Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion

Celesti, Marco; Tol, Christiaan van der; Cogliati, Sergio; Panigada, Cinzia; Yang, Peiqi; Pinto, Francisco; Rascher, Uwe; Miglietta, Franco; Colombo, R.; Rossini, Micol

doi:10.1016/j.rse.2018.05.013

Cited by 45 publications

(36 citation statements)

References 47 publications

(56 reference statements)

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“…agricultural) ecosystems, but compare favourably with other coniferous forests 38 . We thus tend to attribute the model-data mismatch to the SCOPE model and to the uncertain parameterization of parameters such as the fluorescence quantum yield efficiency at photosystem level, which strongly controls SIF simulations, but can be quite variable across vegetation types and still is not fully characterized 39,40 . Other possible causes may be the heterogeneous 3D nature of the canopy, which SCOPE, being a 1D model 19 , is unable to account for and/or difficulties with the simulation of SIF in needleleaf canopies, even though Rossini, et al .…”

Section: Discussionmentioning

confidence: 99%

Sun-induced fluorescence and gross primary productivity during a heat wave

Wohlfahrt

Gerdel

Migliavacca

et al. 2018

Sci Rep

110

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Remote sensing of sun-induced chlorophyll fluorescence (SIF) has been suggested as a promising approach for probing changes in global terrestrial gross primary productivity (GPP). To date, however, most studies were conducted in situations when/where changes in both SIF and GPP were driven by large changes in the absorbed photosynthetically active radiation (APAR) and phenology. Here we quantified SIF and GPP during a short-term intense heat wave at a Mediterranean pine forest, during which changes in APAR were negligible. GPP decreased linearly during the course of the heat wave, while SIF declined slightly initially and then dropped dramatically during the peak of the heat wave, temporally coinciding with a biochemical impairment of photosynthesis inferred from the increase in the uptake ratio of carbonyl sulfide to carbon dioxide. SIF thus accounted for less than 35% of the variability in GPP and, even though it responded to the impairment of photosynthesis, appears to offer limited potential for quantitatively monitoring GPP during heat waves in the absence of large changes in APAR.

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Section: Discussionmentioning

confidence: 99%

Sun-induced fluorescence and gross primary productivity during a heat wave

Wohlfahrt

Gerdel

Migliavacca

et al. 2018

Sci Rep

110

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“…Thus, the color information of each pixel for the whole color image acquired in natural light conditions could be registered using Equations (4)- (6), which were further used for calculating color indices.…”

Section: Registration Of Color Imagesmentioning

confidence: 99%

“…Phenotyping is the measurement of phenotypic traits at spatial and temporal resolutions at the level of complex traits, such as yield, or at a detailed subtrait level for factors impacting yield [5], allowing researchers to gather information about plant architecture. Commonly measured subtraits include the geometric traits (height, canopy breadth, and volume) and physiological information (chlorophyll, nitrogen, phosphorus, and potassium contents) of crops [6], which have great scientific value for breeders and geneticists [7,8]. These phenotypic traits are essential not only for quantitative analysis of genotype-environment interactions [9,10], but also for optimizing field management activities such as cultivation, fertilization and irrigation [11,12].Geometric traits such as plant height and canopy breadth are important in investigations of plant morphology and affect plant yield and total biomass.…”

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confidence: 99%

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Zhu

Guan

et al. 2019

Remote Sensing

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Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

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“…Proximal SIF retrievals can be combined with ancillary measurements at comparable, temporal and spatial scales, to improve the interpretation and understanding of the SIF signal. This will also aid down-scaling to understand leaf level processes and the validation of remote SIF products [8][9][10]. Fifteen years ago, most of the spectroradiometers featured spectral resolutions too coarse for an absolute proximal quantification of SIF (e.g., full width half maximum (FWHM) ≥ 1 nm); and most of the retrievals were based on multispectral Fraunhofer Line Depth (FLD) methods [1,11].…”

Section: Introductionmentioning

confidence: 99%

Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers

Pacheco‐Labrador

Hueni²,

Mihai³

et al. 2019

Remote Sensing

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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, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions.

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Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion

Cited by 45 publications

References 47 publications

Sun-induced fluorescence and gross primary productivity during a heat wave

Sun-induced fluorescence and gross primary productivity during a heat wave

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers

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