| Influence of different UAV flight altitudes on the accuracy of canopy temperature extraction during drip\|irrigated cotton flowering and boll\|setting stage |
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| DOI：10.7606/j.issn.1000-7601.2026.02.08 |
| Key Words: unmanned aerial vehicle flight altitude cotton canopy temperature during the flowering and boll\|setting stage extraction accuracy drip irrigation |
| Author Name | Affiliation | | LIU Yu | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China | | CI Baoxia | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China | | GAO Xuesong | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China | | LUO Hongyang | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China | | ZHANG Lvhuo | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China | | LIU Yang | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China
National and Local Joint Engineering Research Center for Modern Agricultural Production Informatization Management and Application Technology, Xinjiang Production and Construction Corps, Shihezi, Xinjiang, 832003, China | | MA Fuyu | College of Agriculture, Shihezi University, Xinjiang Production and Construction Corps Key Laboratory of Oasis Eco\|Agriculture, Shihezi, Xinjiang 832003, China
National and Local Joint Engineering Research Center for Modern Agricultural Production Informatization Management and Application Technology, Xinjiang Production and Construction Corps, Shihezi, Xinjiang, 832003, China |
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| Abstract: |
| Taking drip\|irrigated cotton at the flowering and boll\|setting stage as the research subject, this study focuses on the impact of flight altitude on the accuracy of canopy temperature extraction. The study acquired thermal infrared images of the cotton canopy at the flowering and boll\|setting stage under water treatment intervals of 3 days, 5 days, 7 days, and 12 days per cycle at flight altitudes of 12 m, 20 m, 30 m, 50 m, and 70 m. Canopy temperature information was extracted using the top and bottom 1% elimination method, the low\|frequency 0.5% elimination method, and the low\|frequency 1% elimination method to analyze the effects of different outlier removal approaches on extraction accuracy. The results showed that for low\|altitude extraction (12~30 m), the low\|frequency 0.5% elimination method yielded the highest accuracy, with the fitted models between extracted canopy temperature and measured temperature achieving R2 values of 0.874 and 0.934, and RMSE values of 2.435 and 2.171 over two years, respectively. For high\|altitude extraction (50~70 m), the top and bottom 1% elimination method performed best, with R2 values of 0.833 and 0.914, and RMSE values of 3.904 and 3.859 over two years, respectively. A flight altitude of 30 m was identified as the optimal height for cotton canopy temperature extraction, with R2 values of 0.863 and 0.720, and RMSE values of 2.424 and 3.664 for the fitted models between image\|derived canopy temperature and measured temperature in 2023 and 2024, respectively. In summary, a study on extracting cotton canopy temperature during the flowering and boll\|setting stage was conducted using differential temperature outlier removal methods for different flight altitudes. The results indicated that combining a flight altitude of 30 m with the low\|frequency 0.5% removal method was the optimal approach for cotton canopy temperature extraction during this period. This method significantly improved the extraction accuracy of cotton canopy temperature from UAV thermal infrared imagery, with prediction accuracy increasing by 70.71% in 2023 and 4.03% in 2024. |
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