Abstract: Fractional Vegetation Cover (FVC) is a crucial indicator for monitoring vegetation growth status, desertification monitoring and evaluation, among other aspects. However, due to the differences in physiological and spectral characteristics between desert vegetation and typical healthy green vegetation, extracting FVC information through remote sensing faces many challenges. This paper summarizes and analyzes the progress in the application of various methods for extracting FVC in desert areas from the aspects of data, methods, and applications. The research results show that: (1) Remote sensing data sources applied in desert areas include multispectral imagery, hyperspectral imagery, radar imagery, and high-resolution UAV imagery. Integrating multi-source remote sensing data and combining multiple methods can significantly improve the estimation accuracy of desert vegetation coverage. (2) The empirical regression model and the pixel decomposition model are the most widest used models; however, the empirical regression model oversimplifies the complex relationship between FVC and remote sensing information, while the pixel decomposition model ignores the spatial heterogeneity of vegetation, both of which fail to fully consider the spatiotemporal characteristics of background remote sensing data. In recent years, machine learning and data mining algorithms have been widely used in desert vegetation cover extraction. This type of algorithm can process large-scale data, significantly improve processing efficiency, and reduce human interference and other factors to certain extent. However, they have limited modeling capabilities for the complexity and non-linear relationships of surface environmental changes, and may face challenges such as sample imbalance, difficulty in feature selection, and insufficient model generalization capabilities. Future desert FVC remote sensing extraction research will adopt multi-scale, multi-source data, and multi-method fusion approach to achieve large-scale, high-precision and time-sequential estimation models. This new form can effectively solve the shortcomings of single satellite remote sensing image data in terms of time, space and spectral resolution information. However, due to the unique physiological and spectral characteristics of desert vegetation, more new remote sensing indicators, theoretical methods, and more improved algorithm need to be further explored.