Monitoring the water status of vegetation by spectral technique is one of the important means to understand the physiological status and growth trend of vegetation. In this study, the Ebinur Lake Wetland Nature Reserve is chosen as the target area. By using cluster analysis, variable importance projection (VIP) and sensitivity analysis method, the vegetation water content was classified, estimated and validated. The Results showed that in the clustering analysis method based on Euclidean distance of the vegetation moisture content is divided into three grades with higher water content, medium water content and low water content, whose ranges are around 7076%~8069%, 5327%~7076% and 31%~5327%, respectively. From 1 350 to 2 500 nm wavelength range, the spectral reflectance of water content is the lowest ,however there is no law from 380 to 1 350 nm wavelength range. By using VIP method, all vegetation water index VIP value of more than 08, indicated that vegetation water index estimation ability of water content of vegetation leaves is strong and the difference is not obvious. The MSI, or GVMI and vegetation water content cubic equation fitting is the best, the fitting coefficients of R2 were 0657 5 and 0674 2 respectively. The RWC in the range of 30%~45%, the MSI value of the NE index is the lowest. In the range of 45%~90%, the GVMI value of the NE index is the lowest. About 70% of NE value NDWI1240 index has undulation, it shows that the NDWI1240 index of the vegetation water content is at about 70% and the prediction ability is poor. Through the error analysis, the error of GVMI exponent inversion is the smallest, different vegetation indices have obvious difference in vegetation estimation results with different water contents. Therefore, it is necessary to estimate vegetation water content. In summary, using hyper spectral remote sensing technology to monitor vegetation growth and drought environment in Ebinur Lake Reserve Area is feasible.The results provide a theoretical basis for the large area inversion of satellite borne hyper spectral sensors for vegetation water content.