Abstract: Soil moisture is of critical importance in land surface processes, which plays a key role in linking the terrestrial water and energy cycles. Remote sensing has become a promising technique for obtaining large-scale soil moisture information. In this study, the authors use the Land Parameter Retrieving Model (LPRM), which is based on radiative transfer equations, to retrieve surface soil moisture from FY3B microwave brightness temperature images over Northeast China (FY3B_LPRM). The FY3B_LPRM soil moisture is then compared with other datasets including in-situ observations at agrometeorological stations in China, the NCEP and ERA-Interim reanalysis of soil moisture, the multi-satellite soil moisture product from European Space Agency (ECV data), and the FY3B soil moisture product officially produced in the National Satellite Meteorological Center of China (FY3B_offical). It is found that the spatial and temporal variations of the derived FY3B_LPRM soil moisture agree well with that of in-situ observations. FY3B_LPRM and in-situ observations both show higher soil moisture content in eastern Northeast China than in western Northeast China. The two datasets are highly correlated in most regions with correlation coefficients more than 0.7 mainly because they agree well with each other in seasonal variation. However, the FY3B_LPRM is highly negatively correlated with the FY3Bofficial and the NCEP and ERA-Interim datasets in some relatively humid regions such as the surrounding areas of Da Hinggan Mountains and eastern part of the study area. Further analysis suggests that the FY3B_LPRM can realistically represent the seasonal variability of in-situ observations, whereas the other three datasets cannot. In most of the study area, the derived FY3B_LPRM agrees very well with the multi-satellite ECV data. The derived FY3B_LPRM soil moisture can be applied for drought monitoring and hydrology and water resource studies. It also provides realistic soil moisture information for data assimilation in numerical weather forecast studies.