Technical Abstract: We develop a robust understanding of the effects of assimilating remote sensing observations of leaf area index and soil moisture (in the top 5 cm) on DSSAT-CSM CropSim-Ceres wheat yield estimates. Synthetic observing system simulation experiments compare the abilities of the Ensemble Kalman Filter (EnKF) and the Sequential Importance Resampling Filter (SIRF) to attenuate the effects of various isolated sources of modeling uncertainty including weather, soil parameters and initial conditions, cultivar parameters, and model state updating equations. Results indicate that data assimilation is effective at improving yield estimates when crops are water stressed and uncertainty is confined to soil parameters, weather, and states – and not due to cultivar parameters. The EnKF is more effective than the SIRF at assimilating soil moisture observations when soil parameters and weather drive uncertainty. The SIRF is better at assimilating observations of leaf area index and is effective when uncertainty is due to vegetation related components such as weather, perturbations to state updating equations including development stage transition timing state variables. Neither filter offers significant improvement to yield estimates when crops are not water stressed or when there is uncertainty in cultivar parameters.