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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Research » Publications at this Location » Publication #389271
Research Project: Experimentally Assessing and Modeling the Impact of Climate and Management on the Resiliency of Crop-Weed-Soil Agro-Ecosystems

Location: Adaptive Cropping Systems Laboratory

Title: The physiological basis for estimating photosynthesis from Chl a fluorescence

Author
item HAN, JIMEI - Cornell University
item Chang, Christine
item GU, LIANHONG - Oak Ridge National Laboratory
item ZHANG, YONG-JIANG - University Of Maine
item MEEKER, ELIOT - University Of California, Davis
item MAGNEY, TROY - University Of California, Davis
item WALKER, ANTHONY - Oak Ridge National Laboratory
item WEN, JIAMING - Cornell University
item KIRA, OZ - Cornell University
item MCNAULL, SARAH - Cornell University
item SUN, YING - Cornell University

Submitted to: New Phytologist
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/9/2022
Publication Date: 2/18/2022
Citation: Han, J., Chang, C.Y., Gu, L., Zhang, Y., Meeker, E.W., Magney, T.S., Walker, A.P., Wen, J., Kira, O., Mcnaull, S., Sun, Y. 2022. The physiological basis for estimating photosynthesis from Chl a fluorescence. New Phytologist. 234; 1206-1219. https://doi.org/10.1111/nph.18045.
DOI: https://doi.org/10.1111/nph.18045

Interpretive Summary: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing tool with wide ranging potential for estimating photosynthesis, an indicator of plant status, from leaf to field and regional scales. In this study we built a model framework to directly connect SIF to photosynthesis and tested the model using 28 species representing major biomes around the world. This effort lays the groundwork for improving estimation of photosynthesis by taking advantage of the unique biological information contained in SIF. This study will be advantageous for future global carbon and crop monitoring efforts that can benefit both researchers and farmers.

Technical Abstract: • The availability of remotely sensed Solar-Induced chlorophyll Fluorescence (SIF) offers the potential to curb large uncertainties in estimating photosynthesis across biomes, climates, and scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. • This study built a quantitative framework to estimate photosynthesis, based on a mechanistic light reaction (MLR) model with SIF as an observational input (denoted as MLR-SIF). Utilizing 28 C3 and C4 plant species native to diverse climates and representative of major plant biomes across the globe, we verified such a framework at the leaf level. • MLR-SIF is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of MLR-SIF using Monte Carlo simulations, and found that the estimated photosynthesis is much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of additional independent information contained in SIF. • SIF provides “parameter savings” to the MLR-SIF as compared to the mechanistically equivalent FvCB and thus shortcuts the uncertainties propagated from imperfect model parameterization. Our findings set the stage for future efforts employing SIF mechanistically to improve photosynthesis estimation across scales, functional groups and environmental conditions.