Background The eutrophication of water bodies will lead to an imbalance in aquatic ecosystems, affect the survival of habitats, and cause economic losses of aquaculture. Rapidly assessing the eutrophic conditions and evolving trends of key aquaculture waters are highly significant for the advancement of marine fisheries and the protection of the ecological environment.

Objective This study aims to establish an inversion model of the eutrophic index applicable to important aquaculture bays to provide scientific reference for marine environmental protection and aquaculture planning.

Methods The study utilized the measured water quality parameters and Sentinel-2 MSI satellite remote sensing image data, screened out the three band combinations with the highest correlation with the logarithmic lg(E) of the eutrophication index as characteristic bands. These bands were then inputted into three machine learning models: CatBoost, BP neural network, and random forest. The study compared the inversion accuracy of these models to determine the optimal one. Subsequently, the study used this model to invert the eutrophication status of the waters in the Zhao’an Bay and Dongshan Bay areas of Fujian Province in 2022, and analyzed the spatial and temporal characteristics.

Results The results showed that b3+b7, b3−b12, and b3×b9 were the best band combinations for inversion of eutrophication index, with correlations all around 0.8. The CatBoost model had higher inversion accuracy than BP neural network and random forest. The coefficient of determination R^2 reached 0.90, the root mean square error (RMSE) was 5.67, and the mean absolute percentage (MAPE) was 43.61%, respectively, the eutrophication index of Dongshan Bay showed a gradually decreasing trend from the top of the bay towards the outside. The eutrophication index of Zhao’an Bay showed a trend of high along the coast and low in the middle of the bay, with obvious characteristics of low in winter and high in other seasons. Conclusion There are significant differences in the spatial and temporal distribution of eutrophication between Dongshan Bay and Zhao’an Bay. The factors that affect eutrophication include input of terrestrial pollutants, seasonal changes in aquaculture, geographical features, hydrological environment, weather changes, etc. These findings provide methodological support for large-scale, rapid, and convenient eutrophication monitoring, and provide reference for environmental assessment, governance, and aquaculture management.