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High spatial resolution and high temporal frequency fractional vegetation cover(FVC)products have been increas-ingly in demand to monitor and research land surface processes.This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors:the 30-m resolution sensor on the Chinese environment satellite(HJ-1)and the 1-km Moderate Resolu-tion Imaging Spectroradiometer(MODIS).The algorithm was implemented for each main vegetation class and each land cover type over China.First,the high spatial resolution and high temporal frequency normalized difference ve-getation index(NDVI)was acquired by using the continuous correction(CC)data assimilation method.Then,FVC was generated with a nonlinear pixel unmixing model.Model coefficients were obtained by statistical analysis of the MODIS NDVI.The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC).Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs)of forest,cropland,and grassland were-0.025,0.133,and 0.160,respectively,in-dicating that the FVCs derived from the proposed algorithm were consistent with ground measurements[R2 = 0.809,root-mean-square deviation(RMSD)= 0.065].An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude(RMSD approxim-ates 0.1).Overall,the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.