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

Abstract

Accurate estimation of gross primary productivity (GPP) plays a critical role in developing effective climate change policies. In addition to climatic factors, CO₂ 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 CO₂ 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 (R²) 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.