Vegetation water content is an important indicator of vegetal state, and a vital parameter of studying agriculture, ecological and hydrological. The diagnosis of vegetation water content has great significance for forest fire forecast and natural vegetation drought condition monitoring. The correlation analysis of the vegetation spectral reflectance and vegetation water content shows that the relativity between the spectral reflectance of different wavelengths and the vegetation water content varies considerably. The spectral reflectance of red band of visible light (620～700 nm) and the near-infrared band(800～1 350, 1 600～1 950, 2 200～2 400 nm) had a higher correlation with the vegetation water content. The slope angle indexes were used as parameters for estimating the vegetation water content based on analyzing the relation between the slope angle indexes and vegetation water content. An evaluation model of vegetation water content was set up by utilizing statistical linear regression model method. The band of 660, 850, 1 630, 2 200 nm were selected as RED, NIR, SWIR1 and SWIR2 band value of the slope angle index based on the analysis of the correlation between spectral reflectance and vegetation water content. A large amount of vegetation spectral information and vegetation water content were collected in the study area(the upstream of Minjiang River), and the linear regression model of the slope angle index (SANI, SASI, ANIR) and vegetation water content (FMC) was build. The linear regression model of ANIR and FMC has the highest of linear fitting and the linearity is up to 0.791. The near infrared angle index(ANIR)was improved on the basis of the analysis the linear regression results of angle slope vegetation index and water content. Near infrared angle normalized index (NANI) and near infrared angle slope index (NASI) were defined, and the linear regression model was established. Compared with the slope angle index (SANI, SASI, ANIR) which were proposed by Palacios-Orueta, NANI had more advantages in the vegetation water content inversion in the study area. The determination coefficient (R2) of the inversion model increased from 0.791 to 0.853, and root-mean-square error (RMSE) reduced from 0.047 to 0.039. Angle slope index had higher linear fitting and estimation accuracy by improving the angle of slope index. NANI and FMC linear regression model was established to estimate the vegetation water content in the study area. In this paper, the main innovation point is that the slope angle index NANI and NASI has been proposed on the basis of predecessors’ research results, and the improved angle slope index has higher linear fitting and estimation accuracy compared with SANI, SASI, ANIR.