This research successfully demonstrates a method for integrating remotely sensed data and a crop simulation for monitoring crop growth and yields of spring wheat at the county and state level in North Dakota. In the first phase of this research, Landsat data were used to assess spring wheat yields for three counties, and the model simulations were adjusted to produce yield that matched with farmer reported yields in the counties. The three optimum periods when remote sensing data were most effective in adjusting the model simulations are during the early vegetative phase, flowering, and senescence. The availability of cloud-free satellite data during the critical periods generally dictates the optimum situation for model calibration. The success in using Landsat data was encouraging for extending this technique using the lower resolution AVHRR data for crop yield assessment in North Dakota