In this paper

a land surface temperature data assimilation scheme is developed based on Ensemble Kalman Filter （EnKF） and Common Land Model version 1.0 （CLM）

which is mainly used to improve the estimation of the sensible and latent heat fluxes by assimilating MODIS land surface temperature （LST） products. Leaf area index （LAI） is also updated dynamically by MODIS LAI products. In this study

the relationship between the MODIS LST and the CLM surface temperature is determined and taken as the observation operator of the assimilation scheme. Meanwhile

the MODIS LST is compared with the ground-measured surface temperature

and the Root Mean Square Error （RMSE） is taken as the observation error. The scheme is tested and validated based on measurements in three observation stations （Blackhill

Bondville and Brookings） of Ameriflux. Results indicate that data assimilation method improves the estimation of surface temperature and sensible heat flux. The RMSE of sensible heat flux reduced from 81.5W·m-2 to 58.4W·m-2 at the Blackhill site

from 47.0W·m-2 to 31.8W·m-2 at the Bondville site

from 46.5W·m-2 to 45.1W·m-2 at the Brookings site. The RMSE of latent heat fluxes reduced from 88.6W·m-2 to 57.7W·m-2 at the Bondville site

from 53.4W·m-2 to 47.2W·m-2 at the Blackhill site. In addition

it is a practical way to improve the estimation of sensible and latent heat flux by assimilating MODIS LST into land surface model.