The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficienciesA review and meta-analysis

Garbulsky, Martín F.; Peñuelas, Josep; Gamon, John A.; Inoue, Yoshio; Filella, Iolanda

doi:10.1016/j.rse.2010.08.023

Cited by 537 publications

(547 citation statements)

References 141 publications

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“…Data that were registered by satellites were not used in the analysis. It should be noted that leaves and canopy levels are widely used scales of PRI measurements [15,61]. Measurements of PRI in leaves are often based on the application of spectrometers and specific systems of PRI measurement (e.g., PlantPen PRI 200) or systems of PRI imaging [15,61].…”

Section: Main Principles Of Data Analysismentioning

confidence: 99%

“…It should be noted that leaves and canopy levels are widely used scales of PRI measurements [15,61]. Measurements of PRI in leaves are often based on the application of spectrometers and specific systems of PRI measurement (e.g., PlantPen PRI 200) or systems of PRI imaging [15,61]. Measurements of PRI in the canopy of leaves (from single plant or group of plants) can be also based on the application of spectrometers or multispectral and hyperspectral cameras [15,61], which can be placed on a mobile platform (e.g., drone) or fixed at a certain distance from the canopy [35,38].…”

Section: Main Principles Of Data Analysismentioning

confidence: 99%

“…Measurements of PRI in leaves are often based on the application of spectrometers and specific systems of PRI measurement (e.g., PlantPen PRI 200) or systems of PRI imaging [15,61]. Measurements of PRI in the canopy of leaves (from single plant or group of plants) can be also based on the application of spectrometers or multispectral and hyperspectral cameras [15,61], which can be placed on a mobile platform (e.g., drone) or fixed at a certain distance from the canopy [35,38]. Photosynthetic parameters in leaves (in particular, ∆F/Fm', NPQ, and LUE) can be measured by standard methods, including PAM-fluorometry [16,17] and analysis of CO 2 exchange [21][22][23][24][25].…”

Section: Main Principles Of Data Analysismentioning

confidence: 99%

“…A meta-analysis of literature data seems to be an effective method for finding a solution to this problem. There are several works [15,61] that are devoted to the meta-analysis of results of PRI measurements. In particular, these works investigated the influence of different spatial scales (leaves, canopy, or ecosystem) and time scales (daily or seasonal) of PRI measurements on photosynthetic parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, these works investigated the influence of different spatial scales (leaves, canopy, or ecosystem) and time scales (daily or seasonal) of PRI measurements on photosynthetic parameters. A determination coefficient (R 2 ) was used in the works [15,61] as the quantitative criterion for the description of the relationship between physiological processes and the photochemical reflectance index. However, these studies, which were the basis of the meta-analysis, used different regression curves (e.g., linear, logarithmic, or exponential functions), making their comparison with using R 2 more difficult.…”

Section: Introductionmentioning

confidence: 99%

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Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Sukhova

Sukhov

2018

Remote Sensing

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Abstract:The development of spectral methods of remote sensing, including measurement of a photochemical reflectance index (PRI), is a prospective trend in precision agriculture. There are many works which have investigated the connection between photosynthetic parameters and PRI; however, their results varied and were sometimes contradictory. For this paper, we performed a meta-analysis of works in this field. Here, only linear correlations of PRI with photosynthetic parameters-including quantum yield of photosystem II (∆F/Fm'), nonphotochemical quenching of chlorophyll fluorescence (NPQ), and light use efficiency (LUE)-were investigated. First, it was shown that the correlations were dependent on conditions of PRI measurements (leaf or canopy; artificial light or sunlight). Second, it was shown that a minimal level of the photosynthetic stress, and the variation of this level among investigated plants, can influence the linear correlation of PRI with ∆F/Fm' and NPQ; the effect was dependent on conditions of measurements. In contrast, the distribution of LUE among plants did not influence its correlation with PRI. Thus, the meta-analysis shows that the distribution of photosynthetic parameters among investigated plants can be an important factor that influences the efficiency of remote sensing on the basis of the PRI measurement.

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Section: Main Principles Of Data Analysismentioning

confidence: 99%

Section: Main Principles Of Data Analysismentioning

confidence: 99%

Section: Main Principles Of Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Sukhova

Sukhov

2018

Remote Sensing

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Effect of environmental conditions on the relationship between solar‐induced fluorescence and gross primary productivity at an OzFlux grassland site

et al. 2017

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Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar‐induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory‐2 (OCO‐2) has enabled fine‐scale (1.3 by 2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO‐2 SIF with tower GPP over the course of a growing season at a well‐characterized natural grassland site. Combining OCO‐2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO‐2 SIF to constrain the estimates of Vcmax, one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall, the linear relationship is more robust at ecosystem scale than the theory based on leaf‐level processes might suggest. Our study also shows that the ability of SIF to constrain Vcmax is weak at the selected site.

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Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data

Lassalle¹,

Fabre²

2022

Advances in Remote Sensing for Forest Monitoring

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The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficienciesA review and meta-analysis

Cited by 537 publications

References 141 publications

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Effect of environmental conditions on the relationship between solar‐induced fluorescence and gross primary productivity at an OzFlux grassland site

Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data

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