Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields

The optimal estimation of soil moisture, soil temperature, and surface turbulent fluxes in irrigation fields is restricted by a lack of accurate irrigation information. To resolve the input uncertainty from imprecise irrigation quantity, an improved data assimilation scheme that is EnKS (Ensemble Kalman Smoother) implemented with inflation and localization (referred to as ESIL) is proposed to estimate soil moisture, soil temperature, and surface turbulent fluxes for irrigated fields by assimilating multi-source observations. The Daman station, which is located at an irrigated maize farmland in the middle reaches of the Heihe River Basin (HRB), is selected in this study to investigate the performance of the proposed assimilation scheme. The measured land surface temperature (LST) and surface soil moisture (SSM) in the first soil layer are taken as observations to conduct a series of data assimilation experiments to analyze the influence of a lack of irrigation information and combinations of multi-source observations on estimations of soil moisture, soil temperature, and surface turbulent fluxes. This study demonstrates the feasibility of ESIL in improving the estimation of hydrothermal conditions under unknown irrigation. The coefficient correlation (R) with the ESIL method increases from 0.342 and 0.703 to 0.877 and 0.830 for the soil moisture and soil temperature in the first layer, respectively. Meanwhile, the surface turbulent fluxes are significantly improved and the RMSE decreases from 173 W/m2 and 186 W/m2 to 97 W/m2 and 111 W/m2 for the sensible and latent heat fluxes, respectively.