Abstract: The rapid and accurate estimation of soil water content at different spatial and temporal scales is key research content in the fields of hydrology, environment, geology, agriculture, and climate change. However, it is still a challenge to obtain accurate soil water content presently. In the past, the traditional point-based soil sampling and analysis methods were time-consuming and laborious. By contrast, retrieving soil water content using remote sensing images has the advantages of a wide range, high timeliness, low cost, and strong dynamic contrast. In hyperspectral remote sensing, soil water content is related to the wavelength range of soil reflectance. So far, many methods have been used to describe the relationships between soil water content and hyperspectral remote sensing. This paper summarized existing methods for estimating soil water content based on hyperspectral reflectance and divided them into four categories: spectral reflectance methods, function methods, model methods, and machine learning methods. Moreover, this paper compared and analyzed the potential and limitations of different methods in terms of accuracy, complexity, auxiliary data requirements, operability under different modes, and the dependence on soil types. Finally, this study put forward corresponding suggestions for future research on the relationships between soil water content and hyperspectral reflectance.