The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R 2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management.Sustainability 2019, 11, 6829 2 of 11 by Li et al. [13] showed that the four VIs (the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the enhanced vegetation index (EVI), and the 2-band enhanced vegetation index (EVI2)) derived from three different sensors (GF-1, HJ-1, Landsat-8) are all highly correlated with the LAI of winter wheat, and the spatial resolution must be considered in practical applications.Despite the scientific community constantly promoting relevant research, however, there still remain inconsistencies in satellite monitoring. In recent years, unmanned aerial vehicle (UAV) remote sensing technology, as the backbone of new remote sensing technology, has sprung up, and has also been favored by agricultural workers. There have been several investigations into the potential of UAV remote sensing platforms in precision agriculture research, with their advantages being that they are more flexible, portable, and active for farmland scale research, as an alternative to traditional remote sensing platforms, such as near-earth, aviation and satellite. Many scholars have used UAVs to carry multispectral and hyperspectral sensors to study crop LAI. For example, studies such as that conducted by Xia et al. [14] have shown that the modified triangular vegetation index (MTVI2) derived from UAV multispectral images has the best performance in monitoring wheat LAI. Gao et al. [15] claimed that the application of UAV hyperspectral remote sensing technology in precision agr...