SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Chen, Ruonan; Liu, Liangyun; Liu, Zhunqiao; Liu, Xinjie; Kim, Jongmin; Kim, Hyun Seok; Lee, Hojin; Wu, Genghong; Guo, Chenhui; Gu, Lianhong

doi:10.1016/j.agrformet.2023.109814

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Journal Article · Thu Nov 16 00:00:00 EST 2023 · Agricultural and Forest Meteorology

DOI:https://doi.org/10.1016/j.agrformet.2023.109814· OSTI ID:2441037

Chen, Ruonan ^[1]; ^[1]; ^[2]; ^[3]; Kim, Jongmin ^[4]; ^[5]; Lee, Hojin ^[6]; ^[7]; Guo, Chenhui ^[2]; ^[8]

International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China). Aerospace Information Research Institute; University of Chinese Academy of Sciences, Beijing (China)
Northwest A&F University, Shaanxi (China)
International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China). Aerospace Information Research Institute
Univ. of Virginia, Charlottesville, VA (United States)
Seoul National Univ. (Korea, Republic of); National Center for Agro Meteorology, Seoul (Korea, Republic of)
Seoul National Univ. (Korea, Republic of)
Forschungszentrum Juelich GmbH (Germany)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Φ_PSIImax), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R² = 0.86, RMSE=1.99 μmol m^-2 s^-1) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. Further, the seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Φ_PSIImax. These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in Φ_PSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: National Natural Science Foundation of China (NSFC); National Research Foundation of Korea (NRF); USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2441037

Journal Information:: Agricultural and Forest Meteorology, Journal Name: Agricultural and Forest Meteorology Vol. 344; ISSN 0168-1923

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
Electron transport rate
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GPP
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SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

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