Abstract: Abstract: Fraction vegetation cover(FVC)can be used to indicate the growing status of vegetation, which is an important input for some ecological models, hydrological models, meteorological models, and so on. And FVC data set with high precision, high temporal resolution, and high spatial resolution is critical to global change monitoring. Unfortunately, current FVC products are produced using only one kind of remote sensing image, and thus their spatial coverage and temporal coverage are limited. Aiming at acquiring continuous FVC data in space and time, we explored the estimation methods of FVC of winter wheat in North China Plain using high and medium resolution images jointly. This study focused on dimidiate pixel model by combining multi-source images includingGF1-PMSimages with spatial resolution of 8m, GF1-WFVwithspatial resolution of 16m, and HJ1-CCD with spatial resolution of 30 m. Four phases of remote sensing images of those 3 sensors were selected as data source to conduct the experiments, which covered 4 growth periods of the winter wheat, including turning green &rising stage(March 23, 2015 and March 29, 2015) and jointing & flowering stage(April 28, 2014 and May 5, 2014).Within the coincidence regions of those 3 kinds of images, we selected randomly 160 winter wheat sample areas (240 m×240 m) as the regression samples, and chose randomly another 80 winter wheat sample areas (240 m×240 m) as the checking samples to verify the performance of the methods. Using these regression samples, we developed multi-source whole-growth-period method (MWM) and multi-source single-growth-period method (MSM) based on the bottom-up method. We compared and analyzed the single-source inversion method (SIM), MWM and MSM based on the estimated FVC result using high spatial resolution GF1-PMS images. The results indicated that the FVC estimations of HJ1-CCD, and GF1-WFV images using SIM method were highly consistent with those of GF1-PMS images, and their R2 values were both higher than 0.9. However, due to the observation angle effect of GF1-WFV and HJ1-CCD sensors, the estimated FVCs were a little higher in the early growing stages of winter wheat, and the bias decreased gradually with the closing of winter wheat canopy. Compared with SIM method, MWM method and MSM method both worked more effectively and generated higher accuracy. Among those two multi-source methods, MSM method showed the relatively higher accuracy, and its determinant coefficients R2 was 0.984 and the root mean square error(RMSE)was 0.030 using GF1-WFV images, while the R2 was 0.978 and the RMSE was 0.034 using HJ1-CCD images. The R2 of MWM method was 0.964 and the RMSE was 0.044 using GF1-WFV images, and the R2 was 0.950 and the RMSE was 0.052 using HJ1-CCD images. Comparison indicated that MWM can be utilized to improve the FVC estimation accuracy using GF1-WFV and HJ1-CCD images when there are no matching GF1-PMS images over the same period. This research shows that the synergetic inversion method of winter wheat FVC with multi-source satellite images can generate long time series and high precision FVC products, which can provide the critical data set for vegetation growth monitoring, monitoring of ecological environment and global change detection.