The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in various of photosynthetic capacity and productivity potential of crops. The great progress of quantitative remote sensing and various data products make FPAR products widely used in carbon cycle and vegetation research in different regional scales. In order to explore the FPAR estimation capability based on the GF-1 wide field view (GF-1 WFV), the canopy spectral reflectance and FPAR of summer maize were measured from regional field and field plot experiments, including five nitrogen fertilization levels and two summer maize varieties from jointing to maturity stage. Firstly, the multi-spectral broadband reflectance was simulated by using the measured canopy hyperspectral reflectance based on spectral response functions of GF-1 satellite with a spatial resolution, and then the vegetation index was established by this simulated reflectance. Secondly, totally 17 vegetation indices were selected on the basis of previous studies, and the quantitative relationship between FPAR and vegetation indices at different growth stages was analyzed. Thirdly, the vegetation indices with high correlation coefficient and extremely significant correlation with FPAR were selected, and estimation models of summer maize FPAR by a linear regression model or multiple stepwise regression model respectively, by analyzing determination coefficient (R2), standard error (SE), root mean squard error (RMSE) and relative error (RE) of the estimation model and validation model, the optimal model for FPAR estimation was screened. Finally, the optimal estimation models were used to estimate the FPAR dynamic variation and spatial distribution for summer maize from jointing to maturity stage by GF-1 satellite data. The results showed that the correlation coefficient (|R|) between simulated broadband spectral reflectance and GF-1 spectral reflectance was 0.967~0.985, and the determination coefficient (R2) was 0.935~0.969, it showed that these was highly consistent between simulated spectral reflectance and GF-1 spectral reflectance. There was a good correlation between FPAR and the vegetation index constructed based on simulated reflectance, and the correlation coefficient of 3-band vegetation indexes was better than 2-band vegetation indexes, in particular, it was extremely significant (P<0.01) between FPAR and enhanced vegetation index (EVI), modified soil adjusted vegetation index 2 (MTVI2), visible atmospherically resistant index (VARI), TCARI/OSAVI, and |R| was 0.813~0.925. The simple linear regression model and multiple stepwise regression model of FPAR were established by EVI, MTVI2, VARI, TCARI/OSAVI, and the coefficient of determination (R2) of estimation model was 0.762~0.843, the coefficient of determination (R2) of validation model was 0.839~0.880, and the relative error (RE) was 7.037%~9.571%, it showed that the multiple stepwise regression model was better than simple linear regression model, and the multiple stepwise regression model could better estimate FPAR at different growth stages. The optimal model was used to estimate the spatial distribution and dynamics of FPAR at regional scale, and the measured values were validated. The R2 between the estimated and measured values was 0.819~0.856, and the relative error (RE) ranged was 8.41%~13.37%. These results indicated that the spatial distribution and dynamic variation of FPAR estimated based on simulated GF-1 WFV of hyperspectral reflectance were consistent with the actual spatial distribution, which provided a scientific basis for estimating regional FPAR and production potential of maize based on high resolution remote sensing data of GF-1 WFV.