Developing and evaluating satellite-derived phenology and physiology indicators for modeling annual gross primary productivity variability

Vegetation annual gross primary production (AGPP), the total yearly carbon assimilation via photosynthesis, serves as a key indicator of ecosystem carbon uptake. While AGPP variations are jointly influenced by both vegetation phenology and physiology, the effectiveness of satellite-derived indicators in capturing these variations has not been fully evaluated. This study develops and evaluates the satellite-derived phenology and physiology indicators for modeling AGPP variability. We assessed the performance of satellite-derived metrics, including solar-induced chlorophyll fluorescence (SIF), leaf area index (LAI), and enhanced vegetation index (EVI), in capturing AGPP variations. Among these, SIF-based indicators exhibited the highest accuracy (Pearson's r ​= ​0.79; root mean square error ​= ​414.7 gC·m⁻²·year⁻¹), outperforming LAI- and EVI-based indicators. To further investigate the mechanisms driving AGPP variability, we used a structural equation model based on in situ observations to quantify the direct and indirect effects of climate on AGPP through phenology and physiology. Our results reveal that vegetation physiology, particularly the seasonal maximum gross primary production, plays a more dominant role in regulating AGPP than phenology. Furthermore, we found that globally, SIF-derived phenology indicators tend to be lower than those from LAI and EVI, whereas SIF-derived physiology indicators are elevated in tropical regions and the Southern Hemisphere. These findings highlight the potential of satellite-derived indicators in advancing AGPP modeling and emphasize the predominant role of vegetation physiology in regulating ecosystem carbon uptake. This study contributes to a refined understanding of global carbon cycle dynamics and provides insights for improving large-scale carbon assessments in the context of climate change.