrator of the forthcoming ESA-FLEX satellite. A reference functional diversity map was obtained applying the Rao's Q entropy metric on principal components calculated on key plant functional trait maps retrieved from the hyperspectral reflectance cube. Based on the spectral variation hypothesis, which states that the biodiversity signal is encoded in the spectral heterogeneity, two moving window-based approaches were tested to estimate the functional diversity from continuous spectral data: i) the Rao's Q entropy metric calculated on the normalized difference vegetation index (NDVI) and ii) the coefficient of variation (CV) calculated on hyperspectral reflectance. Finally, a third moving window approach was used to estimate the functional diversity based on F760 heterogeneity quantified through the calculation of the Rao's Q entropy metric. Results showed a strong underestimation of the functional diversity using the Rao's Q index based on NDVI and the CV of reflectance. In both cases, a weak correlation was found against the reference functional diversity map (r2 = 0.05, p < .001 and r2 = 0.04, p < .001, respectively). Conversely, the Rao's Q index calculated on F760 revealed similar patterns as the ones observed in the reference map and a better correlation (r2 = 0.5, p < .001). This corroborates the potential of far-red F for assessing the functional diversity of terrestrial eco- systems, opening unprecedented perspectives for biodiversity monitoring across different spatial and temporal scales.

The increasing amount of continuous time series of solar-induced fluorescence (SIF) and vegetation indices (e.g. Photochemical Reflectance Index, PRI) acquired with high temporal (sub-minute) frequencies is foreseen to allow tracking of the structural and physiological changes of vegetation in a variety of ecosystems. Coupled with observations of CO2, water, and energy fluxes from eddy covariance flux towers, these measurements can bring new insights into the remote monitoring of ecosystem functioning. However, continuously changing solar-view geometry imposes directional effects on diurnal cycles of the fluorescence radiance in the observation direction (F) and PRI, controlled by structural and biochemical vegetation properties. An improved understanding of these variations can potentially help to disentangle directional responses of vegetation from physiological ones in the continuous long-term optical measurements and, therefore, allow to deconvolve the physiological information relevant to ecosystem functioning. Moreover, this will also be useful for better interpreting and validating F and PRI satellite products (e.g., from the upcoming ESA FLEX mission). Many previous studies focused on the characterization of reflectance directionality, but only a handful of studies investigated directional effects on F and vegetation indices related to plant physiology. The aim of this study is to contribute to the understanding of red (F687) and far-red (F760) fluorescence and PRI anisotropy based on field spectroscopy data and simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. We present an extensive dataset of multi-angular measurements of F and PRI collected at canopy level with a high-resolution instrument (FloX, JB Hyperspectral Devices UG, Germany) over different ecosystems: Mediterranean grassland, alfalfa, chickpea and rice. We found, that F760 and F687 directional responses of horizontally homogeneous canopies are characterized by higher values in the backscatter direction with a maximum in the hotspot and lower values in the forward scatter direction. The PRI exhibited similar response due to its sensitivity to sunlit-shaded canopy fractions. As confirmed by radiative transfer forward simulations, we show that in the field measurements leaf inclination distribution function controls the shape of F and PRI anisotropic response (bowl-like/dome-like shapes), while leaf area index and the ratio of leaf width to canopy height affect the magnitude and the width of the hotspot. Finally, we discuss the implications of off-nadir viewing geometry for continuous ground measurements. F observations under oblique viewing angles showed up to 67 % difference compared to nadir observations, therefore, we suggest maintaining nadir viewing geometry for continuous measurements of F and vegetation indices. Alternatively, a correction scheme should be developed and tested against multi-angular measurements to properly account for anisotropy of canopy F and PRI observations. The quantitative characterization of these effects in varying illumination geometries for different canopies that was performed in this study will also be useful for the validation of remote sensing F and PRI products at different spatial and temporal scales.

Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis.

[EN] The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400-2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670-780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps., We gratefully acknowledge the financial support by the European Space Agency (ESA) for airborne data acquisition, development of the HyPlant processing chain and data analysis in the frame of the HyFLEX campaign (ESA contract no. 4000107143/12/NL/FF/If CCN3), the FLEXSense campaign (ESA Contract No. RFP/3-15477/18/NL/NA) and the HYPER project (ESA Contract No. 4000112890/14/NL/FF/gp). The Deutsche Forschungsgemeinschaft (DFG) provided additional financial support by the SFB/TR 32 'Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation' project D2. Moreover, this work has partially been funded by the German Federal Ministry of Education and Research within the German-Plant-Phenotyping Network (DPPN) (project identification number: 031A053), the European Union in the frame of the INTEREG project 'Sensor Products for Enterprises Creating Technological Opportunities in airborne Remote Sensing' (SPECTORS) (project identification number: 143081) and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 721995 in the frame of the 'Training on Remote Sensing for Ecosystem Modelling' (TRuStEE) network.

Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed (two parameters for F). The novel algorithm is developed and tested on a set of radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes, considering different chlorophyll content, leaf area index and noise levels to produce a large variability in fluorescence and reflectance spectra. The retrieval accuracy is quantified based on several metrics derived from the F spectrum (i.e., red and far-red peaks, O2 bands and spectrally-integrated values). Further, the algorithm is employed to process experimental field spectroscopy measurements collected over different crops during a long-lasting field campaign. The reliability of the retrieval algorithm on experimental measurements is evaluated by cross-comparison with F values computed by an independent retrieval method (i.e., SFM at O2 bands). For the first time, the evolution of the F spectrum along the entire growing season for a forage crop is analyzed and three diverse F spectra are identified at different growing stages. The results show that red F is larger for young canopy; while red and far-red F have similar intensity in an intermediate stage; finally, far-red F is significantly larger for the rest of the season.

[EN] 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, This article is based upon work from COST Action ES1309 OPTIMISE, supported by COST (European Cooperation in Science and Technology; www.cost.eu).Cendrero-Mateo M.P. and Alonso L. are currently funded by AVANFLEX project (Advanced Products for the FLEX mission), no ESP2016-79503-C2-1-P, Ministry of Economy and Competitiveness, Spain. Wieneke S. is currently funded by the individual fellowship of the European Union's H2020 Marie Sklodowska-Curie actions under the grant agreement ReSPEc no [795299]. Goulas Y. is currently funded by the ECOFLUO project (In-situ Remote Sensing of Chlorophyll Fluorescence to Monitor Carbon Dynamics in Ecosystems in support of the FLEX mission), no 4500058116 and 4500058229, Centre National d'Etudes Spatiales (CNES), France. Mac Arthur, A. was funded by the UK's NERC/NCEO Field Spectroscopy Facility, at the University of Edinburgh. Pacheco-Labrador, J. was supported by the EnMAP project MoReDEHESHyReS "Modelling Responses of Dehesas with Hyperspectral Remote Sensing" (Contract No. 50EE1621, German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy) and the Alexander von Humboldt Foundation via Max-Planck Prize to Markus Reichstein.

Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

The contribution presents the development and testing of a fluorescence retrieval scheme based on the ESA's FLuorescence EXplorer mission concept. The algorithm employs on a coupled surface-atmosphere forward model at oxygen absorption bands: i) the atmospheric effect is computed by MODTRAN5; ii) the surface reflectance and fluorescence are modeled by means of the Spectral Fitting approach. The algorithm, previously tested on numerical simulations, was further implemented and optimized to process real observations collected by the FLEX airborne demonstrator HyPlant. The retrieval scheme has been tested on a number of flight lines collected in several locations, different ecosystems types, atmospheric conditions and instrument observation conditions. For the first time, this work shows the capability of retrieving canopy fluorescence from real airborne observations by means of a physically-based algorithm as envisaged for FLEX. The results achieved on the large core data sets of imageries show the consistency of the physical retrieval algorithm for a wide range of scenarios and fluorescence values are in line with ground observations.

Negli ultimi decenni il telerilevamento iperspettrale è stato utilizzato per stimare con successo informazioni sulle proprietà delle piante a diverse scale di indagine. Questa tesi si focalizza sull’uso di dati telerilevati iperspettrali per stimare lo stato di salute della vegetazione a livello di canopy. Recentemente, il telerilevamento passivo della fluorescenza della clorofilla indotta dal Sole (F) è emerso come un ambito scientifico di grande interesse per la studio del comportamento dinamico della fotosintesi. F è un prodotto dell’assorbimento della luce da parte della clorofilla a, emesso come radiazione elettromagnetica nel rosso e nel vicino infrarosso (≈ 640 nm to 850 nm), ed è relazionato allo stato energetico dei fotosistemi. Oltre che dalla fisiologia F è influenzata anche dalla struttura della foglia e della canopy, dalla concentrazione dei pigmenti e dalle condizioni di illuminazione/meteorologiche, e la sua interpretazione univoca è tuttora complessa. Questo guida l’interesse nell’esplorare metriche in grado di isolare l’informazione legata alla fisiologia presente nel dato di F. In questa tesi ho analizzato due casi studio: i) una foresta di pino loblolly (Parker Tract, North Carolina, U.S.A.) dove ho investigato la variazione di F e delle metriche derivate al variare dei processi fisiologici legati all’età delle piante; e ii) un esperimento di stress indotto dove è stata inibita la fotosintesi di un tappeto erboso. Nel primo caso di studio, utilizzato dati iperspettrali acquisiti con il sensore aereo HyPlant per caratterizzare la F emessa da 18 stand coetanei di pino di èta compresa tra 3 e 46 anni, e per calcolare la radiazione fotosinteticamente attiva assorbita (APAR). Ho calcolato gli yield di F normalizzando F per la APAR. I risultati mostrano che nel loblolly: i) gli yield di F nel rosso e nel vicino infrarosso cambiano al variare dell’età dello stand, i giovani pini dissipano più F nel rosso rispetto a quelli più maturi (fino al 60% in più) e il declino dello yield di F nel rosso con l’età dello stand è più pronunciato di quello di F; ii) il declino dello yield di F nel rosso può essere relazionato alla limitazione idraulica che si manifesta nel loblolly durante la crescita. Nel secondo caso di studio tre plot di 9m x 12m di un tappeto erboso sono stati trattati con diverse dosi di Chlortoluron. Questo erbicida inibisce la fotosintesi bloccando la catena di trasporto degli elettroni. Ho utilizzato dati acquisiti a terra con spettroradiometri ad alta risoluzione , immagini aeree con i sensori HyPlant e TASI-600, e misure di scambi gassosi, per studiare le dinamiche a breve termine dell’efficienza fotosintetica della vegetazione, indotte dallo stress. I risultati mostrano che subito dopo il trattamento si è verificato un rapido incremento di F, dello yield di F, della temperatura della canopy e del Photosynthetic Reflectance Index (PRI). Successivamente è stato osservato un decremento dose-specifico di F e del PRI, assieme a una riduzione del contenuto di clorofilla a e di altri indici di vegetazione legati ai pigmenti. A partire dai dati spettrali acquisiti a terra sul tappeto erboso, ho invertito numericamente una versione semplificata del modello SCOPE, per stimare per la prima contemporaneamente lo spettro completo di F, lo yield di F, assieme ai principali parametri della vegetazione che controllano l’assorbimento e il riassorbimento della luce. L’effetto del contenuto di pigmenti, delle proprietà strutturali della foglia e della canopy, e la fisiologia sono state discriminate con successo. La loro osservazione combinata nel tempo ha portato al riconoscimento di pattern dinamici di risposta e di adattamento allo stress. Hyperspectral Remote Sensing (RS) data have been exploited in the last decades to successfully retrieve information about plant properties at different scales. This thesis focuses on the use of state of the art hyperspectral RS data to retrieve vegetation status at canopy level, using both experimental and modeled data. In the last years, RS of Sun-induced chlorophyll fluorescence (F) emerged as a novel and promising scientific field for studying the dynamic behavior of photosynthesis. F is a physical side product of chlorophyll a light absorption that is emitted as an electromagnetic radiation in the red and far-red spectral regions (≈ 640 nm to 850 nm), and it is related to the energetic status of the photosystems. Nevertheless, apart from physiology F is concurrently influenced by leaf and canopy structure, pigment concentration and weather/illumination conditions, and its unambiguous interpretation is still challenging. This drives the interest in exploring F-derived metrics able to disentangle the physiological information from the remotely sensed F signal. In this thesis I analyzed data from two case studies: i) a managed loblolly pine forest (Parker Tract forest, North Carolina, U.S.A.) where I investigated how F and F-derived metrics vary with age-related changes in plant physiology; and ii) an experiment of induced stress where the photosynthesis of a homogeneous lawn was inhibited with a herbicide treatment. In the first case-study, I used hyperspectral data acquired with the HyPlant airborne sensor to characterize the F emission of 18 evenly aged stands in a range from 3 to 46 years old, and to calculate the Absorbed Photosynthetically Active Radiation (APAR). I computed the F yields in the red and far-red regions normalizing the corresponding F data for the APAR. Results show that in loblolly pine: i) red F and red F yield change with stand age, younger loblolly pines dissipate more red F than older one (up to 60% more) and the decline of F yield with stand age is more pronounced than that for red F; ii) the decline of red F yield can be related to the increase in water limitation occurring as loblolly trees grow in age and height. In the second case study three 9 m × 12 m plots of a homogeneous lawn were treated with different doses of a commercial formulation of Chlortoluron. This herbicide inhibits photosynthesis by blocking the electron transport chain in the photosynthetic apparatus. I exploited data collected on the ground with very high resolution spectroradiometers, airborne images collected with the HyPlant and the TASI-600 sensors, as well as canopy-level gas exchange measurements collected with closed chambers, in order to detect short-term dynamics of photosynthetic efficiency in vegetation, induced by stress. Results show that immediately after the application there was a rapid increase of F, F yields and the photosynthetic reflectance index (PRI). Canopy temperature also increased after the application of Dicuran. A later decrease of fluorescence and PRI was observed together with a reduction of chlorophyll a content and a drop of pigment-related vegetation indices. Moreover, the dosage of Chlortoluron had an impact in the dynamics of F. Starting from the ground level hyperspectral measurements over the lawn, I inverted numerically a simplified version of the SCOPE model to concurrently retrieve F, F yield and several biochemical and biophysical parameters of the vegetation from apparent reflectance data. For the first time the full spectrum of canopy F, the fluorescence yield, as well as the main vegetation parameters that control light absorption and reabsorption, were retrieved concurrently using canopy-level high resolution apparent reflectance measurements. The effects of pigment content, leaf/canopy structural properties and physiology were effectively discriminated. Their combined observation over time led to the recognition of dynamic patterns of stress adaptation and stress recovery.

International audience; The photosynthetic, optical and morphological characteristics of a chlorophyll-deficient (Chldeficient) “yellow” soybean mutant (MinnGold) were examined in comparison with two green varieties (MN0095 and Eiko). Despite the large difference in Chl content, similar leaf photosynthesis rates were maintained in the Chl-deficient mutant by offsetting the reduced absorption of red photons by a small increase in photochemical efficiency and lower non-photochemical quenching (NPQ). When grown in the field, at full canopy cover, the mutants reflected a significantly larger proportion of incoming shortwave radiation, but the total canopy light absorption was only slightly reduced, most likely due to a deeper penetration of light into the canopy space. As a consequence, canopy-scale grossprimary production and ecosystem respiration were comparable between the Chl-deficient mutant and the green variety. However, total biomass production was lower in the mutant, which indicates that processes other than steady state photosynthesis, caused a reduction in biomass accumulation over time. Analysis of NPQ relaxation and gas exchange in Chl-deficient and green leaves after transitions from high to low light conditions suggested that dynamic photosynthesis might be responsible for the reduced biomass production in the Chl-deficient mutant under field conditions.

Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far-red Sun Induced chlorophyll Fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even-aged loblolly pine plantations in North Carolina (USA). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far-red peaks was computed. This quantifies the fluorescence emission efficiencies that is more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly 2-fold higher red fluorescence yield than mature forest plantations, while the far-red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intra-pixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long-term forest dynamics in response to future climatic conditions from remote sensing of SIF. This article is protected by copyright. All rights reserved

Sun-induced canopy chlorophyll fluorescence in both the red (FR) and far-red (FFR) regions was estimated across a range of temporal scales and a range of species from different plant functional types using high resolution radiance spectra collected on the ground. Field measurements were collected with a state-of-the-art spectrometer setup and standardized methodology. Results showed that different plant species were characterized by different fluorescence magnitude. In general, the highest fluorescence emissions were measured in crops followed by broadleaf and then needleleaf species. Red fluorescence values were generally lower than those measured in the far-red region due to the reabsorption of FR by photosynthetic pigments within the canopy layers. Canopy chlorophyll fluorescence was related to plant photosynthetic capacity, but also varied according to leaf and canopy characteristics, such as leaf chlorophyll concentration and Leaf Area Index (LAI). Results gathered from field measurements were compared to radiative transfer model simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. Overall, simulation results confirmed a major contribution of leaf chlorophyll concentration and LAI to the fluorescence signal. However, some discrepancies between simulated and experimental data were found in broadleaf species. These discrepancies may be explained by uncertainties in individual species LAI estimation in mixed forests or by the effect of other model parameters and/or model representation errors. This is the first study showing sun-induced fluorescence experimental data on the variations in the two emission regions and providing quantitative information about the absolute magnitude of fluorescence emission from a range of vegetation types.

Sun-Induced Fluorescence (SIF) estimates from remote sensing data are becoming more and more popular, from leaf level to global scale. This signal is a promising tool for the remote estimation of plants functioning, and its accurate quantification from space in the red and far red regions is the main aim of the Florescence Explorer (FLEX) mission, candidate for European Space Agency eighth Earth Explorer (ESA, EE-8). Although it is inherently related to plant photosynthesis efficiency, remotely sensed SIF signal is also highly influenced by environmental variables and vegetation biophysical and biochemical parameters. According to the recent Global Sensitivity Analysis (GSA) of the state-of-the-art Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model, the maximum rate of carboxylation (Vcmax) drives only a relatively small portion of the SIF signal. In turn, variables such as broadband incoming shortwave radiation (Rin), Leaf Chlorophyll Content (LCC) and Leaf Area Index (LAI) are major drivers of SIF intensity. Their variation in the spatial and temporal domains, if not properly accounted for, can lead to biased interpretation of the remotely sensed SIF signal in relation to the physiological behaviour of the vegetation. In this contribution we evaluate the potential of normalizing SIF by different quantities related to the incident photosynthetically active radiation (PAR), the photosynthetically active radiation absorbed by the canopy (APAR) and traditional vegetation indices linked to canopy greenness, for minimizing the influence of Rin, LAI and LCC on the SIF signal. The analysis is carried out using a simulated dataset generated with the SCOPE model, setting all variables fixed except for Rin, LAI and LCC. For these three variables, a broad range of input values is tested . The effect of the normalization methods is evaluated individually for several SIF wavelengths in the red and far-red emission regions. The indices related to APAR perform best among all investigated ones, and are followed by the vegetation indices linked to canopy greenness and finally by metrics related to the incoming radiation. The use of APAR performs well in normalizing the effect of LCC variations on both red and far-red SIF, while LAI influence is still affecting the red SIF even after the normalization. The metrics proposed for SIF normalization are then tested using an experimental dataset collected in August 2015 in an experimental farm in Northern Italy, over two monospecific soybean vegetation patches (a wild variety and a mutant one). Along with high spectral resolution top of canopy measurements, the main canopy biochemical and structural characteristics of the two soybean varieties were also measured. In-situ results confirm those obtained from the analysis of canopy fluorescence emission spectra simulated with the SCOPE model. The analysis is carried out using a simulated dataset generated with the SCOPE model, setting all variables fixed except for Rin, LAI and LCC. For these three variables, a broad range of input values is tested . The effect of the normalization methods is evaluated individually for several SIF wavelengths in the red and far-red emission regions. The indices related to APAR perform best among all investigated ones, and are followed by the vegetation indices linked to canopy greenness and finally by metrics related to the incoming radiation. The use of APAR performs well in normalizing the effect of LCC variations on both red and far-red SIF, while LAI influence is still affecting the red SIF even after the normalization. The metrics proposed for SIF normalization are then tested using an experimental dataset collected in August 2015 in an experimental farm in Northern Italy, over two monospecific soybean vegetation patches (a wild variety and a mutant one). Along with high spectral resolution top of canopy measurements, the main canopy biochemical and structural characteristics of the two soybean varieties were also measured. In-situ results confirm those obtained from the analysis of canopy fluorescence emission spectra simulated with the SCOPE model.

Sun-induced chlorophyll fluorescence signal is explored as a novel remote sensing method, notable for its potential to be used as a direct indicator of photosynthetic efficiency. In the last years, there was an increasing interest of the scientific community on the remote sensing of Sun-Induced chlorophyll Fluorescence (SIF). Several SIF estimates in the far-red region have been produced from spaceborne sensors, and the future FLEX satellite mission (European Space Agency, Earth-Explorer 8) aims to detect canopy level SIF in both red and far-red regions at global scale. In the context of FLEX calibration/validation activities, a network of ground station to calibrate/validate SIF estimates from space can be considered crucial, but few studies have proposed optimal technical requirements for commercially available spectroradiometers. At canopy level, SIF is traditionally retrieved from incoming and upwelling radiance measurements, exploiting two narrow oxygen absorption bands, within the O2-B and O2-A spectral regions. Only recently, the feasibility of retrieving the SIF spectrum was demonstrated. The rationale behind the exploitation of narrow spectral regions, characterized by strong absorptions, resides in the higher contribution of SIF with respect to the reflected radiance. In order to detect the signal in those narrow spectral regions, high spectral resolution observation is needed. In this study, we compared several high resolution field spectroradiometers with different Full Width at Half Maximum (FWHM), Spectral Sampling Interval (SSI) and Signal-to-Noise Ratio (SNR), to evaluate their performance in SIF estimates. We applied several state-of-the-art, radiance-based retrieval algorithms to radiance measurements taken with the FluoWAT. This device allows to measure leaf reflected and transmitted radiance, solar incident radiance and, upward and downward leaf fluorescence spectrum by means of a low pass filter, that were used as a reference. Results show that there is a large variability in the Relative Root Mean Square Error (RRMSE) between reference and retrieved values among different setups (up to 45%). Among the tested spectrometers, those with a higher SNR, low SSI and low FWHM seem to be favoured. Retrieval algorithms based on the Spectral Fitting Method perform generally better than various implementation of the Fraunhofer Line Depth methods, particularly for O2-B fluorescence, while differences are less evident for the O2-A fluorescence.

In this contribution we present activities and selected results obtained in recent studies and campaigns conducted in the context of the FLuorescence EXplorer (FLEX) mission. FLEX is a candidate mission for the ESA 8th Earth Explorer and large efforts are currently dedicated to the development of an implementation scheme for an accurate mapping of fluorescence from the selected spaceborne sensor and mission configuration. Field and airborne data collected in different experimental campaigns, together with simulated data, have been used to demonstrate the feasibility of fluorescence retrievals and the potential of exploiting high spatial resolution fluorescence maps for a better understanding of the environment from space.