Abstract: Hyperspectral reflectance information is a crucial method to detect total nitrogen content in plant leaves, meanwhile, vegetation nitrogen content has a strong relationship with nitrogen in water. Taking Mencheng Lake Wetland Park supplied with reclaimed water as study area, the vegetation hyperspectral data (Phragmites australis and Typha angustifolia), and the content of total nitrogen in water were detected to investigate the feasibility of estimating total nitrogen content in reclaimed water based on hyperspectral reflectance information from emergent plants. We established simple linear regression model, stepwise multiple linear regression model and partial least square regression model based on four hyperspectral indices (spectral indices, normalized difference indices, trilateral parameters, absorption feature parameters), respectively. The accuracy of these models was  coefficient of determination (R²) and root mean square error (RMSE). The results showed that stepwise multiple linear regression model and partial least square regression model predicted more accurately than simple linear regression model, and the accuracy of prediction models based on P. australis reflectance spectra was higher than those on T. angustifolia. Partial least square regression model was the most useful explorative tool for unraveling the relationship between spectral reflectance of P. australis and total nitrogen content in water with R² of 0.854 and RMSE of 0.647. 500-700 nm was the best band range for detecting water total nitrogen content. The reflectance ratio of green peak and red valley  could be effectively predicted by the absorption feature parameters.