The objectives of this work were (i) to evaluate pedotransfer functions (PTFs) to generate an ensemble of water flow models for the ensemble Kalman filter (EnKF) application to the assimilation of soil water content data and (ii) to research how effective assimilation of soil moisture sensor data can be in correcting simulated soil water content profiles in field soil. Data from a fi eld experiment were used in which 60 two-rod time domain reflectometry (TDR) probes were installed in a loamy soil at five depths to monitor the soil water content. The ensemble of models was developed with six PTFs for water retention and four PTFs for the saturated hydraulic conductivity (Ksat). Measurements at all five depths and at one or two depths were assimilated. Accounting for the temporal stability of water contents substantially decreased the estimated noise in data. Applicability of the Richards’ equation was confirmed by the satisfactory calibration results. In absence of calibration and data assimilation, simulations developed a strong bias caused by the overestimation of Ksat from PTFs. Assimilating measurements from a single depth provided substantial improvements at all other observation depths. An increase in data assimilation frequency improved model performance between the assimilation times.