Improving Soil Moisture and Evapotranspiration Estimation by Data Assimilation and Remote Sensing Data over Irrigated Cropland in the Northern China Plain

Abstract:

Land surface model is considered to be a powerful tool to estimate continuous soil water content and surface fluxes. However, simulation error tends to accumulate in the process of model simulation due to the inevitable uncertainties of forcing data and intrinsic model errors. Data assimilation technique considered the uncertainty of the model, update model states during the simulation period, and therefore improve the accuracy of soil water content and surface fluxes estimation. In this study, an Ensemble Kalman Filter (EnKF) technique was coupled to a Hydrologically-Enhanced Land Process (HELP) model to update model states including soil water content and surface temperature. The remotely sensed latent heat flux (LE) estimated by Surface Energy Balance System (SEBS) was used as the observation value in the data assimilation system to update the model states such as soil water contents and surface temperatures, etc.The model was validated by the observation data in 2006 at Shandong Weishan eco-hydrological station, where the open-loop estimation without state updating was treated as the benchmark run. Results showed that the root mean square error (rmse) of soil water content reduced by 30%-50% compared to the benchmark run, while the surface fluxes also had significant improvement to different extents, among which the rmse of LE estimation from wheat season and maize season reduced by 33% and 44%, respectively. These results demonstrated that the application of data assimilation technique can substantially improve estimation of land surface energy fluxes and soil water states. It is suggested that data assimilation system had great potential to be used in land surface models in agriculture and water management.