Exploring the Significance of Chlorophyll Fluorescence‐Based Photosynthetic Capacity in Gross Primary Productivity Simulations Across Diverse Ecosystems in China

Accurate estimation of gross primary productivity (GPP) plays a critical role in developing effective climate change policies. In addition to climatic factors, CO 2 levels, and leaf area index (LAI), GPP is also primarily regulated by the maximum rate of carboxylation ( V cmax ) in ecosystem models. However, significant uncertainties in V cmax measurements, along with its limited availability over larger geographical areas, hinder our ability to address scientific questions in context of increasing atmospheric CO 2 concentrations. Recently, solar‐induced fluorescence (SIF) signals have been used as non‐invasive way to monitor plant physiological processes. In this study, we utilized eddy covariance‐based GPP and the Soil‐Canopy Observation of Photosynthesis and Energy (SCOPE) model to infer V cmax . We aimed to establish relationships between site‐scale V cmax and far‐red solar‐induced chlorophyll fluorescence yield (SIF yield ) to estimate photosynthetic capacity across diverse ecosystems in China from 2008 to 2010. Our findings revealed a robust relationship between SIF yield and site‐level V cmax retrievals, with a coefficient of determination ( R 2 ) ranging from 0.36 to 0.74 ( p < 0.05) at biweekly (once every two weeks) intervals across all studied sites. Incorporating SIF yield ‐derived V cmax into the SCOPE model resulted in a 9% improvement in GPP simulation accuracy compare to using a constant V cmax . Additionally, integration SIF yield ‐derived V cmax into the BEPS (Boreal Ecosystem Productivity Simulator) model demonstrated strong agreement between flux‐based and simulated GPP values, further validating the accuracy of the estimated V cmax in capturing ecosystem photosynthetic capacity. This study highlights the importance of utilizing SIF yield to precisely quantify GPP estimates in the context of imminent climate change challenges.