The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature(Tb) simulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging(BMA) ensemble approach. The simulations by the community microwave emission model(CMEM) coupled with the community land model version 4.5(CLM4.5) over mainland China are conducted by the 24 configurations from four vegetation opacity parameterizations(VOPs),three soil dielectric constant parameterizations(SDCPs), and two soil roughness parameterizations(SRPs). Compared with the simple arithmetical averaging(SAA) method,the BMA reconstructions have a higher spatial correlation coefficient(larger than 0.99)than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system(AMSR-E) at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference(RMSD) of 4 K and a temporal correlation coefficient of 0.64. The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature (Tb) simulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model The simulations by the community microwave emission model (CMEM) coupled with the community land model version 4.5 (CLM4.5) over mainland China are conducted by the 24 configurations from four vegetation opacity parameterizations (VOPs), three Compared with the simple arithmetical averaging (SAA) method, the BMA reconstructions have a higher spatial correlation coefficient (larger than 0.99) than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system (AMSR-E) at the vertical polarization. Moreover, the BM A product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference (RMSD) of 4 K and a temporal correlation coefficient of 0.64.