Unoccupied aerial vehicles (UAVs) equipped with thermal cameras show great promise for precision agriculture, but challenges persist in analyzing land surface temperature (LST). This study explores the influence of ambient environmental conditions and intrinsic characteristics of uncooled thermal cameras on the accuracy of temperature measurements obtained through UAV-based thermal cameras. The research utilized DJI Matrice 210 quad-rotor UAVs equipped with FLIR Tau 2 and WIRIS 2nd Gen thermal cameras. The experimental design involved strategically selected temperature reference materials of diverse compositions. UAV flights were conducted at varying altitudes, capturing thermal images correlated with ground-based thermocouple measurements. Results indicate that increasing flight altitude resulted in underestimated temperatures measured by UAVs for objects with higher kinematic temperatures, while objects with lower temperatures displayed higher measurements. The study integrates multiple environmental metrics, illustrating the complex influence of air temperature, humidity, net radiation, and wind speed on temperature measurements, with variations observed between FLIR Tau 2 and WIRIS 2nd Gen camera models. Linear regression models highlight the diverse impact of these metrics on UAV-based temperature observations. Furthermore, an analysis of uncooled thermal sensor characteristics reveals a correlation between UAV-measured temperatures and the focal plane array (FPA) temperature, emphasizing the importance of considering intrinsic sensor dynamics. These findings provide valuable insights for enhancing the reliability of UAV-based thermal measurements in agricultural and environmental monitoring. The research underscores the necessity for a comprehensive understanding of both ambient conditions and camera-model-specific dynamics to optimize thermal imaging accuracy for precision agriculture applications. Accordingly, the recommended procedures have been described to reduce the effect of identified sources of influence.

中文翻译：

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在精准农业的田间条件下优化基于无人机的非制冷热像仪


配备热像仪的无人飞行器 （UAV） 在精准农业方面显示出巨大的前景，但在分析地表温度 （LST） 方面仍然存在挑战。本研究探讨了周围环境条件和非制冷型热像仪的内在特性对通过无人机型热像仪获得的温度测量精度的影响。该研究使用了配备 FLIR Tau 2 和 WIRIS 第 2 代热像仪的 DJI Matrice 210 四旋翼无人机。实验设计涉及战略性地选择不同成分的温度参考材料。无人机在不同的高度飞行，捕获与地面热电偶测量相关的热图像。结果表明，飞行高度的增加导致无人机测得的运动温度较高时被低估，而温度较低的物体则显示较高的测量值。该研究整合了多个环境指标，说明了空气温度、湿度、净辐射和风速对温度测量的复杂影响，并在 FLIR Tau 2 和第 2 代 WIRIS 相机型号之间观察到了差异。线性回归模型突出了这些指标对基于 UAV 的温度观测的不同影响。此外，对非制冷热传感器特性的分析揭示了无人机测量的温度与焦平面阵列 （FPA） 温度之间的相关性，强调了考虑本征传感器动力学的重要性。这些发现为提高农业和环境监测中基于无人机的热测量的可靠性提供了有价值的见解。 该研究强调了全面了解环境条件和相机特定型号动力学的必要性，以优化精准农业应用的热成像精度。因此，已经描述了推荐的程序，以减少已确定的影响来源的影响。