An Ensemble Kalman Filter (EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using a Soil-Vegetation-Atmosphere Transfer (SVAT) model during a complete growing season of corn in Central Mexico. Synthetic and field soil moisture (SM) observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates. Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation (ASD) and the root mean square error (RMSE) by 60 % and 50 %, respectively, compared to the open-loop ASD. The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals. Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions. The results improved SM estimates up to 10-cm depth using SMAP SM retrievals; however, additional studies are needed to improve SM at deeper layers. The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas, particularly in developing countries.