In this work, a soil moisture data assimilation scheme was developed based on the Community Land Model Version 3.0 (hereafter CLM) and Ensemble Kalman Filter. Soil moisture in the 1st soil layer was assimilated into CLM to evaluate the improvements of land surface process simulation. The results indicated that the assimilation system could improve the model accuracy effectively. It can transfer the variations of shallow soil layer’s moisture to the deep soil and make great improvements to the soil water and heat status in an overall level. The system could improve the soil moisture accuracy from the 1<sup>st</sup> soil layer to the 6<sup>th</sup> soil layer by 50%. According to this experiment, the transfer depth of soil moisture was from 40 cm to 60 cm. After assimilation, the correlation coefficient of latent heat flux observation and simulation increased from 0.68 to 0.91 and the RMSE dropped from 86.7 W/m<sup>2</sup> to 45.7 W/m<sup>2</sup>. For the sensible heat flux, the correlation coefficient increased from 0.69 to 0.80 and the RMSE reduced from 105.1 W/m<sup>2</sup> to 71.3 W/m<sup>2</sup>. It was feasible and significant to assimilate soil moisture remote sensing products.
Land surface heat and water fluxes are key components of water and energy cycles between land and atmosphere. Information about these surface fluxes can guide agricultural production and environmental preservation, and manage different ecosystems to mitigate climate change. The main objective of this work is to estimate the surface heat fluxes and evapotranspiration. For this purpose, the Community Land Model Version 3 was used, atmospheric forcing data and flux observation were extracted from AmeriFlux standardized Level 2 database, then surface heat fluxes under two different underlying surfaces were modeled. The results showed that the model works well regarding the simulation of daily surface fluxes and diurnal surface fluxes although these values were underestimated relative to the values observed from eddycovariance. After validation, evapotranspiration was chosen as the indicator for specific comparison. CLM3.0 showed a better performance in simulating the moisture and evapotranspiration.