Water color is a fundamental parameter for describing the optical properties of water bodies and encapsulates vital information about the aquatic environment. As the most visually direct indicator in marine surveys, water color reflects not only change in the aesthetic quality of water bodies but also plays a key role in environmental impact assessments, especially in sensitive areas. Water color is influenced by light scattering and changes in environmental conditions, closely related to factors such as chlorophyll, suspended particulate matter (SPM), and the absorption and scattering of colored dissolved organic matter. Previous research has mainly relied on the Forel-Ule index (FUI) for measuring water color. However, due to the complexity and variability of China’s coastal waters, the FUI may not capture detailed water color information or accurately represent the environmental conditions of the water body. In contrast, the hue angle (α) in the Commission internationale de l’éclairage (CIE) color system, as a continuous variable, provides a more accurate representation of water color characteristics and helps extract detailed water quality information. Monitoring water color not only provides vital information on global and regional water quality assessments but also plays a crucial role in marine environmental protection and the maintenance of ecological balance.

Based on data collected during research cruises, including Secchi depth (Z_sd), mass concentration of suspended particulate matter (SPM), and phytoplankton absorption coefficient (a_ph), we develop inversion models for Z_sd, SPM, a_ph (443), and a_ph (670) using the hue angle as the key variable. These models are validated using the leave-one-out cross validation method. Utilizing hyperspectral remote sensing reflectance (R_rs) data collected during the cruise, we perform stepwise regression analysis with SPSS software. The hue angle serves as the dependent variable, while R_rs values at the MODIS’s central spectral bands act as the independent variables. The data are divided so that 2/3 of data are used to calculate the chroma parameter K. Finally, long-term hue angle information for China’s coastal waters is obtained using satellite R_rs data. This study also compares the effectiveness of the hue angle and the FUI in characterizing changes in water color parameters based on cruise-measured data. The results reveal that the hue angle provides a more detailed and continuous representation of variations in water color parameters.

This study uses R_rs data of MODIS and SeaWiFS to obtain long-term hue angle information for China’s coastal waters, utilizing a method that extracts hue angles from multispectral R_rs (Fig. 6). The highest hue angles are recorded near the coast (around 200°), with values decreasing offshore. The Bohai Sea has the highest average hue angle (180°), followed by the Yellow Sea, which also shows the most significant seasonal variation, with offshore values ranging from 70° to 140°. The East China Sea has the lowest average hue angle (60°) and the least seasonal fluctuation. Seasonal patterns are observed, with hue angles decreasing from spring to summer, reaching their lowest in summer, then increasing in autumn and peaking in winter. In certain characteristic sea areas, the hue angle shows strong covariation with water quality parameters like mass concentrations of Chl-a and SPM. In areas with high hue angles, a significant correlation is observed between hue angles and mass concentration of SPM, while in areas with lower hue angles, strong covariation is observed among hue angles, mass concentration of SPM, and mass concentration of Chl-a (Fig. 7). Using in-situ data from the East China Sea, we develop models for several water quality parameters based on hue angles and validate their accuracy using the leave-one-out cross validation method. This approach can be applied to portable high-definition imaging devices, such as smartphones and digital cameras, to capture ocean water color images, extract hue angle information, and obtain water quality data. A comparison of the hue angle (α) and FUI reveals that the discrete nature of FUI leads to the loss of water color information (Fig. 8). In contrast, the continuous nature of the hue angle captures more detailed color data (Figs. 9 and 10).

We applied the CIE-XYZ color system and a hue angle retrieval method based on satellite multispectral remote sensing reflectance. By combining data from the MODIS and SeaWiFS satellites, a long-term dataset of hue angle for China’s coastal waters was obtained. The study reveals that the hue angle exhibits distinct spatiotemporal distribution characteristics. The Bohai Sea has the highest monthly average (180°), followed by the Yellow Sea (100°), both showing significant seasonal variability. In contrast, the East China Sea has a lower monthly average (60°) with minimal seasonal variability. In addition, there is a strong covariation between hue angles and water quality parameters. Based on cruise-measured data, we developed retrieval models for water quality parameters using hue angle. The results demonstrate that the models for transparency (R²=0.79), suspended particulate matter concentration (R²=0.90), phytoplankton absorption coefficient at 443 nm (R²=0.79), and phytoplankton absorption coefficient at 670 nm (R²=0.80) exhibit high goodness of fit and accuracy. Furthermore, we analyzed and discussed the advantages of using hue angle over traditional water color indices for representing water color information in the complex coastal waters of China. The findings suggest that the hue angle provides a more accurate and effective measure of water color, offering superior capability in conveying aquatic environmental information. This highlights the potential application value of the hue angle as a parameter for accurately expressing oceanic water environmental information.