###### Abstract  
Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO\({}_{2}\) concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m\({}^{2}\)/month and 10.84 gC/m\({}^{2}\)/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO\({}_{2}\) concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO\({}_{2}\) growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (\(\leq\)9 months drought durations) and large-scale (e.g., regional to continental scales) droughs on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere's carbon cycle and for illustrating how GPP responds to drought at a continental scale. g 2023 15 676 10.3390/15/030676 10.3390/15/030676 10.390/15/030676 10.  
accurate estimates of photosynthesis and vegetation primary production across large spatial scales are necessary for understanding the drivers of the terrestrial carbon (C) cycle. At regional or a larger scale, GPP is usually estimated by remote sensing [11; 12], process-based models (i.e., terrestrial biosphere models (TBMs)) [13; 14], or flux tower observation-based upscaling methods [15]. A useful tool for researching the local, regional, continental, and global levels of ecosystem GPP and understanding the response of terrestrial ecosystems to droughts are process-based TBMs [14; 16]. Process-based models, which are based on more explicit mechanisms than remote sensing and machine learning GPP models, can be used to simulate historical GPP and forecast future GPP in response to environmental changes. However, process-based models that simulate GPP highly depend on their inputs, parameters, and structures as well as show notable uncertainty and thereby variations amongst models [17; 18]. One commonly used technique to reduce uncertainty in process-based TBMs is data assimilation. The main application of such data assimilation approach is focused on constraining model parameters with observations such as atmospheric CO\({}_{2}\) concentrations [19]. With the use of the Carbon Cycle Data Assimilation System (CCDAS), [PERSON] et al. (2005) [19] demonstrated that through assimilated atmospheric CO\({}_{2}\) data, parameter uncertainties can be reduced significantly. CCDAS is now able to assimilate Earth observations (EO) data such as Fraction of Absorbed Photosthetically Active Radiation (FAPAR) data [20]; in addition, it has achieved the ability to assimilate both atmospheric CO\({}_{2}\) observations and FAPAR simultaneously [21], as well as the combination of in situ CO\({}_{2}\) concentration and soil moisture from passive microwave observations [22; 23] to constrain the parameters related to terrestrial carbon and the water cycle.