Abstract: The small low-altitude unmanned aerial vehicle (UAV) is flexible and easy to operate, which can be used to acquire high spatial resolution images with centimeter level. It is an important technical way for phenotyping the breeding corn. In order to determine the retrieving parameters of the corn canopy leaf area index (LAI) and chlorophyll content computed by UAV images, the DJI S1000+ UAV platform with the French Parrot Sequoia camera was used to obtain multispectral images in Yacheng corn breeding base, Sanya City, Hainan Province in this study. Six different kinds of vegetation indices were used in computing the corn canopy LAI and chlorophyll content , each vegetation index was obtained from images with 10 spatial resolutions ranging from 0.1 to 1 m. The vegetation indices used in this study were the normalized difference vegetation index (NDVI), grassland chlorophyll index (GCI), ratio vegetation index (RVI), normalized difference rededge-red index (NDIrer), normalized difference rededge-green index (NDIreg) and renormalized difference vegetation index (RDVI). The correlation analysis between different vegetation indices from different resolutions images and in-situ measured LAI was done to select the optimal spatial resolution and optimal vegetation index for computing corn canopy LAI, and in similar for chlorophyll content. The study results revealed that the NDVI from image with 0.6 m spatial resolution was the optimal selection for LAI computing, where the correlation coefficient R2 between NDVI and the in-situ measured LAI was 0.80 with a R2 of 0.73 for verification. And the highest correlation coefficient R2 between NDIreg from image with 0.1m spatial resolution and in-situ measured chlorophyll content is 0.70, with a R2 of 0.63 for verification. The conclusions of this study were as followed: 1) The selection of vegetation index: ① For the corn canopy LAI computing, the vegetation indices without green band were higher than that with green band, which revealed that the green band was not sensitive to LAI; ② For corn canopy chlorophyll content computing, the vegetation indices including the red edge band were higher than that without red edge band, which revealed that the red edge band was very sensitive to chlorophyll content. 2) The spatial resolution selection for UAV image: the optimal resolution for LAI computing was 0.6 m when the correlation coefficient between NDVI and measured LAI reached the maximum; the optimal resolution for canopy chlorophyll content computing was 0.1-0.3 m when the correlation coefficient between NDIreg and in-situ measured chlorophyll content reached the maximum. This study can be used to give the reference for spatial resolution selection and vegetation index selection for corn canopy LAI and chlorophyll content computing by UAV image.