Abstract: Due to the fact that soil wetness plays an important role in the global hydrological cycle and climate system, it has been widely concerned. Accurate and high-resolution soil wetness data are the basic data for in-depth understanding of the earth atmosphere process, ecological environment evolution, hydrological cycle and climate change, as well as the prediction of weather and climate and the assessment of flood and drought disasters. In recent years, the global soil wetness observation program, as well as grid soil wetness with high spatial and temporal resolution through remote sensing retrieval and model simulation has developed rapidly, especially in the field of hydrologic cycle and weather and climate, which have been gradually applied from the initial research level to the practical business. Firstly, the article briefly introduces the main land surface models for simulating soil moisture, as well as the corresponding results of the Land Surface Process Parameterization Scheme Comparison Plan (PILPS) and the Global Soil Moisture Project (GSWP). Secondly, introduces the progress of land surface data assimilation system to simulate soil wetness from two aspects: data assimilation algorithm and assimilation system. Finally, summarize main methods, indexes and forms for soil wetness verification. In the end, the article makes prospects and suggestions from four aspects: (1) upgrading the observation system to obtain high-quality in-situ observation data; (2) Improving the ability of remote sensing detection to expand the application of products; (3) developing high-precision meteorological driving data and preparing land surface parameters to improve the model results; (4) increasing the understanding of the earth and atmosphere system to improve the physical process of the model.