Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence

Yang, Peiqi; Tol, Christiaan van der; Campbell, Petya K. E.; Middleton, Elizabeth M.

doi:10.1016/j.rse.2020.111676

Cited by 97 publications

(113 citation statements)

References 72 publications

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“…The two approaches proposed to serve as proxies for Γ rt that we evaluate are (1) the far-red fluorescence correction vegetation index (FCVI) defined as the difference between the NIR directional reflectance (R NIR ) and the broadband visible (VIS) directional reflectance (R vis ) proposed by Yang et al [25], and (2) the NIR V , defined as the product of R NIR and the normalized difference vegetation index (NDVI = (R NIR − R red )/(R NIR + R red )), i.e., NIR V = NDVI × R NIR , as proposed by Zeng et al [44]. As the GOME-2 SIF data set includes R NIR and R red , we were able to readily compute NIR V collocated with SIF.…”

Section: Data Analysis Approachmentioning

confidence: 99%

“…fPAR chl and e are determined by canopy structure and leaf biochemistry, while physiological effects, including responses to various types of stress, are embedded in Φ F (e.g., [24]). Yang et al [25] defined the product of fPAR and e as Γ rt that they termed the radiative transfer factor.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Wittenberghe et al [33] stressed the importance of quantifying canopy structural parameters such as leaf area index and angular distribution with reflectance data for detailed analysis of remotely-sensed SIF. In addition, it is important to account for solar-satellite geometry that impacts illumination and e in the direction of the observer (e.g., [17,25,30,[34][35][36][37][38][39][40][41][42][43][44][45][46]). Vertical and horizontal inhomogeneity of vegetation canopies also affect canopy-level SIF including its angular distribution [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches have been proposed and/or implemented to more fully account for sun-satellite geometrical dependencies of satellite SIF measurements (e.g., [25,30,[37][38][39][42][43][44][45]52]). These methods rely on various ancillary data including reflectances along with theoretical and/or machine learning constructs (see e.g., [25] for a review). They have been tested using simulated measurements and observations, showing promise for dealing with angular variations in global satellite SIF data sets.…”

Section: Introductionmentioning

confidence: 99%

“…They have been tested using simulated measurements and observations, showing promise for dealing with angular variations in global satellite SIF data sets. Various limitations of these methods, such as for sparsely vegetated scenes where there is a substantial contribution from soil reflectance, have also been discussed (e.g., [25,44,46] and references therein).…”

Section: Introductionmentioning

confidence: 99%

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Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

et al. 2020

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While solar-induced fluorescence (SIF) shows promise as a remotely-sensed measurement directly related to photosynthesis, interpretation and validation of satellite-based SIF retrievals remains a challenge. SIF is influenced by the fraction of absorbed photosynthetically-active radiation at the canopy level that depends upon illumination geometry as well as the escape of SIF through the canopy that depends upon the viewing geometry. Several approaches to estimate the effects of sun-sensor geometry on satellite-based SIF have been proposed, and some have been implemented, most relying upon satellite reflectance measurements and/or other ancillary data sets. These approaches, designed to ultimately estimate intrinsic or physiological components of SIF related to photosynthesis, have not generally been applied globally to satellite measurements. Here, we examine in detail how SIF and related reflectance-based indices from wide swath polar orbiting satellites in low Earth orbit vary systematically due to the host satellite orbital characteristics. We compare SIF and reflectance-based parameters from the Global Ozone Mapping Experiment 2 (GOME-2) on the MetOp-B platform and from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor satellite with a focus on high northern latitudes in summer where observations at similar geometries and local times occur. We show that GOME-2 and TROPOMI SIF observations agree nearly to within estimated uncertainties when they are compared at similar observing geometries. We show that the cross-track dependence of SIF normalized by PAR and related reflectance-based indices are highly correlated for dense canopies, but diverge substantially as the vegetation within a field-of-view becomes more sparse. This has implications for approaches that utilize reflectance measurements to help account for SIF geometrical dependences in satellite measurements. To further help interpret the GOME-2 and TROPOMI SIF observations, we simulated cross-track dependences of PAR normalized SIF and reflectance-based indices with the one dimensional Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) canopy radiative transfer model at sun–satellite geometries that occur across the wide swaths of these instruments and examine the geometrical dependencies of the various components (e.g., fraction of absorbed PAR, SIF yield, and escape of SIF from the canopy) of the observed SIF signal. The simulations show that most of the cross-track variations in SIF result from the escape of SIF through the scattering canopy and not the illumination.

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Section: Data Analysis Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

et al. 2020

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Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests

Pierrat,

Magney,

Maguire

et al. 2024

Ecology

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The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive “greenness” of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIFrelative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems.

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Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies

Chakhvashvili,

Machwitz,

Antala

et al. 2024

Precision Agric

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Introduction Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

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Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence

Cited by 97 publications

References 72 publications

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests

Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies

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