The characterization of soil hydraulic parameters and their heterogeneity is related to many scientific problems in soil and groundwater fields. Due to the limitation of time and sampling cost, the traditional experimental approaches cannot address this issue adequately. With the development of Internet of Things technology, the state variables related to soil water movement (such as water content and pressure head) can be acquired in real time through sensors. This has sparked some debates about how to estimate the soil hydraulic parameters using these measurements. Data assimilation methods can estimate the soil hydraulic parameters by integrating the measurements into numerical models. This paper systematically analyzes the uncertainty sources and measurement approaches of soil hydraulic parameters, expounds on the basic principles of several common data assimilation methods and their applications in soil hydraulic parameter inversion, and discusses the latest advances in data assimilation methods from aspects of computational efficiency and accuracy. Finally, the development direction of data assimilation methods is provided. The results show that the data assimilation methods can break through the limitation of the traditional experimental approach, and thus are suitable for the characterization of soil hydraulic parameters and their heterogeneity. However, limitations such as the strong nonlinearity of the unsaturated flow model, spatial heterogeneity of soil and sparsity of in-situ measurements do exist. It is, therefore, essential for us to unfold in-depth research on soil hydraulic parameter inversion from the aspects of supervised dimension reduction method, multi-source and multi-scale data fusion, and coupling of machine learning with physical mechanisms, thereby assisting agricultural soil and water management as well as the prevention, control, and remediation of pollution in agroecosystems.