Related Experiment Video
Updated: Nov 4, 2025

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
Published on: June 24, 2019
Linking remote sensing parameters to CO2 assimilation rates at a leaf scale
Kouki Hikosaka1, Katsuto Tsujimoto2
1Graduate School of Life Sciences, Tohoku University, Aoba, Sendai, 980-8578, Japan. hikosaka@tohoku.ac.jp.
Abstract:
Solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) are expected to be useful for remote sensing of photosynthetic activity at various spatial scales. This review discusses how chlorophyll fluorescence and PRI are related to the CO2 assimilation rate at a leaf scale. Light energy absorbed by photosystem II chlorophylls is allocated to photochemistry, fluorescence, and heat dissipation evaluated as non-photochemical quenching (NPQ). PRI is correlated with NPQ because it reflects the composition of xanthophylls, which are involved in heat dissipation. Assuming that NPQ is uniquely related to the photochemical efficiency (quantum yield of photochemistry), photochemical efficiencies can be assessed from either chlorophyll fluorescence or PRI. However, this assumption may not be held under some conditions such as low temperatures and photoinhibitory environments. Even in such cases, photosynthesis may be estimated more accurately if both chlorophyll fluorescence and PRI are determined simultaneously. To convert from photochemical efficiency to CO2 assimilation, environmental responses in stomatal conductance also need to be considered. Models linking chlorophyll fluorescence and PRI with CO2 assimilation rates will contribute to understanding and future prediction of the global carbon cycle.
Related Concept Videos
Light Acquisition
The Calvin Benson Cycle
Adaptations that Reduce Water Loss
Regulation of Transpiration by Stomata
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

