The information of crop lodging is very important for agricultural hazard assessment and agricultural insurance claims. Remote sensing is a fast and efficient technology to gain the information of crop lodging, but satellite remote sensing cannot provide available data. Recently, unmanned aerial vehicle (UAV) remote sensing system has grown rapidly, and UAV remote sensing system can get available data neatly and fleetly. There was no survey on winter wheat lodging by using multitemporal UAV remote sensing data. Therefore, a survey method of winter wheat lodging was proposed by using images derived from the UAV remote sensing experiments, which were carried out in the winter wheat test field of Institute of WaterSaving Agriculture in Arid Areas of China (IWSA), Northwest A&F University on May 4th and 16th of 2017. Images were handled with the second low pass filter firstly to enhance the image space domain. Then the scatter diagram of lodging and unlodging wheat was gained in different feature combination coordinate systems. The single features of wheat lodging information extraction based on the welldefined boundary of the scatter diagram were selected. Feature parameters F1 and F2 were gained by fitting boundary points of May 4th and 16th. Using the similarities of F1 and F2 can obtain F3 to extract winter wheat lodging information of two periods. Using F1, F2 and F3 combined with K-means to extract the lodging information of winter wheat. It was turned out that the overall accuracy was over 86.44%, the Kappa coefficient was over 0.73, and the lodging extracting accuracy was over 81.07%, so F3 can be the feature parameter to extract the lodging information of two periods. To research the accuracy and versatility of this method, two verification areas were selected and the method of this paper, support vector machine (SVM), neural network and maximum likelihood method were respectively used to extract the lodging information of winter wheat. The results showed that the overall accuracy, Kappa coefficient and lodging extracting accuracy of the method were over 86.29%, 0.71 and 80.60%, and the overall accuracy, Kappa coefficient and lodging extracting accuracy of the other common methods were 69.68%～87.44%, 0.49～0.72 and 65.33%～79.76%, respectively. The results indicated that the overall accuracy, Kappa coefficient and lodging extracting accuracy of this method were all tower over other methods. Therefore, the proposed method was accurate and versatile to extract the lodging information of winter wheat in the watery stage.