The objective of land data assimilation is to merge multi-source observations into the dynamics of land surface model for improving the estimation of land surface states. We have developed a land data assimilation system for China's land territory. In this system, the Common Land Model and Simple Biosphere Model 2 are used to simulate land surface processes. The radiative transfer models of thawed and frozen soil, snow, lake, and vegetation are used as observation operators to transfer model predictions into estimated brightness temperatures. A Monte-Carlo based sequential filter, the ensemble Kalman filter, 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), TRMM microwave imager (TMI), and advanced microwave scanning radiometer enhanced for EOS (AMSRE) and the conventional in situ measurements of soil and snow. A spatiotemporally consistent assimilated dataset for soil moisture, soil temperature, snow and frozen soil, with a spatial resolution of 0.25 degree and temporal resolution of one hour, has been produced. This paper introduces the development of Chinese land data assimilation system and the progress made on data assimilation algorithms, land surface modeling, microwave remote sensing of land surface hydrological variables, and the preparation of atmospheric forcing data. The distinct characteristics and challenges of developing land data assimilation system and the perspectives for future development are also discussed .