Extraction and accuracy evaluation of cotton canopy temperature under drip irrigation based on uav thermal infrared remote sensing
[Objective]To increase the accuracy of canopy temperature extraction derived from thermal infrared imagery of drip irrigated cotton in Xinjiangin the hope of providing a technical support for precise water status monitoring.[Methods]Different soil moisture contents were set at cotton seedling and squaring sta-ges.The thermal infrared images of different treatments were acquired by using UAV,and the temperature of ra-diation calibration plate in plot and water in the bucket were measured by using a portable handheld thermome-ter.For the above information,the latter temperature was used to calibrate the former temperature extracted from thermal imagery.The Otsu and Canny edge detection algorithms were used to mask thermal infrared ima-ges and remove soil background.Cotton canopy temperature was extracted by region of interest(ROI)and band math,and then the canopy temperature frequency histograms were plotted and optimized.Meanwhile,the actual cotton canopy temperature was obtained from a portable handheld thermometer.The consistency analysis was performed between actual canopy temperature and extracted canopy temperature to calibrate the accuracy of ex-tracted temperature from thermal imagery.[Results]Canny edge detection algorithm eliminated soil back-ground and extracted canopy image with higher accuracy than Otsu algorithm(91.90%>82.52%、92.76%>80.60%),which reached the best effect.The canopy temperature histograms constructed by Otsu algorithm and Canny edge detection algorithm after removing soil background are skewed,but the canopy temperature histo-grams constructed by Canny edge detection algorithm after removing soil background were smoother and less noisy than Otsu algorithm,and the average canopy temperature of Canny edge detection algorithm in two years was the lowest(29.95,30.54℃),with the smallest difference from the measured temperature(2.78,3.43℃).Correlation analysis showed that the extracted canopy temperature based on Canny edge detection algorithm had the highest correlation with the measured temperature(r = 0.94,0.95)and the lowest RMSE(1.59,1.43℃),where the 1%temperature information at both ends of the temperature histogram of Canny edge de-tection algorithm was dislogded.[Conclusion]The Canny edge detection algorithm improves the precision of cotton canopy temperature extraction from UAV thermal infrared images,and the optimization of 1%tempera-ture at both ends of the temperature histogram is helpful to improve the precision of cotton canopy temperature extraction.
国家科技期刊平台
NSTL
万方数据
