Abstract: Accurate acquisition of crop information is very important for the real-time monitoring of crop growth status in crop breeding and agricultural precision management. The ever-increasing remote sensing (RS) and Unmanned Aerial vehicles (UAV) have been widely used to collect the big phenotypic data (especially image data) of various plants in a large area. Specific sensing devices are often used to obtain accurate and comprehensive information on crop growth. However, the diversity of sensing devices can bring a great challenge to the cooperation between them on UAV. It is very urgent to establish UAV flight parameters suitable for the complex field environment. In this study, an effective approach was developed to explore the effects of UAV flight height on the estimated fractional vegetation cover (FVC) and vegetation index (VI). The UAV can ultimately be utilized to collect the phenotypic data within the effective range under the multi-sensor combinations based on suitable flight height. The multispectral and high-resolution RGB cameras were simultaneously mounted on the UAV to acquire the images at a speed of 2.5 m/s. Among them, the heading and the side overlaps were set as 75% and 60%, respectively, particularly for the possibility of successful image stitching. The initial flight heights were set as 25 and 50 m, in order to exclude the wind field generated by the high-speed rotation of the UAV's paddles from disturbing the crop. The high ground resolution (GR) images were collected by the UAV at the low flight height. These images were then degraded by image processing into a series of images with different GR. The simulation was finally carried out to predict the crop images obtained by the UAV at different flight heights. As such, the impact of environmental changes on image quality was reduced, such as the light intensity. The FVC of every sample plot was estimated using Random Forest (RF). The results show that the vegetation classification accuracy was greater than 91%. The regular FVC changes were found in the different GR, due to the image blending phenomenon. Once the FVC was less than half, the indicator was constantly underestimated with the decrease of the GR. Otherwise, the indicator was overestimated. Pearson correlation analysis was also performed on the real and simulated images at a flight height of 50 m, in order to verify the feasibility of simulated images for FVC estimation. The results show that the Pearson correlation coefficient was 0.992 8 between the real and simulated images at a flight height of 50 m, indicating a high correlation. Moreover, a similar regular variation was also found in the FVC that was estimated by the real images. Five VIs (the combination of spectral reflectance) were calculated to enhance vegetation information and weaken non-vegetation one. The hypothesis testing was used to verify whether the vegetation indices had significant differences. The results show that significant differences (P<0.05) occurred in the RGB VIs at a ground resolution of 15 mm/pixel (i.e., the flight height of 61 m), whereas no difference (P>0.1) was found at 10 mm/pixel (i.e., flight height of 42 m). Meanwhile, the multispectral VIs were not significantly different at both ground resolutions (P>0.5). Fortunately, the flight height range within 42 m presented less impact on the acquisition of RGB spectral information. Consequently, a flight height of less than 42 m can be the optimal parameter to collect the crop information using the two devices at the same time. Anyway, the spectral and spatial information using other devices at higher flight heights can also be further investigated in the future. The finding can provide a strong reference for setting the suitable flight height to obtain the crop information, in order to reduce the operating costs using multi-sensor equipment carried by UAVs.