Supplying urban wetlands with reclaimed water is recognized as a superior way for wetland restoration and reconstruction. However, the high concentration of nitrogen and phosphorus in reclaimed water can easily lead to water eutrophication. Although remote sensing technology has become a useful tool to monitor the eutrophication of water body, it is usually employed to detect eutrophication in open water. Limited applications have been found in measuring eutrophication of wetland covered by vegetation. Utilizing plants spectral response to environment can monitor environmental changes. This study explores the possibility to use wetland vegetation reflectance spectra in estimating total nitrogen content which is one of the key indicators of water eutrophication. The South Wetland in the Olympic Park in Beijing, a typical wetland using reused water, was selected as our study area. The leaf reflectance spectra of main wetland plants, reed (Phragmites australis) and cattail (Typha angustifolia), were acquired by means of an ASD FieldSpec 3 spectrometer (350-2500nm). Water quality samples were collected at the same time and analyzed by Center for Environmental Quality Test, Tsinghua University subsequently. The research established several univariate models including simple ratio spectral index (SR) model and normalized difference spectral index (ND) model, as well as multivariate models including stepwise multiple linear regression (SMLR) model and partial least squares regression (PLSR) model. The accuracy of these models was tested through cross-validated coefficient of determination (R_cv²) and cross-validated root mean square error (RMSE_cv). The results have shown that 1) In comparison with univariate techniques, multivariate regressions can improve the estimation of total nitrogen concentration in water. The accuracy of PLSR model was the highest (R_cv²=0.72, RMSE_cv=0.24) among all models. PLSR provides the most useful explorative tool for unraveling the relationship between spectral reflectance of wetland plants and total nitrogen content in water at leaf scale. 2) The accuracy of prediction models built in this study using Phragmites australis reflectance spectra is higher than those using Typha angustifolia reflectance spectra. 3) Other environmental factors should also be discreetly considered in modeling exercise. Total phosphorus is found to have impact on the relationship between TN and reflectance spectra from wetland vegetation. Strong predictive power for multiple regression equations has been achieved when the range of total phosphorus was restricted. The result from this study can not only fill the gaps in the detection of eutrophication using remote sensing, but also provide a strong scientific basis for the water quality monitoring and management of urban wetlands using recycled water.