Accurate estimates of fractional vegetation cover (FVC) using remotely sensed images collected using unmanned aerial vehicles (UAVs) offer considerable potential for field measurement. However, most existing methods, which were originally designed to extract FVC from ground-based remotely sensed images (acquired at a few meters above the ground level), cannot be directly used to process aerial images because of the presence of large quantities of mixed pixels. To alleviate the negative effects of mixed pixels, we proposed a new method for decomposing the Gaussian mixture model and estimating FVC, namely, the half-Gaussian fitting method for FVC estimation (HAGFVC). In this method, the histograms of pure vegetation pixels and pure background pixels are firstly fit using two half-Gaussian distributions in the Commission Internationale d'Eclairage (CIE) L*a*b* color space. Then, a threshold is determined based on the parameters of Gaussian distribution to generate a more accurate FVC estimate. We acquired low-altitude remote-sensing (LARS) images in three vegetative growth stages at different flight altitudes over a cornfield. The HAGFVC method successfully fitted the half-Gaussian distributions and obtained stable thresholds for FVC estimation. The results indicate that the HAGFVC method can be used to effectively and accurately derive FVC images, with a small mean bias error (MBE) and with root mean square error (RMSE) of less than 0.04 in all cases. Comparatively, other methods we tested performed poorly (RMSE of up to 0.36) because of the abundance of mixed pixels in LARS images, especially at high altitudes above ground level (AGL) or in the case of moderate vegetation coverage. The results demonstrate the importance of developing image-processing methods that specially account for mixed pixels for LARS images. Simulations indicated that the theoretical accuracy (no errors in fitting the half-Gaussian distributions) of the HAGFVC method reflected an RMSE of less than 0.07. Additionally, this method provides a useful approach to efficiently estimating FVC by using LARS images over large areas.