基于耦合的叶片与冠层辐射传输模型，研究并使用了改进的植被指数提取植被含水量，以实现植被水分含量的遥感监测.使用PROSAIL模拟的高光谱数据，首先对NDVI、WI、NDWI、MSI、CSI及NDII等已有的植被指数提取含水量进行可行性分析比较.结果显示NDVI不能估算出植被含水量，CSI估算能力也不理想，其余四个指数WI,NDWI,NDII,MSI估算植被含水量时相关系数平方在0.76左右；之后对NDWI提取植被含水量进行了敏感因子分析，结果表明NDWI与MCARI具有较好的线性关系，从而得出改进的植被含水量估算方法.该方法可以较好地去除LAI的干扰，估算的相关系数平方提高为0.97，增加了27%，估算残差由初始的0.0156降低为0.00535，减少了65%.

By coupling leaf with canopy radiation transfer model, a modified vegetation index was explored to extract the vegetation water content in order to monitor the vegetation water content from remote sensing data. Some vegetation indices for the vegetation leaf water content, such as NDVI, WI, NDWI, MSI, CSI and NDII, were analyzed and compared based on the hyperspectral data simulated by PROSAIL. The results show that the water content can not be estimated by NDVI, and CSI is not an ideal tool. The rest four indices WI, NDWI, NDII, and MSI can generate relatively good results with the correlation coefficients square between the estimation and real value being all about 0.76. Then, sensitivity factors were analyzed for NDWI extracting the water content, and the results show that NDWI and MCARI have a good linear relationship. This method can better remove the interference of LAI. The correlation coefficient square between the estimation and real value has been improved to 0.97, which is increased by 27% compared to the foregoing value. The estimated residual is reduced from 0.0156 to 0.00535, a reduction of 65% compared to NDWI method.