###### Abstract
Drought is the most common factor that affects GPP negatively [24; 25; 26]. A number of recent studies have attributed recent declines in managed ecosystem productivity to climate change-induced droughts [27; 28; 29]. [PERSON] et al. (2020) [30] showed that drought intensity and duration are the primary factors that influence how the GPP responds to drought at large scale, and the drought duration is a key diagnostic variable for diagnosing drought-induced changes in GPP. Due to the GPP algorithm's lack of consideration for the impacts of soil moisture on photosynthesis, they may not accurately reproduce the inter-annual variations in GPP and may therefore underestimate the effects of drought on GPP [31]. For example, [PERSON] et al. [32] found that due to an inadequate representation of both human management and natural processes, current TBMs understate the effect of drought on GPP. Additionally, existing TBMs simplify soil hydrological processes and have shown large uncertainty in predicting the effects of drought on the land carbon sink [16; 33; 34; 35]. The need for deeper knowledge of the water stress mechanisms integrated in TBMs is highlighted by the significant inter-model heterogeneity [36; 37; 38]. Due to continued global warming, the frequency and severity of droughts have grown throughout the world over the past few decades and are expected to become worse [39; 40]. Therefore, in order to control and reduce the harmful effects of climate change, it is crucial to study how GPP responds to drought. Surface soil moisture is a promising proxy for realistic assessment of the drought duration-vegetation response relationship [30]. Four earth system models' outputs indicated that the long-term global terrestrial carbon uptake would be greatly affected by soil moisture [36]. Previous studies indicated that dryness stress on ecosystem output is largely dominated by soil moisture [41]. When evaluating the responses of GPP to drought at regional and global scales, the assimilation of soil moisture data in this case can contribute to obtaining a more reliable conclusion [42; 43]. The relationship between the carbon, water, and energy cycles in the physical climate system and the biogeochemical cycles is regulated by soil moisture, which is essential for vegetative processes [44]. Therefore, one of the major factors in ecosystem models that controls stomatal conductance and regulates both plant water usage and carbon uptake is soil moisture [45; 46; 47]. The modeling of various land surface processes and the responses of GPP to drought have been demonstrated to benefit from assimilation of remotely sensed surface soil moisture [42; 43; 48; 49]. Although simulaneous assimilation of atmospheric CO\({}_{2}\) concentration and satellite-derived soil moisture data has been studied in CCDAS [22; 23], they lacked analysis of how the optimized GPP responded to drought. In this study, we assimilated the globally derived long-term remote sensing soil moisture data from 1980 to 2015 (European Space Agency Climate Change Initiative (ESA-CCI) v4.4), combining the atmospheric CO\({}_{2}\) concentrations from the same period (Table A1, Scripps Institution of Oceanography (SIO) stations information) to optimize the process parameters with the underlying Biosphere Energy Transfer HYdrology (BETHY) [50] scheme of CCDAS.