June 24, 2021
Rapidly Predicting the Photosynthetic Capacity of Trees
Leaf reflectance spectroscopy can rapidly and accurately estimate photosynthetic properties of leaves across a wide range of species and growth conditions.
Computer models used to simulate vegetation under different environmental conditions require detailed information on the properties of leaves that regulate photosynthesis. Traditional collection methods for these properties are slow and expensive. On the other hand, measuring the light reflected from leaves allows scientists to non-destructively infer these photosynthetic properties. Also, these more rapid and robust remote sensing approaches allow researchers to collect substantially more data than traditional methods. By leveraging these tools, scientists can provide improved information for models.
Climate change impacts global vegetation and threatens the health of temperate and tropical forests. Traditional methods for studying tree photosynthesis are slow and expensive, limiting the amount of usable information for investigating forest responses to climate change. To address this major challenge, scientists have developed new remote sensing methods that allow for more rapid estimation of photosynthesis. These methods provide significantly more data for the same amount of time used with traditional approaches and will help to better inform models.
Remote sensing approaches, from leaf to whole-landscape scales, can fill critical observation gaps in scientific understanding of global vegetation. Using spectrometer instruments, scientists can quickly measure light reflected from leaves and infer the underlying photosynthetic properties needed to investigate vegetation responses to climate change. This study leveraged spectrometers to illustrate how scientists can develop simple, general approaches for examining photosynthesis across a wide variety of trees. It also revealed how spectrometers were more effective than other alternative approaches currently used by researchers. By continuing to improve and use reflectance measurements, scientists will be able to obtain the information needed to make models better at predicting how plants will change in the future.
Brookhaven National Laboratory
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
This project was supported by the National Natural Science Foundation of China (nos. 31922090, 31901086), the Research Grants Council Early Career Scheme (no. 27306020) and Seed Fund for Basic Research (no. 201905159005). It was also partially supported by the Division of Ecology and Biodiversity PDF research award (awarded to Zhengbing Yan). Finally, the Next-Generation Ecosystem Experiments (NGEE–Tropics) project, supported by the Office of Biological and Environmental Research (BER) in the Department of Energy’s (DOE) Office of Science. DOE’s contract (no. DE-SC0012704) to Brookhaven National Laboratory also helped fund this project.
Yan, Z., et al. "Spectroscopy Outperforms Leaf Trait Relationships for Predicting Photosynthetic Capacity across Different Forest Types." New Phytologist 232 (1), 134–147 (2021). https://doi.org/10.1111/nph.17579.