Abstract: Abstract: Fast and accurate prediction of crop yield at field scale is an effective way to optimize agricultural management by government or local farmers for improving agriculture production. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) remote sensing monitoring system has some advantages, such as obtaining images at high spatial resolution rapidly and cost-effectively, and flying under the clouds at low altitude. The complex equations and methods were commonly used to improve accuracy of yield prediction, but lacked quickness and simplicity. Thus, the object of this study was to: 1) Explore the optimal vegetation index (VI) and operation time to enhance the accuracy and quickness of yield prediction by wing-fixed UAV during wheat growing season. 2) Verify and improve the applicability of this method based on satellite remote sensing to UAV remote sensing research. The study was carried out 3 times i.e. from green to jointing stage, from the heading to filling stage, and the maturation stage during winter wheat growing season in 2016 in Binzhou City, which is in northwestern Shandong Province. In order to get stable winter wheat canopy multi-spectral datum, the cloudless and calm weather with better lighting conditions were selected to conduct the monitoring. Whiteboard data were collected before each monitoring event for later radiation correction. UAV remote sensing images with a spatial resolution of 0.16 m were generated after radiation correction, image mosaic and orthographical correction. In addition, 9 common vegetation indices (VIs) were calculated from green, red, red edge and near-infrared images, including EVI2 (enhanced vegetation index without a blue band), MSAVI2 (modified secondary soil adjusted vegetation index), OSAVI (optimized soil adjusted vegetation index), NDVI (normalized difference vegetation index), SAVI (soil adjusted vegetation index), MCARI (modified chlorophyll absorption ratio index), MTVI1 (modified triangular vegetation index), GNDVI (green normalized difference vegetation index) and MSR (modified simple ratio). Models of VIs and measured yield were obtained using the least squares regression method. To assess validity and generalization of the model, we validated models via the leave-one-out cross validation procedure which is applicable to small sample data. The measured yield data and UAV remote sensing data of wheat showed the spatial heterogeneity of different field yields and VIs were significant, so the samples have a good representation. Analysis of the multi-period UAV remote sensing images showed that R2 values of 6 models reached 0.70 following the order of EVI2 > MSAVI2 > SAVI > MTVI1 > MSR > OSAVI. And corresponding RMSE (root mean square error) value of them followed the order of EVI2 < MSAVI2 < SAVI < MTVI1 < MSR < OSAVI. Moreover, due to remote sensing images with very high resolution, soil pixels could be filtered to gain pure vegetation pixels by threshold filtering method. Data in mature stage weren't suitable for prediction because of senescent leaves and lack of chlorophyll, so they were excluded. The soil filtered result showed the R2 (n=34) of yield estimation was increased from about 0.20 to over 0.30 from the green to jointing stage, and corresponding RMSE and mean relative error were decreased. Although the R2 of yield prediction models was not changed obviously from heading to filling stage, RMSE and mean relative error of them decreased remarkably. In summary, the heading-filling period was the optimal period for winter wheat yield prediction with VIs at a single stage, and corresponding optimal VI was EVI2 with R2 (n=34) value of 0.73, and RMSE value of 579.93 kg/hm2. We concluded that the traditional statistical regression method of crop yield and vegetation index is also suitable for UAV remote sensing, and optimal yield prediction model based on EVI2 can diagnose and assess the growth and yield of winter wheat quickly and accurately, which can provide a practical and high-efficiency way at low latitude for large-scale agricultural planting and management.