Abstract: Turbidity, as a key optical parameter measuring the scattering and absorption of light by suspended substances (including organic/inorganic particles, phytoplankton, and microorganisms), is one of the core indicators in water quality assessment. In recent years, frequent pollution events in inland water bodies, such as tailings leakage, abnormal turbidity discharges from reservoirs, and industrial runoff, have heightened the need for efficient and accurate water quality monitoring technologies. Traditional turbidity monitoring methods rely on field sampling and laboratory analysis. Although these techniques provide high-accuracy results, they are inherently limited in spatial coverage and temporal frequency, while also being labor-intensive and time-consuming. With the advancement of remote sensing technology, the acquisition of multispectral data via satellite or aerial platforms has emerged as a promising and cost-effective approach for large-scale and periodic turbidity retrieval. Remote sensing-based turbidity estimation offers several advantages, including wide-area coverage, temporal consistency, and the ability to monitor inaccessible regions. A bibliometric analysis of 574 relevant research articles indexed in the Web of Science reveals a clear upward trend in the number of turbidity-related remote sensing studies. Moreover, the diversity of satellite data sources continues to expand. Among them, the Landsat series stands out as the most commonly used due to its 30 m spatial resolution and extensive historical archive spanning over four decades. Other widely adopted sensors include MODIS (Moderate Resolution Imaging Spectroradiometer), which is suited for large-scale observations; MERIS (Medium Resolution Imaging Spectrometer), known for its ocean color-specific spectral bands; and Sentinel-2 MSI and Sentinel-3 OLCI, which strike a balance between spatial and spectral resolution. In terms of retrieval methodologies, early studies primarily employed linear or polynomial regression models. Over time, more sophisticated approaches have emerged, forming a diverse methodological framework. These include empirical models such as single-band threshold methods and band ratio algorithms, semi-analytical models that incorporate water optical properties, and data-driven machine learning algorithms that offer improved flexibility and adaptability. However, accurately estimating turbidity in optically complex inland waters remains a challenge, primarily due to the heterogeneous absorption and scattering effects of different water constituents. Looking forward, future research should prioritize three key directions: integrating multi-source remote sensing data to overcome limitations in spatial and temporal resolution; improving atmospheric correction algorithms to effectively eliminate interference from aerosols and atmospheric particles; and developing classification-based retrieval frameworks that account for the variability of water optical properties across different inland water types. These efforts will collectively enhance the accuracy, robustness, and general applicability of turbidity remote sensing in complex inland aquatic environments.