## 4 Discussion
### Multi-Source Data and Methods Reveal a More Comprehensive Picture in China's Terrestrial Ecosystems  
Modeling and remote sensing data are commonly used to investigate ecosystem GPP dynamics across various spatial and temporal scales. However, different GPPs are prioritized differently in estimation and monitoring [41]. Terrestrial ecosystem models driven by meteorological factors are frequently employed for estimating terrestrial ecosystem GPP. While these models can accurately simulate terrestrial ecosystem dynamics and their interactions with other Earth systems, they are susceptible to uncertainties stemming from the model's sensitivity to meteorological parameters and the uncertainty associated with meteorological datasets [42]. Furthermore, terrestrial ecosystem models are based on the current academic knowledge of the processes of the global carbon cycle and may have some limitations, which in turn lead to uncertainty in the model results [43]. Remote sensing GPP products, on the other hand, rely on a globally parameterized GPP model and are derived from long time series of remote sensing data, ultimately producing remotely sensed products. However, these remote sensing data are prone to atmospheric interference and may produce misleading signals under vegetation stress conditions, such as green canopies that are not actively photosynthesizing [44]. Furthermore, satellite remote sensing data can only offer momentary spectral information, and the GPP derived from sensor photography and data processing remains inadequate. Previous studies have shown that choosing only model data or remote sensing data, but not both, to analyze vegetation dynamics can lead to controversial conclusions. There are significant uncertainties among different datasets, and even within the same model comparison program, GPP values can vary greatly between different models. In addition, analyzing only spatial trends in GPP across grids and ignoring trend changes in annual average GPP may lead to some uncertainty in the results of the study. Therefore, we suggest utilizing multivariate data, including both models and remote sensing, to analyze the spatial and temporal dynamics of past, present, and future GPP across China's terrestrial ecosystems, aiming to comprehensively assess the carbon balance dynamics. We also recommend that a combination of annual average GPP and spatially gridded GPP trend analyses should be used to jointly characterize the changing state of vegetation GPP in China's terrestrial ecosystems. By avoiding reliance on a single data source and method, which could potentially yield misleading conclusions, this paper aims to offer a more comprehensive and realistic portrayal of productivity changes in China's terrestrial ecosystems. The Physiological Role of Plant Should Be Given More Attention to Better Explain Inter-Annual Variability in GPP  
Previous research indicates that annual changes in GPP result from various biotic and abiotic factors, primarily alterations in plant phenology and physiological processes [45; 46]. In this study, through partial correlation analyses of plant phenology and peak photosynthetic productivity with interannual GPP changes, we ascertained that plant physiology outweighs phenology in determining GPP's long-term trend. The importance of plant phenology on seasonal and annual GPP variability has been shown in many studies [45; 47; 48], Seasonal phenological events, such as leaf-out and senescence, directly influence the length of the growing season and hence the period during which photosynthesis can occur. However, phenological changes cannot explain GPP reduction caused by climate extreme events, which account for the majority of global inter-annual variability in GPP [49]. For instance, extreme droughs or heatwaves can lead to immediate reductions in photosynthetic activity due to stomatal closure and other stress responses, regardless of phenological stage.