Abstract: Total nitrogen content (TN) in leaves is an important indicator for monitoring and identifying the physiological status and growth trend of wetland plants. Using hyperspectral remote sensing technology to estimate wetland plant leaf nitrogen content is essential for understanding the nitrogen cycle of wetland ecosystems. This study aimed to investigate the potential of leaf spectra of wetland vegetation in estimating the nitrogen content. The TN content of leaves of typical wetland plants Phragmites australis and Zizania caduciflora in Xihu Lake wetland park of Yunnan Province was studied. The plant leaf reflectance spectra of sample sites were acquired with all ASD Field Spec 3 spectrometer (350-2500 nm), and leaf total nitrogen contents were determined by Kjeldahl nitrogen measurement method after acquiring the leaf reflectance spectra. Ten advanced differential transformation spectral algorithms were used for spectral pre-treatments. The correlations between leaf nitrogen contents and the differential transformation spectrum were evaluated by partial correlation analysis, and then univariate models and multivariate models including partial least squares regression and back propagation (BP) neural network algorithm model were established. Moreover, the accuracy of all the models was tested through Pearson correlation coefficient of determination (r²) and root mean square error (RMSE). The results showed that: (1) The differential transformation can effectively improve the sensitivity of the original spectrum leaf nitrogen content inversion, and fully reflect the sensitivity of short-wave infrared wave band representing leaf TN content. The correlation between second-order differential (R'') reflectance of Phragmites australis and TN content was the highest at 1682 nm, with the correlation coefficient of 0.70. The correlation between square root second-order differential ((\sqrtR)'' ) reflectance of Zizania caduciflora and TN content was the highest at 1190 nm, with the correlation coefficient of −0.80. (2) Comparing different types of wetland plants, the accuracy of all established prediction models using Zizania caduciflora reflectance spectra was higher than that using Phragmites australis reflectance spectra. (3) Compared with the univariate techniques, the accuracy of BP model was the highest, and two types of plants accuracy (r²) was 0.96, and RMSE of Phragmites australis and Zizania caduciflora was 0.63 and 0.47, respectively. BP provided the most useful explorative tool to reveal the relationship between spectral reflectance and TN content of leaves. The results not only provide a scientific basis for artificial intelligence technology in wetland monitoring and management, but also provide a new method for lake water environment pollution control.