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A novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed. The combined retrieval of vegetation biochemical and biophysical parameters and Sun-induced chlorophyll fluorescence ( F ) was investigated exploiting high resolution spectral measurements in the visible and near-infrared spectral regions. First, the retrieval scheme was evaluated against a synthetic dataset. Then, it was applied to very high resolution (sub-nanometer) canopy level spectral measurements collected over a lawn treated with different doses of a herbicide (Chlorotoluron) known to instantaneously inhibit both Photochemical and Non-Photochemical Quenching (PQ and NPQ, respectively). For the first time the full spectrum of canopy F , the fluorescence quantum yield (Φ F ), as well as the main vegetation parameters that control light absorption and reabsorption, were retrieved concurrently using canopy-level high resolution apparent reflectance ( ρ * ) spectra. 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. As a reference, F values obtained with the model inversion were compared to those retrieved with state of the art Spectral Fitting Methods (SFM) and SpecFit retrieval algorithms applied on field data. Φ F retrieved from ρ * was eventually compared with an independent biophysical model of photosynthesis and fluorescence. These results foster the use of repeated hyperspectral remote sensing observations together with radiative transfer and biochemical models for plant status monitoring. [ABSTRACT FROM AUTHOR]

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 (F 760) 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 F 760 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 F 760 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 ecosystems, opening unprecedented perspectives for biodiversity monitoring across different spatial and temporal scales. • Far-red fluorescence heterogeneity provides information on functional diversity. • Far-red fluorescence outperforms diversity measures based on NDVI heterogeneity. • Far-red fluorescence captures the spatial variability of functional traits (LCC, LMA). [ABSTRACT FROM AUTHOR]

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. • Synergistic usage of optical domains permits a holistic view of plant stress. • Three dose- and duration-related phases of stress defined. • Study designs were mainly driven by available sensors and economic reasons. • Coupling multi-domain observations with integrated crop growth models is proposed. [ABSTRACT FROM AUTHOR]

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. • Spectral, radiometric and SIF retrieval-related uncertainties are explored. • The investigated sources of uncertainty are relevant for SIF cal/val activities. • This work contributes to the design and implementation of SIF cal/val networks. • A thorough design of site infrastructure and analysis capability is important. • Spatial heterogeneity is a crucial aspect in SIF mission cal/val. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) shows great potential to assess plants physiological state and response to environmental changes. Recently the near-infrared reflectance of vegetation (NIRv) provides a promising way to quantify the confounding effect of canopy structure in SIF, while the difference between SIF and NIRv under varying environmental conditions has not been well explored. Here we developed a simple approach to extract the fluorescence yield (Φ F) by the combined use of SIF and the near-infrared radiance of vegetation (NIRvR). The proposed NIRvR approach was evaluated in multiple ways, including with the seasonal leaf-level steady-state fluorescence yield. Results indicate that NIRvR-derived Φ F well captured the seasonal variation of the fluorescence yield changes, and achieved similar results with the existing approach. Both SIF and NIRvR were derived from the airborne imaging fluorescence spectrometer HyPlant for three case studies to evaluate the impacts of light adaptation, heat stress and water limitation on Φ F. For the light adaptation case study, Φ F over the low-light adapted sugar beet field was about 1.3 times larger compared to an unaffected reference area while the difference in NIRvR was minimal, which clearly shows the short-term photosynthetic light induction effect and the ability of SIF to detect plant physiological responses. For the heat stress experiment, Φ F decreased during a natural heatwave in 2015 in the fields of rapeseed from 0.0150 to 0.0130, barley from 0.0152 to 0.0144, and wheat from 0.0146 to 0.0142 which showed signs of senescence, while slightly increased from 0.0125 to 0.0130 in the corn field which was still in growing. At the water-limited sugar beet field, Φ F first increased towards solar noon and then slightly decreased during the afternoon over the water-limited areas from 0.017 to 0.021 and 0.020, with high temperature and high light at noon. The advantages to use SIF/NIRvR as a proxy of Φ F to detect stress-induced limitations in photosynthesis include that the impacts of canopy structure and sun-sensor geometry on the Φ F estimation are explicitly cancelled, and photosynthetically active radiation (PAR) is not required as input. Finally, our approach is directly applicable to satellite-derived estimates of SIF, enabling the study of variations in Φ F to detect the effects of abiotic changes and stresses at large scale. • The slope of SIF and NIRvR indicates the canopy-scale fluorescence yield Φ F. • Φ F can detect several effects of abiotic changes, e.g., the heat stress. • This approach with HyPlant dataset can minimize the canopy structure effects. • This method does not require PAR and FPAR products as inputs to calculate Φ F. [ABSTRACT FROM AUTHOR]

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 (NIR v and Red v), 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. • Practical approaches for removing the directional effects in far-red and red SIF. • NIR v /Red v -based approach only depends on red and near-infrared reflectance. • Kernel-driven approach requires collection of directional SIF observations. [ABSTRACT FROM AUTHOR]