The description of field soil water content time series can be affected by uncertainty due to measurement errors of the respective state variables, errors due to assumptions underlying the model, and errors in the determination of boundary conditions. In this study, a simple state-equation was applied for predicting field soil water contents at three different soil depths. The simple state-model yielded large deviations of predictions from the measured soil water content, especially for the upper soil depth. Apparently, the magnitude of the estimated evaporation rate was too high. The prediction result could significantly be improved when the calculated evaporation was reduced by a factor of 0.7. In order to account for uncertainty sources associated with this simple approach, the state-equation was combined with a stochastic technique, the so-called Kalman-Filter. Applying the Kalman-Filter, the prediction quality significantly increased, even when the erroneously high evaporation was assumed to be true. However, prediction uncertainty increased for the same time periods, for which it was shown earlier that spatial correlation of soil water status was either random or very short. When the Kalman-Filter was applied in a scenario to the surface layer only, simulated soil water content in layers 2 and 3 agreed to measurements and were highly improved compared to simulations when layer I was not filtered. Hence, application of lab determined soil hydraulic property functions in combination with state observations of upper soil horizon water content and with the Kalman-Filter provides a promising opportunity to describe and predict soil water contents for entire soil profiles even under the presence of uncertainty sources.