Improved Estimation of Terrestrial Gross Primary Production Using a Mechanistic Light Reaction Model

Accurate quantification of global terrestrial gross primary production (GPP) is critical for understanding the carbon cycle, yet significant discrepancies persist in current estimates regarding their magnitude and spatiotemporal patterns. Solar-induced chlorophyll fluorescence (SIF) has emerged as a promising proxy for GPP; recent mechanistic light reaction (MLR) theories have successfully elucidated the mechanistic SIF-GPP link, while their applicability at the global scale remains unclear. Here, we propose an improved mechanistic light reaction model, qMLR, designed to apply the mechanistic SIF-GPP relationship for global GPP estimation. Building upon the original leaf-scale MLR theory, the model integrates GOSIF with flux tower-based parameter calibration; by implementing a climate-zone-specific calibration strategy based on the Köppen-Geiger system and employing Genetic Algorithms and Bayesian Optimization, we precisely characterized the nonlinear responses of maximum quantum yield of photochemistry (Φ_PSIImax) and the fraction of open PSII reaction centers (q_L) to environmental gradients, factors previously unaccounted for in global SIF-based GPP estimations. This approach generated a global 0.1° monthly GPP product for the period of 2004-2024. Validation against 425 eddy covariance sites demonstrates that qMLR matches or outperforms existing benchmark products in overall accuracy (R²=0.70), with a regression slope (0.93) closer to unity. The model's mechanistic framework corrects the systematic underestimation prevalent in traditional (e.g., FLUXCOM GPP and MODIS GPP) models over tropical regions: across 7 tropical forest validation sites, qMLR achieved a mean bias of -3.29%, markedly outperforming other mainstream products (mean bias of -28.21%). Our results reveal a global multi-year average GPP of approximately 152.03 ± 4.42 PgC yr^-1, higher than the conventional estimate of ~120 PgC yr^-1, and show an increasing trend of 0.642 PgC yr^-2. This study successfully brings MLR model to global scale and provides a long-term global GPP dataset based on MLR model for the first time, highlights the central role of tropical forests in the global carbon cycle, and offers a new physical benchmark for accurately assessing global vegetation productivity.