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

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 (Phi(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 R-2 = 0.86, RMSE=1.99 mu mol 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. The seasonal variation in the SIF-J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Phi(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 Phi(PSIImax) under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

Automated summary

Solar-induced chlorophyll fluorescence (SIF) has the potential to estimate gross primary production (GPP), but the quantitative relationship between them is not constant. In this study, a mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) was developed, considering the seasonal variation in a key parameter of the model. The GPP estimates from this model were more accurate compared to other benchmark models, especially in extreme conditions.