Eutrophication of sea-going rivers seriously threatens the regional ecological environment and human safety as they transport pollutants from land-based sources to the sea. With the deep implementation of Xi Jinpings ideology of ecological civilization, the widespread application of management systems such as the “river chiefs” and the “bay chiefs”, as well as the comprehensive fight against pollution, the water quality of rivers and near-shore waters entering the sea has steadily improved. Despite this, water quality fluctuates, and pollution prevention remains vital. There is an urgent need to strengthen real-time and effective large-scale remote sensing monitoring to track and consolidate treatment effectiveness. Recently, as high-resolution satellite and UAV remote sensing technology has rapidly developed, a research hot topic in the field has been the application of quantitative remote sensing monitoring of water column components in river water bodies to promote further improvement of pollution prevention and control. In this paper, a quantitative remote sensing inversion study of chlorophyll a and trophic level index (TLI(Σ)) of the main seagoing rivers in Lianyungang was conducted utilizing field measurements of chlorophyll a, total phosphorus, and total nitrogen content in the Qiangwei River, Linhong River, Gubo Shanhou River, and Guan River during June 2022, as well as Sentinel-2A MSI L2A satellite images. A significant correlation was found between chlorophyll concentration, TLI(Σ), and visible band reflectance. In particular, this was evident in the three bands of 490, 560, and 665 wavelengths, which can be used as sensitive bands for modeling. Analysis showed that the correlation coefficients of R(λ) and Chl a were -0.697, -0.681 and -0.693, respectively, and the correlation coefficients of R(λ) and TLI(Σ) were -0.728, -0.744, and -0.706. The accuracy comparison of the inversion models revealed that the multiplicative power model with R(665) as the independent variable in logarithmic coordinates of chlorophyll concentration was the optimal model for its remote sensing quantitative inversion (R2=0.67, MAPE=47.34%, RMSE=12.89 μg·L-1), while the multiplicative power model with R(560) as the independent variable model was the optimal model for the quantitative remote sensing inversion of TLI(Σ) (R2=0.61, MAPE=4.36%, RMSE=3.45). Using Sentinel-2A MSI L2A images acquired on 25 June 2022, models were applied to calculate the spatial distribution of chlorophyll concentration and TLI(Σ) of the main seagoing rivers in Lianyungang. As a result, the Qiangwei/Linghong River had the highest chlorophyll concentration and the highest TLI(Σ), followed by the Gubo Shanhou River and the Guan River, with the lowest. The inversion results were generally higher in the upper reaches of the rivers than in the lower reaches.