With the ability to monitor soil moisture in time comes the opportunity to develop ways to incorporate these measurements into predictive models, without compromising or overriding the model physics. The importance of soil moisture to the growth of crops is well understood and because of this it is recognized as one of the more important parts of crop modeling programs. This research focused on improvements to the Decision Support System for Agrotechnology Transfer - Cropping System Model (DSSAT-CSM) as determined by the accuracy of soil moisture estimates. To accomplish this, data assimilation techniques were implemented to process the uncertainty of the model estimates and in situ measurements of soil moisture. Consideration of soil parameter uncertainty, which influences model estimates of soil moisture and model output, was taken into account using a Monte Carlo approach. A Kalman filter was used to combine the model estimates of soil moisture with in situ soil moisture measurements, while varying several important soil parameters in the model using a Monte Carlo approach. Covariances for the Kalman filter were calculated for the model and measurements based on the model's standard deviation from the Monte Carlo soil moisture estimates and the standard deviation of the in situ soil moisture measurements. Data for this study was obtained from a research study conducted on irrigated wheat during the winters of 2003-04 and 2004-05 in Maricopa, Arizona, in which thorough field and crop data were collected. Results of the simulations were compared against biomass and yield measurements to determine the effectiveness of the data assimilation scheme. The Monte Carlo approach with assimilation done in the top layer of the soil profile was only able to moderately address uncertainty present in the soil parameters. Improvement resulted for data assimilation of soil moisture through the reduction of the error between the measured and simulated grain yield and canopy weight for 47% and 37% of the simulations for the 2003-2004 and for 25% and 32% of the simulations for the 2004-2005 season, respectively. Assimilation was more effective for improving the model output of grain yield for the 2004-2005 than the 2003-2004 season and canopy weight for the 2003-2004 season than the 2004-2205 season. The results of model estimated daily NO3 levels in the soil layers from data assimilation simulations indicates that assimilation of soil moisture can influence its levels. The data assimilation combined with a Monte Carlo approach showed the use of remotely sensed soil moisture could lead to improvements of frequently studied model outputs, such as grain yield and canopy weight. Further study is needed to fully understand the most desirable conditions for soil moisture assimilation and what other influencing effects data assimilation of soil moisture presents.