Soil moisture is an important link for the exchange of water and heat processes between the surface and the atmosphere, and is of great significance to agricultural production, ecological planning, and water resources management. Microwave remote sensing has the advantages of not being affected by weather conditions and being able to better detect the soil information and trend of soil moisture change under vegetation coverage. Thus, it is a hot spot for accurate soil moisture retrieval by remote sensing. With the gradual increase in the number of Earth observation satellites, microwave detectors have developed from C-band to L-band, and soil moisture datasets have become more and more abundant. In this paper, the existing global-scale soil moisture products based on microwave remote sensing are summarized. It also analyzes and compares the principle, characteristics, application scope and key technological progress of active microwave remote sensing, passive microwave remote sensing, and active and passive microwave fusion in soil moisture retrieval. The advantage of active microwave remote sensing and passive microwave remote sensing is high spatial resolution and high temporal resolution, respectively. High spatial resolution can capture the subtle spatial information features of the surface, but it is limited by the complex relationship between soil moisture and backscattering coefficients, especially the interference of vegetation and surface roughness on the radar backscattering coefficient making retrieval of soil moisture inaccurate. Therefore, selecting different scattering models according to the actual situation and using multi-source data synergy are the current research hotspots to improve accuracy. This paper summarizes the active microwave soil moisture retrieval method into three types: mechanism model, empirical model and semi-empirical model according to the principle. We also considered the applicability and shortcomings at the same time. For instance, high temporal resolution can achieve global and large-scale soil moisture monitoring, but it is difficult to meet the actual research needs of small-scale or small-scale areas. In order to better match the measured data in space, four downscaling methods based on the geostatistical method, mathematical statistics method, data assimilation method and multi-source remote sensing data fusion method are proposed. Combining the advantages and disadvantages of the above two microwave remote sensing methods, researchers can rely on more abundant data sources and relatively mature observation technology to fuse and extract more moisture information and to improve the retrieval accuracy or obtain long-term series data. However, this method often has the problem of missing data due to factors such as revisit time, seasonal ice period or radio frequency interference, which has a great impact on the spatiotemporal continuity of the data. So, the interpolation methods based on time, space, and statistics have been employed to solve the missing value issues. In the current method, soil moisture retrieval shows good performance at small scales, but at the global scale, there will be many problems such as missing data, poor applicability, low retrieval accuracy, and too complicated retrieval processes. Improvements can be made by employing multiple observation methods (multi-polarization, multi-angle, multi-band), multi-temporal repeated observation, the introduction of new algorithms on the original model and data assimilation. The rapid development of long-term stable and high spatiotemporal resolution L-band microwave signals in the field of land remote sensing of Global Navigation Satellite System also provides a reference for the development of China's BeiDou Navigation Satellite System, showing its huge application potential.