This study focused on the production of 18-year global root zone soil moisture (RZSM) by the joint land surface data assimilation using the satellite observed leaf area index (LAI) and surface soil moisture (SSM). The impact of the assimilation on RZSM, LAI, and other key surface variables was also assessed. The multilayer diffusion scheme, biomass and CO2 interactive scheme, and the simplified extended Kalman filter were applied in the model. It was found that the assimilation could effectively reduce the biases in LAI, and that the diverse regional effects on RZSM were varied with seasons, soil wetness, error covariance in the assimilation, and water transfer in the model. A downward increase of the RZSM pattern (< similar to 0.03 m(3) m(-3)) was found in vegetated regions with low to moderate soil wetness because of the reduced LAI by the assimilation. A general upward change of RZSM (within similar to +0.01 m(3) m(-3)) was found in dry desert regions due to the assimilation of SSM. The evaluation for the central South America shows that the assimilation improved the correlation for SSM (0.9 to 0.91) and significantly reduced the mean biases of LAI (similar to 40%). Positive impacts on day/night land surface temperature (LST) were identified to be mostly through the RZSM and LST coupling, with the improvements in the range of +1 or 2 K. The slight adverse impact of LAI over the Amazon forests had no degradations to RZSM and LST. The assessment of the impact on water, energy, and carbon cycles over France revealed that the strongest/weakest change was found in LAI (-6.3%)/deep layer soil water index (0.03%). Ecosystem respiration, sensible heat, and evapotranspiration had relatively large changes. The underlying mechanism of the impact supports the global analysis results, indicating that the joint assimilation is beneficial for drought monitoring and heatwave detection.