Abstract: As a major form of soil degradation, soil salinization can greatly harm agricultural production and ecological environment. Remote sensing methods can acquire soil spectral characteristics in a rapid, macroscopic, and timely manner. Based on this, remote sensing monitoring models can be built for a wide range of soil salinization monitoring and assessment. Thus, summarizing and discussing the building methods for remote sensing monitoring models of soil salinization is of great significance to improve the precision of remote sensing monitoring of soil salinization and to monitor and control salinized soil. This study reviewed the recent literature related to remote sensing studies concerning soil salinization at home and abroad. Then, it summarized the steps such as factor selection, model building, and precision verification in the building of remote sensing monitoring models of soil salinization. Focusing on the current hot research topic, this study discussed the limitations and development trends. The main conclusions are as follows. The remote sensing monitoring models of soil salinization are important means for monitoring and forecasting salinized soil. In recent years, the hot research topic in this field is to improve the model precision using new data sources and models. Differences exist in the use of remote sensing data sources among different studies, but the modeling factors are all optimized from spectral sensitive bands, prior spectral indices, and remote sensing-derived data. The remote sensing monitoring models of soil salinization mainly include the linear regression model and the machine learning model. The remote sensing models built for different regions have different precision and applicability.