Improved modeling of leaf stomatal conductance by incorporating its highly dynamic responses to varying light conditions in Mango species (Mangifera indica L.)

Zhuang, Jie; Wang, Quan; Jin, Jia

doi:10.1016/j.scienta.2024.112894

Scientia Horticulturae

2024

DOI: 10.1016/j.scienta.2024.112894

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Improved modeling of leaf stomatal conductance by incorporating its highly dynamic responses to varying light conditions in Mango species (Mangifera indica L.)

Jie Zhuang,

Quan Wang,

Jia Jin

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2024

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Coupling Light Intensity and Hyperspectral Reflectance Improve Estimations of the Actual Electron Transport Rate of Mango Leaves (Mangifera indica L.)

Jin,

Wang,

Zhuang

2024

Remote Sensing

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Real-time and accurate assessment of the photosynthetic rate is of great importance for monitoring the contribution of leaves to the global carbon cycle. The electron transport rate is a critical parameter for accurate simulation of the net photosynthetic rate, which is highly sensitive to both light conditions and the biochemical state of the leaf. Although various approaches, including hyperspectral remote sensing techniques, have been proposed so far, the actual electron transport rate is rarely quantified in real time other than being derived from the maximum electron transport (Jmax) at a reference temperature in most gas exchange models, leading to the decoupling of gas exchange characteristics from environmental drivers. This study explores the potential of using incident light intensity, hyperspectral reflectance data, and their combination for real-time quantification of the actual electron transport rate (Ja) in mango leaves. The results show that the variations in Ja could be accurately estimated using a combination of incident light intensity and leaf reflectance at 715 nm, with a ratio of performance to deviation (RPD) value of 2.12 (very good predictive performance). Furthermore, the Ja of sunlit leaves can be predicted with an RPD value of about 2.60 using light intensity and a single-band reflectance value within 760–1320 nm, while the actual electron transport rate of shaded leaves can only be predicted with a lower RPD value of 1.73 (fair performance) using light intensity and reflectance at 685 nm. These results offer valuable insights into developing non-destructive, rapid methods for real-time estimation of actual electron transport rates using hyperspectral remote sensing data and incident light conditions.

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Coupling Light Intensity and Hyperspectral Reflectance Improve Estimations of the Actual Electron Transport Rate of Mango Leaves (Mangifera indica L.)

Jin,

Wang,

Zhuang

2024

Remote Sensing

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Improved modeling of leaf stomatal conductance by incorporating its highly dynamic responses to varying light conditions in Mango species (Mangifera indica L.)

Cited by 1 publication

References 60 publications

Coupling Light Intensity and Hyperspectral Reflectance Improve Estimations of the Actual Electron Transport Rate of Mango Leaves (Mangifera indica L.)

Coupling Light Intensity and Hyperspectral Reflectance Improve Estimations of the Actual Electron Transport Rate of Mango Leaves (Mangifera indica L.)

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