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Remote sensing technology is an effective means of obtaining surface information quickly, nondestructively and on a large scale, and plays an important role in agricultural surveys and crop growth monitoring. The crop growth model systematically quantifies the growth and development process of crops according to crop growth patterns and environmental effects, and establishes a dynamic mathematical model that can accurately simulate the growth and development of crops at a single point scale. Data coupling effectively combines the advantages of remote sensing technology and crop growth model, and has great application potential in crop growth monitoring. Dry aerial mass (DAM) is one of the important physiological parameters in crop growth and development, which is of great significance for crop growth monitoring and yield estimation. In order to explore an accurate and efficient method for estimating the DAM of winter wheat, experiment data of winter wheat in two growing seasons of 2013—2014 and 2014—2015 were obtained. The leaf area index (LAI) remote sensing inversion model was constructed by using the 2015 experimental data, and the 2014 data was used to verify the accuracy of the inversion model, and the optimal estimation model was screened according to the modeling and verification accuracy. The results showed that the regression model constructed by NDVI705 performed the best, and the R2, RMSE and NRMSE of modeling and verification were 0.755, 0.769, 24.23% and 0.668, 0.869, and 26.96%, respectively. LAI was the coupling variable between remote sensing and simple algorithm for yield estimates (SAFY) model. By using the shuffled complex evolution with PCA (SP-UCI) algorithm, three sensitive parameters such as emergence date (D0), effective lightuse efficiency (ELUE) and sum of temperature for senescence (STT) were optimized, and then the dynamic growth simulation was performed for the whole growth period of winter wheat. The results showed that the LAI of winter wheat simulated by SAFY model showed an increasing trend in the vegetative growth stage. It was increased significantly after the returning green stage (about 160 days after sowing) and reached its maximum at the end of vegetative growth (about 200 days). Later, LAI began to decay and approached zero at the end of grain filling (about 250 days), which was highly consistent with the actual growth of winter wheat. The R2, RMSE and NRMSE of the winter wheat leaf area index simulated by the model 2014—2015 and 2013—2014 were 0.760, 0.769, 24.22% and 0.677, 0.879, 27.25%, respectively. During the whole growth period, SAFY model simulated that the winter wheat DAM had an overall upward trend, and the growth accelerated after the returning green stage, reaching the maximum growth rate at the end of vegetative growth (about 200 days), and then the growth was gradually slowed down, and the DAM growth ended at the end of grouting (about 250 days). The R2, RMSE and NRMSE of the winter wheat DAM simulated by the model in 2014—2015 and 2013—2014 were 0.887, 1.001t/hm2, 19.41% and 0856, 1033t/hm2, 19.86%, respectively. The results showed that the coupled hyperspectral remote sensing and SAFY crop growth model can accurately simulate the dynamic change of winter wheat growth, and the estimation accuracy of winter wheat DAM was high, which can provide reference for remote sensing monitoring of winter wheat growth.