Lakes are the main components of water resources on the earth surface and are closely related to natural environment, human life, and social economics. The variation of lake ecosystem triggered by natural changes and human activities has attracted attention of scientists and governments worldwide. As a major lake ecological problem, lake eutrophication can lead to algal blooms, causing ecosystem disaster and drinking water risk. Therefore, effective monitoring of lake eutrophication process is an important cornerstone to accurately grasp the lake ecological dynamics and strictly control the lake environment pollution. This study mainly discusses the research progress on remote sensing assessment of lake nutrient status and retrieval algorithms of characteristic parameters.Through in-depth analysis of a large number of relevant literatures in recent years, this study systematically summarizes the existing methods for remote sensing assessment of lake nutrient status and introduces the research progress on retrieval algorithms of characteristic parameters. In addition, suggestions and prospects for the studies of lake eutrophication are put forward from the perspective of remote sensing big data. Thus, the objectives of this study are to provide an overview of remote sensing algorithms as useful reference and demonstrate the feasibility of remote sensing big data for the assessment of lake nutrient status.Accurate, real-time, and large-scale monitoring of lake nutrient status is an important basis for understanding the characteristics of lake environment change, through analysis, evaluation, remediation, and management of lake eutrophication. Compared with the traditional survey approaches, remote sensing has the advantages of fast, wide and periodicity. It has been broadly used in monitoring various lake environmental parameters, such as chlorophyll, transparency, and nutrient status. This study focuses on remote sensing assessments based on Trophic State Index (TSI) and Trophic Level Index (TLI). Moreover, the latest studies on retrieval algorithms (including empirical model, semi mechanism model, and machine learning model) of characteristic parameters are summarized. Therefore, the reliability of remote sensing assessment of lake nutrient status has been fully demonstrated.Through combing the research progress on the conventional assessments (i.e., TSI and TLI) and the retrieval algorithms of key characteristic parameters (i.e., ZSecchi Disk, Forel—Ule index, chlorophyll a, total nitrogen, and total phosphorus), the potential correlation between the two methods is clarified. The results can provide reference for the studies on lake ecological environment and the possibility for improving the remote sensing technology of lake optics and water color in the future.In recent years, with the continuous improvement of quantitative retrieval algorithm and satellite sensor technology, research progress on remote sensing assessment of lake nutrient status has entered a rapid development stage. The review of related studies has advanced our understanding of lake eutrophication by remote sensing data and technology. In summary, remote sensing plays a significant role in the research of lake eutrophication and provides practical contribution to the monitoring and protection of lake ecological environment in China and even the world.