Crop harvest time has an important impact on crop yield and quality. The development and wide application of remote sensing technology provides an effective method for large-area and real-time monitoring of crop growth. However, remote sensing cannot capture changes in its intrinsic mechanism characteristics. Therefore, a framework that assimilated leaf area index (LAI) derived from remote sensing data into crop growth mode was presented to predict the maturity of crops. LAI was used as the coupling variable, moderate resolution imaging spectroradiometer (MODIS) LAI was used as the remote sensing data source, meteorological data and meteorological forecast data of 2017—2018 were used as weather input of world food studies (WOFOST) crop growth model, May 1st as the predicting date. By means of shuffled complex evolution method developed by the University of Arizona (SCE-UA) algorithm, it was simulated in each pixel in the study area and retrieved the optimal parameters set of this pixel. Then the WOFOST was run by the optimal parameter set to simulate the growth and development of winter wheat and retrieve the maturity-prediction. Verified by the observation data of the agrometeorological sites in the study area, it was demonstrated that the method had substantial accuracy in predicting regional anthesis and maturity date with the root mean square error (RMSE) as 2.10d and 2.48d. The method provided a reference for the maturity prediction of other crops at a regional scale.