This paper proposes a time-series calibration method based on Kalman filtering to monitor the changes in the on-orbit radiometric performance of satellite sensors, increase the frequency and accuracy of calibration, and meet the application requirements of quantitative remote sensing in the new era. Taking the Dunhuang radiation calibration field as the test field, we obtain the multi-scene images of the GF1 wide field of view (GF1-WFV) sensor in the sites of Dunhuang from 2013 to 2020. First, the gray values of the images are normalized, and the standard deviation is used to eliminate outliers; thus, valid images are filtered out. Then, the time-series calibration method based on stable targets with historical data is employed to calculate the calibration coefficients, and the idea of Kalman filtering is applied to further process the calibration coefficients. Finally, the calibration results in this paper are compared with the calibration results of the China Centre for Resources Satellite Data and Application (CCRS) and Landsat8 calibration results separately. The results reveal that the maximum annual attenuation rate of the GF1 sensor during the on-orbit operation period does not exceed 0.3%, which indicates that the sensor has not significantly attenuated. Through the comparison and verification of the calibration results, we find that the calibration results obtained by the time-series calibration method based on Kalman filtering are more accurate than those of CCRS, and the maximum reduction in the average relative error of each band is 16.14%. This demonstrates that the time-series calibration method based on Kalman filtering has good reliability and stability.