ESTIMATION OF REGIONAL SOIL MOISTURE BY ASSIMILATING MULTI-SENSOR PASSIVE MICROWAVE REMOTE SENSING OBSERVATIONS BASED ON ENSEMBLE KALMAN FILTER

We have developed Chinese land data assimilation system (CLDAS). In this system, the Common Land Model (CoLM) is used to simulate land surface processes. The radiative transfer models of thawed and frozen soil, snow, and vegetation are used as observation operators to transfer model predictions into estimated brightness temperatures. The EnKF algorithm is implemented as data assimilation method to integrate modeling and observation. The system is capable of assimilating passive microwave remotely sensed data such as special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer enhanced for EOS (AMSR-E). In this study, we primarily compare the assimilation results of soil moisture with AMSR-E L3 surface soil moisture products and in situ observations from GAME-Tibet experimental fields. The results indicate that the relationship between the simulated and assimilated surface soil moisture with AMSR-E L3 surface soil moisture products is very low. In comparison with in situ observations from GAME-Tibet experimental fields, the assimilated results of soil moisture are better than the simulated results. Additionally, the assimilated results can describe the freeze/thaw cycle. Index Terms — Ensemble Kalman filter, land dat