Riparian zone plays an important role in intercepting surface pollutants from entering rivers, so it is particularly important to explore the influence of riparian landscape on river total nitrogen(TN) concentration for the regulation of river water quality. However, it is difficult to quantitatively analysis the complex relationship between riparian landscape and river water quality to identify the key landscape metrics and optimal riparian strip. Taking the inlet rivers of Lake Chaohu controlled by non-point source pollution as the study area, a remote sensing inversion model was firstly constructed to retrieval TN concentration in rivers using the machine learning regression algorithms according to the measured water quality data and the synchronous Sentinel-2 MSI images, then the recursive feature elimination algorithm was introduced to optimize the landscape indices, and a new random forest regression model was lastly constructed to explore the influence of different width riparian landscape on river TN concentration, to determine the most effective riparian zone width and key landscape indices affecting river TN concentration. Results showed that (1) The retrieval model suitable to TN concentration in the inlet rivers of Lake Chaohu was gradient boosted regression model, and its inversion accuracy of R ², mean squared error and mean absolute percentage error reached 0.93, 0.35 mg/L and 28.86%, respectively. (2) Compared with the traditional methods such as redundancy analysis (RDA), combining the recursive feature elimination with random forest regression algorithms was more effective method to capture the complex nonlinear relationships between landscape and water quality, with the goodness of fit R ² ＞0.87. (3) The most effective widths of riparian zone for influencing river TN concentrations in the dry and wet seasons were 1500 m and 1000 m, respectively, and the key landscape metrics contained the farmland fragmentation, proportion of urban and town, landscape fragmentation and vegetation coverage. It is suggested to reduce the farmland fragmentation, urban construction proportion, landscape fragmentation and improve the vegetation coverage in the effective width of riparian zone, so as to reduce the TN concentration in the river and the lake. Our study can provide an effective method and scientific basis for investigating the influence of surface landscape on river water quality and the prevention of river water pollution controlled by non-point source pollution in the agricultural basin.