## 4 Discussion
### Uncertainty and Limitations  
Due to the varying drought sensitivities of different types of terrestrial ecosystems as well as the interactions between drought and other environmental variables, judgments on regional and global GPP responses to drought are highly uncertain [97]. For instance, satellite-based GPP models do not take into account the interconnected physiological and ecological processes and therefore tend to neglect the increased consequences of ecosystem responses during extreme climatic events [98]. The flux-tower-based FluxCom GPP model, which is forced by machine learning algorithms, may be considered as in situ GPP estimates and thus is widely used to estimate GPPs from multi-decadal space-based datasets and process-based models. However, it provides a poor representation of certain fundamental processes including nitrogen deposition and CO\({}_{2}\) fertilization, making it difficult to detect GPP annual changes adequately [56; 99]. While the LUE-based model often provides inconsistent long-term trends in GPP due to the absence of key critical processes such as the direct effects of soil moisture and CO\({}_{2}\) fertilization on LUE [100; 101] it recently was improved by including CO\({}_{2}\) effects on LUE photosynthesis (the revised EC-LUE) [23]. However, the revised EC-LUE model did not account for the effect of the soil nitrogen content on photosynthesis in the model. This aspect is critical given that atmospheric nitrogen deposition has increased dramatically in recent decades as a result of immmoderate fossil fuel combustion in the transportation and industrial sectors as well as the misuse of nitrogenous fertilizer in agricultural practices [102].  
In this context, [PERSON] et al. [100] revealed that GPP estimates based on remote sensing underestimate the impacts of drought on GPP due to insufficient parameterization in the model such as accounting for the possible impact of soil moisture on the LUE. Some studies have shown that the VPM [49; 103] and modified VPM [104] outperformed other GPP estimates in detecting the effect of droughs on GPP due to the inclusion of the impact of soil moisture on photosynthesis in the models, which was presented in our study and several other studies [105; 106]. As a result, water-deficit variables (e.g., LSWI in the VPM) that reflect plant ecological and physiological characteristics rather than atmospheric moisture (e.g., the VPD parameter included in the revised EC-LUE) should be considered further in GPP estimates, particularly when used during drought events [106]. Furthermore, fPAR, EVI, NDVI, and LAI remote sensing data may have limitations that contribute to the excess variance in trained variables and result in a poor output in model-parameter adjustment, thus resulting in significant uncertainty in the simulated GPP [107; 108]. In general, we selected the SDEI as an influencing factor that reflected the climatic impacts on GPP; however, human interventions also can have an impact on the GPP. As such, a method to quantify the contribution of each influencing factor and extract information that can fully reflect the effects of human interventions will be another focal point of our future work. Furthermore, in terms of discrepancies in the results reported by different GPP products, more attention could be paid to investigating the causes of these discrepancies and determining their sources to reduce uncertainty. However, these findings provided a significant incentive to better comprehend the spatio-temporal changes in terrestrial GPP to meet the increasing requests of global climatic, environmental, and ecological research.