Abstract: Crop canopy temperature is one of the most important indexes for evaluating crop physiological conditions for it is closely related leaf stomatal conductance, water use, transpiration of crops. Therefore, crop canopy temperatures can be used in monitoring drought resistance traits of crop. Traditional crop canopy temperature estimates are based on artificial field measurement, which are not only time and labor consuming, but more importantly, are difficult to apply over large areas. In recent years, with the rapid development of unmanned aerial vehicle (UAV) technology, UAVs have been widely used in agricultural phenotypic data acquisition. However, when using thermal infrared image data to extract crop canopy temperatures, UAV is equipped with a miniaturized thermal infrared instrument with low spatial resolution due to the limited load capacity of the UAV. Thermal infrared images often cannot effectively separate soil and crops individually, thus reducing the accuracy of extracting crop canopy temperatures. In this paper, a low-altitude UAV remote sensing system equipped with a thermal infrared imager and a RGB high-definition digital camera was adopted for remote sensing data collection. Experiments were conducted at the Xiao Tangshan National Precision Agriculture Research Center of China, which is located in Changping District of Beijing, PR China. A total of 800 maize materials with different varieties in the jointing period were collected. While UAV acquiring images, a total of 72 ground samples were measured using a hand-held thermometer portable, which was used to verify the maize canopy temperature results from thermal infrared images. The RGB high-definition digital orthophoto map (DOM) was generated and geometry was corrected using ground control points (GCPs) and digital camera images in Agisoft's PhotoScan. A RGB high-definition DOM was used as base an image for the thermal infrared images geometrical calibration to solve the problem of coordinate mismatch between thermal infrared images and digital images, facilitating the removal of the soil background in the next step. The temperature of the black and white surface of the radiant correction plate was measured by a hand-held thermometer portable before and after the flight, which was used for the radiation calibration of the thermal infrared image. A high-resolution digital image was used to calculate the red-green ratio index (RGRI), and the image was binarized after classification of maize and soil. Then the binarization result was used to generate a maize mask file for the experimental area, which was used to extract the pixels of the maize vegetation on the thermal infrared image. By doing so, the low spatial resolution thermal infrared image pixels were separate into soils and crops parts, and crop canopy temperature was extracted from the crops. Finally, the canopy temperature of maize with different characters in the experimental area was statistically analyzed. And the consistency with observations on the ground was analyzed to verify and evaluate the effect of the maize canopy temperature extraction based on thermal infrared images. Our results indicated that canopy temperature based on thermal infrared images was highly relevant with ground observations with R2=0.723 6, RMSE=0.60 ℃. Our results demonstrated that it was accurate and feasible to use the high spatial resolution digital images to remove the soil background pixels on low spatial resolution thermal infrared images. In addition, the new method of obtaining maize canopy temperature based on UAV thermal infrared imagery was feasible and effective. Our results also demonstrated that the canopy temperature of materials was significantly correlated with the canopy coverage with R2=0.534 5 (P< 0.000 1). Canopy temperature decreased with increasing of canopy coverage.