Data assimilation on soil moisture content based on multi-source remote sensing and hydrologic model

This paper proposed a one-dimensional soil moisture content data assimilation system based on the ensemble Kalman filter (EnKF), the distributed hydrology-soil-vegetation model (DHSVM), microwave radiative transform model (advanced integration equation model, AIEM) and optically semi-empirical model (temperature-vegetation dryness index, TVDI) for soil moisture content retrieval in bare soil. Numerical experiments were conducted at the middle reaches of the Heihe River Basin from June 1 to July 2, 2008. The results indicate that EnKF is an efficient approach to handle the strongly nonlinear problem. By assimilating multi-source remote sensing observations, the assimilation method works successfully with DHSVM and significantly improves the soil surface moisture estimation in the surface layer and root layer, the root mean square error (RMS) and mean bias errors (MBE) decrease 0.021 7 and 0. 032 9 in surface layer and 0.019 3 and 0.025 in root layer respectively, both in Yingke station. In the Linze station, the retrieve precision was also improved. It is practical and effective for soil moisture content estimation by assimilation of multi-source remote sensing data.