Chlorophyll content is an important parameter in plant growth and is closely related to crop yield. Unmanned aerial vehicle (UVA) remote sensing technology as a new means of data acquisition, has been widely used in agriculture. In this study, take maize as an example, two light and small multispectral sensors (MCA and Sequoia) with different spectral response functions were simultaneously mounted on a six-rotor UAV. Multispectral sensors were used to collect multispectral imagery of maize during the flowering stages under different levels of nitrogen fertilizers. At the plot level, the 26 vegetation indices based on two kinds of multi-spectral sensors were calculated and regressed with the chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) measured on the ground. The sensitivity of different band reflectivity to SPAD value were analyzed. And the accuracy and stability of SPAD values predicted by vegetation indicesbased on two multi-spectral sensors were also analyzed. The results showed that for the broadband Sequoia, the reflectance of 550 nm (green band) and 735 nm (red-edge band) is more sensitive to the change of the SPAD values, and the correlation coefficient of 550 nm and SPAD values is the largest (R2=0.802 9). For the narrowband MCA, the reflectance of 720 nm (red-edge band) has high correlation with SPAD value (R2=0.724 8), followed by the 550 nm. In addition, the correlation coefficient between the reflectance of 660 nm (Sequoia) and the SPAD value is 0.778 6, and the correlation coefficient between the reflectance of 680 (MCA) and the SPAD value is 0.488 6, which may because of the difference of central wavelength and the wavelength width. Using multi-spectral remote sensing technology of UAV to predict the SPAD values of field maize had a high accuracy, but the same vegetation index showed a great difference for different multi-spectral sensors. Among them, there were significant difference in RVI, DNVI, PVI and MSR. The broadband Sequoia is superior to the narrowband MCA. In addition, for sequoia camera, the GNDVI and RENDVI predicted the SPAD value with high accuracy, RMSE is 3.699 and 3.691, respectively. For MCA camera, RENDVI had the highest prediction accuracy (RMSE=3.742), followed by the GNDVI (RMSE=3.912). The MCARI/OSAVI with lower accuracy, the RMSE is 7.389 (Sequoia) and 7.361 (MCA). In all of the vegetation indices, the vegetation indices that using green, NIR bands and the vegetation indices constructed with red and near infrared bands were used to predict the SPAD values more accurate, which were higher than the vegetation index constructed in the red and near infrared bands. The use of complex vegetation indices constructed with more bands (three or more) did not significantly improve the prediction accuracy. For the prediction model, MCARI1 was more suitable for logarithm Model, which can effectively improve the prediction accuracy. The study also found that prediction the SPAD values in the plot level, Sequoia cameras have strong anti-jamming capability for environmental factors such as vegetation coverage, shadows and exposed soil, except for NDVI and TVI. For MCA cameras, TVI, DVI, MSAVI2, RDVI and MSAVI were very sensitive to environmental background and with a low accuracy of SPAD prediction. In addition, removal of environmental background did not always improve predictive accuracy of SPAD. This study is instructive for the prediction of high-accuracy chlorophyll content using UAV multispectral remote sensing technology, and has certain reference value for the popularization and application of precision agriculture.