There are a wide range of saline soils in Xinjiang. It covers a large area. Various types of halophytes which have prominent significance for improving the saline lands, maintaining ecological stability and promoting ecological balance grow on these saline soils. Studies have shown that many halophytes absorb a large amount of sodium. Both sodium and potassium can increase the cell osmotic pressure to adapt to the high-salt condition, producing turgor pressure and promoting cell elongation. So it is beneficial to its growth and can partially replace the function of potassium. Thus, mastering the sodium characteristics of halophytes is helpful to understand the long-term adaptation and response of halophytes to the ecological environment. In this paper, HyperSpectral technique was used to effectively explore the characteristics of leaf sodium. Firstly, the discrete wavelet transform (DWT) and db5 wavelet were used to decompose the original spectral in 9 layers, and the optimal decomposition layer is 5 layers. Secondly, the original spectral were decomposed by db5 wavelet in 5 layers, and the wavelet vegetation indices were created by the decomposed high-frequency components and low-frequency components. We selected the wavelet vegetation indices which could sensitively characterize sodium ion content of halophytes. Finally, the SVR, LS-SVR, PSO-SVR and PSO-LS-SVR models were used to estimate the sodium ion content of halophytes vegetation. The results were compared to the models created by the spectral vegetation indices of original spectral. In addition, we used the partial least squares regression model as a comparison to evaluate the advantages of the parameter-optimized support vector machine method in estimating the sodium ion content of the leaves of the halophytes vegetation using hyperspectral techniques. The results showed that: (1) The prediction results of the five models showed that PSO can effectively optimize the parameters (c, g) of SVR and LS-SVR models, and improve the accuracy and prediction ability of the models. The optimized models had the advantages of high prediction accuracy, strong generalization ability and good robustness performance. (2) The model constructed by the multiple wavelet index was an inversion model of integrated multi-scale and multi-resolution data, which can reflect the vegetation information from different aspects. Therefore, the four models constructed by the multiple wavelet index were superior to the models constructed by the single wavelet index. (3) Contrasting the inversion results of two types index, the Na+ content prediction model constructed by a single wavelet vegetation index can achieve a better prediction results. The single spectral index is not effective in estimating Na+ content, which is because the wavelet transform can reduce the noise of the original spectral and highlight the detailed feature of the spectral, improving the prediction results. The model accuracy and prediction effect of the integrated wavelet vegetation index were better than those of the integrated spectral index. More spectral subtle feature can be highlighted by wavelet transform, thus improving the ability of retrieving Na+ content in leaves by HyperSpectral method.