The atmospheric state varies significantly in terms of the temporal and spatial scales. The atmospheric correction of remote sensing satellite images is limited because it is difficult to dynamically obtain atmospheric parameters matching with the images to be corrected. As the civil optical remote sensing satellite with the highest spatial resolution in China, the Gao Fen Duo Mo satellite is equipped with the first civilian Synchronization Monitoring Atmospheric Corrector (SMAC). The SMAC onboard the GFDM satellite platform is capable of multispectral and polarization detection and can offer time-synchronized, and field-of-view overlapped atmospheric measurements to obtain atmospheric parameters synchronized with the main sensor. This study proposed a synchronous atmospheric correction method for high-spatial resolution image based on the atmospheric parameters retrieved from SMAC. Firstly, based on the principle of time synchronization, the original data of SMAC was processed to form the SMAC-Level1 product, combining with the auxiliary data of the main camera. Then, according to the SMAC-Level1 data, the SMAC pixels covered with cloud were discriminated, and the aerosol and water vapor parameters of the pixels without cloud coverage were retrieved to form the SMAC-L2 product. Finally, based on the 6SV radiative transfer model, the atmospheric radiometric correction and proximity effect correction were carried out on the remote sensing image from the GFDM satellite (Level1), and the surface reflectance product of the main camera (Level2) was obtained. In the experimental part, Syn-AC was applied to the remote sensing image from the GFDM satellite, and the image quality before and after the atmospheric correction was evaluated. Furthermore, the surface reflectance after the correction was compared with the ground-measured value to discuss the accuracy of the synchronous atmospheric correction method. In addition, the classical correction method FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method.The results show that the reflectance obtained from the corrected image of Syn-AC is in good agreement with the ground-measured value, and the mean absolute error is 0.012 2 (the mean absolute error of FLAASH is 0.027 4). The atmospheric correction method based on synchronous atmospheric parameters retrieved from SMAC has great potential in improving satellite image quality and remote sensing quantitative applications.