归一化植被指数(NDVI)在植被多光谱遥感反演中占据尤为重要的地位，而遥感影像中普遍存在的阴影对NDVI的精度产生很大的影响，因此去除阴影对植被NDVI的影响对更精确的定量化研究具有应用价值。本文基于光照区和阴影区的太阳辐射能量差异，模拟出同一植被在光照区和阴影区的辐亮度，分析阴影对NDVI的影响机理；利用植被固有反射率谱间关系，引入对阴影极敏感的且与植被信息相关性小的归一化暗像元指数NDPI (Normalized Dark Pixel Index)，分析同一植被处于光照区与阴影区的NDVI关系，构建以光照区植被NDVI为基准的NDVI阴影影响去除模型NSEE (NDVI Shadow-Effect-Eliminating)，并应用于Landsat 8 OLI影像进行验证。结果表明：NDVI阴影影响基本去除，阴影区NDVI接近正常值，且光照区NDVI保持稳定；有效解决了阴影导致NDVI统计直方图的偏态问题，使其更接近正态分布；与验证影像NDVI沿剖面线逐像元比对发现，植被NDVI阴影影响基本去除；均方根误差RMSE为0.067。本模型能够将本身NDVI值很低的像元与阴影导致NDVI降低的植被像元区分开，符合实际地物情况；模型基于影像自身信息，去除NDVI阴影影响的同时，有效保持了NDVI的相对空间关系；本文基于物理机理构建模型，模型表达简洁、易于应用，且仅依赖于影像自身信息，无需异源数据，计算方便且高效。

This paper presents a multispectral-image-based model for eliminating the shadow effect on NDVI. This NDVI Shadow-Effect-Eliminating model (NSEE) is derived from simulated data and then applied on two Landsat 8 OLI images (one for the experiment and one for verification). NDVI plays a key role in the multispectral remote sensing retrieval of vegetation and has been widely used in many areas. However, the shadow normally existing in remote sensing images always influences the accuracy of NDVI. These effects will be transmitted in the process of further remote sensing retrieval, thereby resulting in errors. In this case, eliminating the shadow effect on vegetation is crucial and has a positive application value and high necessity. The difference between the shaded region and non-shaded regions in an image depends on how much solar irradiance these regions have received (i.e., supposing that the shaded region receives a solar diffuse radiation whereas the bright region receives total solar radiation, including direct and diffuse ones). The total solar irradiance (E ₀), solar direct irradiance (E _d), and solar diffuse irradiance (E _f) are simulated by using MODTRAN 4.0, the typical vegetation reflectance spectra (R) are selected from the spectra library in ENVI 5.3, and the radiances of vegetations (L _R, L _R′) in the shaded and non-shaded regions are calculated (using E ₀, E _f, and R. The mechanism behind the shadow effects on the NDVI of vegetation is analyzed by using the aforementioned simulated data. A normalized dark pixel index (NDPI) that shows high sensitivity in shadow detection and low relativity to NDVI is introduced. By analyzing the relationships between two sets of simulated NDVI (under solar diffuse radiation and under total solar radiation) of the same vegetation spectrum (to simulate shaded and non-shaded situations in remote sensing image), the NSEE model of NDVI Shadow-Effect-Eliminating(NSEE) is constructed to correct the NDVI in shaded regions based on the NDVI in the bright regions of an image. The NSEE model is applied on two Landsat 8 OLI images. The results show that, the NDVI values in the shaded regions are basically corrected to be normal, whereas the NDVIs in the bright regions remain stable in both the experimental and verification images. The NSEE model can also normalize the skewness of the NDVI statistical histogram caused by the shadow effect. The NDVI values of the experimental and verification images are compared pixel by pixel along the two transect lines, and the result shows that the reduction in NDVI due to shadow is eliminated and that the NDVI in the bright region belonging to either vegetation or non-vegetation pixels remains stable. The total RMSE is 0.067, thereby validating the effectiveness of the model is effective. The NSEE model effectively eliminates the shadow effects of shadow on the NDVI of vegetations. This model can also distinguish the NDVI-decreasing pixels of NDVI-decreasing (due to shadow effects) from those pixels with relatively low original NDVI values, thereby suggesting that the model fits well with land type. This model is entirely based on the image information itself, it can effectively maintain the relative spatial relations of NDVI, and effectively eliminate the influence of shadow. The proposed NESS model is based on a physical mechanism, it is concise and can be easily applied. This model only depends on the information of the multispectral image, does not require different data sources, and shows a convenient and efficient calculation.