Abstract: Abstract: The canopy temperature can indicate crop water stress condition, but the traditional method of canopy temperature measurement is based on ground point measurement, time-consuming and laborious. The calculation of crop water stress index (CWSI) requires more ground data monitoring and the crop water deficit physiological indicators are also difficult to measure in specific practical application, so it is difficult to popularize widely in agriculture. With the rapid development of unmanned aerial vehicle (UAV) platforms and thermal infrared sensors, the thermal infrared technology of UAV can monitor crop canopy temperature quickly and effectively and obtain a large area of crop canopy temperature with fast, dynamic monitoring, all-weather operation advantages, and further diagnose the water stress condition of agricultural crops. To solve the problem of low precision of the current UAV thermal infrared remote sensing for the diagnosis of crop water stress, in this paper, the 4 water treatment plots I1, I2, I3 and I4 with 50%, 65%, 80% and 95%-100% of field holding water as the upper limit were set up, each treatment plot had 3 repeated tests, a total of 12 treatment plots, and the cotton at flower boll period under 4 kinds of water treatments was selected as the test object. A six-rotor unmanned aerial vehicle was used to carry the thermal infrared sensor, and the high resolution thermal infrared images of cotton canopy at 13 o'clock at noon were collected in 5 consecutive days. First, the custom coordinates for thermal infrared images are defined in order to make the image have the same operating position, and Canny edge detection algorithm is used to obtain the edge feature raster image of cotton canopy. Then, the edge feature raster images are processed in ArcGIS and ENVI (environment for visualizing images) software to obtain a polygon vector layer, including the cotton canopy edge feature raster image converting to polyline vector layers and the polyline vector layers converting to polygon vector layers. Finally, use the polygon vector layer to clip, mask statistics and draw the canopy temperature histogram. Through the Canny edge detection algorithm, the thermal infrared image of the soil background is effectively removed, and the application of canopy temperature histogram verifies the elimination effect. The number, position and size of the original temperature histogram's peaks correspond to different matter pixels. If the temperature histogram is a single peak histogram for the high coverage crop canopy, the crop canopy with low coverage will be characterized by double or triple peaks. According to the canopy temperature histogram, the temperature characteristics of cotton canopy are calculated, including standard deviation of canopy temperature (CTSD) and canopy temperature coefficient of variation (CTCV). The relationships between cotton canopy temperature characteristics and cotton leaf stomatal conductance (Gs), transpiration rate (Tr), crop water stress index (CWSI) and soil water content (SWC) were studied, and the applicability of canopy temperature characteristics to diagnose cotton water stress was analyzed. The results showed that the cotton canopy temperature characteristics were correlated with the physiological and physical indices representing cotton water stress, and the maximum determining coefficient (R2) was 0.88. The determining coefficients of CTSD and CTCV with Gs, Tr, CWSI and SWC were 0.884, 0.625, 0.673, 0.550 and 0.853, 0.583, 0.620, 0.520, respectively. The CTSD was more sensitive to crop water stress condition, which could be used as a new index to diagnose crop water stress. However, the correlation between CTCV and physiological and physical indicators of crop water deficit is not ideal. Compared with CWSI, Gs, Tr and SWC which are traditional diagnostic indicators of crop water stress, this method greatly simplifies the calculation of crop diagnostic indicators. In this research, it suggests that the calculation method of canopy temperature characteristics only needs the thermal infrared image data of UAV and does not require any meteorological factors, so the canopy temperature characteristics are more easily promoted for use in agricultural water-saving irrigation. It has great potential to be used in the diagnosis of crop water stress compared with other water stress indices.