###### Abstract  
Guangzhou and Shenzhen are two core cities in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It is increasingly important to regulate water quality in urban development. The Forel-Ule Index (FUI) can be obtained by optical data and is an important indicator. Therefore, we used Sentinel-2 to calculate the FUI of 41 lakes and reservoirs in Guangzhou and Shenzhen from January to December in 2016-2021, and analyzed their spatio-temporal variations, including spatial distributions, seasonal variations, and inter-annual variations. We also performed a correlation analysis of driving factors. In Guangzhou, the FUI was low in the north and west, and high in the south and east. In Shenzhen, the FUI was high in the west and low in the east. Moreover, 68% of the lakes and reservoirs in Guangzhou exhibited seasonal variations, with a low FUI in summer and autumn, and high levels in spring and winter. Shenzhen had the lowest FUI in autumn. Furthermore, 36% of the lakes and reservoirs in Guangzhou exhibited increasing inter-annual variations, whereas Shenzhen exhibited stable and decreasing inter-annual variations. Among the 41 lakes and reservoirs analyzed herein, the FUI of 10 water areas were positively correlated with precipitation, while the FUI of 31 water areas were negatively correlated with precipitation. Increased precipitation leads to an increase in external pollutants and sediment, as well as the resuspension of substances in the water, resulting in more turbid water. Therefore, an increase in precipitation is positively correlated with the FUI, whereas a decrease in precipitation is negatively correlated with the FUI. These findings can be used to design suitable management policies to maintain and control the local water quality. F 2023 12 2023 20  
respect to spatio-temporal variations, particularly in inaccessible water areas. Remote sensing technology is advantageous in terms of both a wide monitoring range and a short monitoring cycle [1; 2]. In addition, remote sensing can retrieve some traditional parameters, such as water clarity (SDD) [3; 4; 5], chlorophyll a (Chla) [6; 7; 8], suspended particulate matter (SPM) concentration [9; 10], colored dissolved organic matter (CDOM) [11], etc. However, owing to the complex optical properties of inland water, many algorithms for parameters are limited to different regions and seasons [12; 13]. To apply remote sensing to analyze the variations in water quality, it is necessary to select parameters that are invariant by optical properties, such as water color. Water color can provide regional and global water quality characteristics, and is used to monitor lakes and reservoirs. Water color results from the interactions between sunlight and the substances (Chla, SPM, and CDOM), and is related to the absorption and scattering of the substances [14]. Previous studies have applied water color parameter analyses for water quality research. [PERSON] and [PERSON] (1978) used Landsat data for chromaticity analysis and found that it was a rapid and simple water quality monitoring method [15]. Related studies have also shown that differences in the water components produced considerable differences in the main wavelength range of the water color. Related studies have indicated that, owing to the uncertainty of the substances in the water, extensive retrieval methods for water quality parameters using water color analysis and the band ratio method have certain limitations and instabilities [16]. [PERSON] et al. (2016) analyzed water quality variations of ten lakes in China by extracting the Forel-Ule Index (FUI) from the Moderate Resolution Imaging Spectrometer (MODIS) [17]. Furthermore, [PERSON] et al. (2018) used MODIS to retrieve the trophic status of water using the FUI, and analyzed the variations in global inland water [18]. The FUI dataset for global lakes and reservoirs from 2000 to 2018 based on MODIS was then released, which provided information on spatio-temporal variations in water color of global lakes and reservoirs during the period [19].