Most process-based crop models employ an approach that simulates crop growth based on two key parameters: the fraction of photosynthetically active radiation (FPAR) and crop leaf area index (LAI). The biases on both FPAR and LAI estimations are often critical to the success of crop yield simulations. Therefore, we hypothesize that incorporation of real-time-observed FPAR and LAI data into a physical crop model would improve the yield simulation accuracy, especially under unusual growth circumstances. The global products of eight-day FPAR and vegetation LAI dataset derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements are now available by the MODIS land science team. This provides us a great opportunity to test our hypothesis by developing a hybrid crop model that combines both physical understanding and remote sensing observations. An existing crop model, EPIC, was modified to be able to incorporate the MODIS FPAR and LAI data as part of inputs. Ground measurements of LAI and yields will be conducted starting 2005 growing season on multiple corn fields in Sangamon County, IL, for model validations. The model will also run with multiple scale soil and weather datasets to examine the extend of applications of such a remote sensing driven model in the light of the availability of other commonly required input data.