Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications
Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications
复制标题
提高无人机系统热红外遥感的精度，用于农业应用中的能量平衡模型
作者：
Mitch S Maguire;C. Neale;W. Woldt
近年来，无人机遥感技术得到了迅速发展，一些适合无人机集成的多光谱和热红外传感器得到了发展。遥感热红外图像已被用于检测作物水分胁迫，并通过利用水分胁迫植物增加的热特征来管理灌溉。适用于无人机遥感的热红外相机通常是非冷却的微辐射热计。这种类型的热像仪受到不精确的影响，而不典型地出现在冷却热像仪中。此外，大气干扰也可能导致地表温度测量不准确。在这项研究中，使用集成了FLIR Duo Pro R （FDPR）热像仪的无人机收集了玉米和大豆田的热图像，其中包含12个红外温度计（IRT）来测量表面温度。通过比较UAS FDPR热图像的表面温度测量值和经过发射率和大气干扰校正的现场红外热图像，确定FDPR热图像的精度。大气干扰校正后的UAS FDPR和IRT表面温度测量值的比较得出RMSE为2.24摄氏度，R2为0.85。校正UAS FDPR热图像的其他方法探索了线性、二阶多项式和人工神经网络模型。这些模型简化了UAS FDPR热成像的校正过程。所有三种模型都表现良好，线性模型的RMSE为1.27摄氏度，R2为0.93。实验室实验也已完成，以测试FDPR热像仪随时间的测量稳定性。这些实验发现，热像仪需要一段预热时间来实现热测量的稳定性，随着预热时间的增加，热测量的准确性可能会提高。
Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.