The NDVI (Normalized Difference Vegetation Index) calculated based on the spectral reflectance is proved as one of the important parameters to estimate the chlorophyll content of crops, which indicates the growth condition of crop quickly and nondestructively. Thus, the distribution of NDVI of crops can be studied by the binocular stereo vision system with visible RGB (Red, Green, Blue) and near infrared (NIR) images. And the NDVI distribution and dynamics of crops are monitored through the image analysis at different angles. After the spatial distribution maps of crop vegetation index were established based on the matching of RGB and NIR images, the spatial distribution characteristics and influencing factors were discussed by the visualization of NDVI. The RGB and NIR images of 51 maize plants were collected synchronously by the binocular stereo vision system at 90°, 54°, 35° respectively. The RGB-NIR images were pre-processed by Gauss filtering and Laplace operator enhancement. Firstly, three algorithms, namely, SURF (Speeded-Up Robust Features), SIFT (Scale-invariant Feature Transform) and ORB (Oriented Brief), were studied and discussed for RGB-NIR image matching and alignment. Four evaluation indices wereused to determine the optimal matching methodfor RGB-NIR image matching and alignment, including matching time, PSNR (Peak Signal to NoiseRatio), MI (Mutual Information) and SSIM (Structural Similarity Index). Secondly, the crop and background were segmented by using ExG (Extra Green) algorithm and Maximum Interclass Variance algorithm (OTSU). The R (Red), G (Green), B (Blue) and NIR components of the segmented RGB images were extracted. The influence of illumination was discussed and Spectral reflectance was corrected based on the I component of HSI (Hue-Saturation-Intensity) color model. Then, the NDVI of each pixel in the image was calculated, the spatial distribution map of crop vegetation index was drawn, and the distribution characteristics of NDVI under different shooting angles were compared and analyzed. The NDVI distribution was used to display the chlorophyll distribution of crop plants. The RGB-NIR image matching results showed that the matching time with SIFT (1.865 s)>SURF (1.412 s)>ORB (1.121 s), the matching accuracy with SURF≈SIFT>ORB, and the matching stability with SURF≈SIFT>ORB. According to discussion results, the SURF algorithm was selected as the optimal matching algorithm. In order to eliminate the influence of ambient light, the image reflectance was corrected by 4 gray level standard plates on the basis of discussing the I component and gray histogram of HSI model. The R2 of R, G, B and NIR component correction models were 0.78, 0.76, 0.74 and 0.77 respectively. The vegetation index distributions of leaves and stems of crops were presented from 90 and 35 angles, which could provide new technical support for analyzing and monitoring the nutritional status and distribution of crops.