Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/15/2000
Publication Date: 10/2/2000
Citation: N/A Interpretive Summary: The ability to track light-use efficiency (LUE) of plant photosynthesis (the amount of photosynthesis per incident photosynthetically active photons), as plants grow or maintain themselves, would be useful for monitoring the health of individual production fields or entire ecosystems. The photosynthetic reflectance index (PRI), which is calculated from reflectance at 529 nm and 569 nm, has been found to be correlated with LUE of individual leaves. This study investigated the relationship between PRI and LUE of pine, spruce, and aspen forest stands. Strong correlations were found when all of the species were either grouped together or separated into deciduous and conifer species. The study suggests that LUE of individual leaves can be inferred from canopy-level measurements. This will be of interest to individual producers managing crops and strategic decision makers charged with monitoring large crop areas and natural vegetation for signs of stress.
Technical Abstract: Remote measurement of light-use efficiency (LUE) of photosynthesis would be beneficial for monitoring the status of natural and agronomic plants. The photosynthetic reflective index (PRI) has been formulated to do this for leaves by using reflected light at approximately 529 nm and 569 nm. This study tested the relationship between PRI and LUE of a forest canopy. Measurements of the PRI from a helicopter-based sensor system were compare with light-use efficiency calculated from eddy covariance measurements of CO2 fluxes from spruce, pine, aspen, and incident photosynthetic photon flux density. Significant correlations were found when all of the species were grouped together and when they were separated into conifers and deciduous. The study suggests that relative photosynthectic rates may be derived from remotely-sensed reflectance measurements. These findings will be of interest to government agencies tracking regional and global vegetation changes from long-term stress. It may also be of use to producers seeking a method to better quantify plant stress for management decisions.