植被含水量是植被生长状态的重要指示因子, 是农业、 生态和水文等研究中的重要参数, 其诊断对于监测自然植被群落的干旱状况、 预报森林火灾等都具有重要意义。 通过对植被光谱反射率与植被含水量的相关性分析, 发现植被波谱不同波段的光谱反射率与植被含水量的相关性差异很大, 其中可见光红光波段（620～700 nm）、 近红外波段（800～1 350, 1 600～1 950, 2 200～2 400 nm）的光谱反射率与植被含水量具有较好的相关性, 选取了660, 850, 1 630和2 200 nm的光谱反射率作为RED, NIR, SWIR1和SWIR2的波段值来建立角度斜率指数; 分析了植被含水量与角度斜率指数的关系, 将角度斜率指数（SANI, SASI, ANIR）作为反演植被含水量的参量, 建立植被含水量与角度斜率指数之间线性回归模型。 通过对近红外角度指数ANIR改进, 提出了近红外角度归一化指数NANI（near infrared angle normalized index）与近红外角度斜率指数NASI（near infrared angle slope index）, 建立植被含水量与NANI和NASI之间线性回归模型, 结果显示: NANI与Palacios-Orueta等提出的角度斜率指数（SANI, SASI, ANIR）相比有一定的优势, 模型可决系数R2从原最高0.791提高到0.853, RMSE也从原最小0.047降低到0.039。 确定了NANI为反演植被含水量的最佳角度斜率指数, 并建立了植被含水量反演模型。 该研究主要创新点: 在前人研究成果基础上, 通过对原角度斜率指数的改进, 提出了NANI和NASI角度斜率指数, 使其在植被含水量反演上具有更高的精度。

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.