Methods to retrieve surface soil moisture were assessed at the global scale for one entire year by using simulated Soil Moisture and Ocean Salinity brightness temperatures (T B) and vegetation coverage information which can be derived from optical sensors. The global T B database consists of half-degree continental pixels and accounts for within-pixel heterogeneity, based on 1-km resolution land cover maps. The retrievals were performed by using a three-parameter inversion method applied to the L-band Microwave Emission of the Biosphere model. By using a Bayesian approach, vegetation data were injected as a priori information. Two options were investigated to profit from normalized difference vegetation index products: providing an a priori knowledge either on vegetation optical depth or on the vegetation cover fraction (f cover). The latter option allows for a better description of the surface heterogeneity by considering a bare soil fraction. When an error of 1 K is applied to the T B, both synergistic schemes significantly improved the soil moisture accuracy compared with methods using microwave data only. Using the vegetation a priori information, about 80% of the pixels present soil moisture retrieval accuracy less than 0.04 m3middotm-3 in terms of root-mean-square error, whereas methods based only on the microwave data provide 63% of pixels of the studied area with this accuracy. If the error in T B is larger (2 or 3 K), the soil moisture retrieval accuracy decreases significantly for both methods. The use of optical data to give a priori value of vegetation optical option is then the best for these cases.