Torres-Rua Alfonso
Utah Water Research Laboratory, Utah State University, Logan, UT 84322, USA.
Sensors (Basel). 2017 Jun 26;17(7):1499. doi: 10.3390/s17071499.
In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called "AggieAir", developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon") and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m²/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m²/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from -5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times.
近年来，与小型无人机系统（sUAS）兼容的轻型微测辐射热计热成像仪的出现，使得它们能够用于各种需要亚米级像素分辨率的科学和管理活动。然而，与已经用于温度遥感的传感器（如陆地卫星）一样，为了估算陆地表面温度，需要进行辐射大气校正。这是因为在任何sUAS飞行高度的大气条件都会对使用微测辐射热计技术的图像精度、衍生计算和研究可重复性产生不利影响。本研究提出了一种可追溯到美国国家标准与技术研究院（NIST）标准和当前大气校正方法的sUAS温度图像替代校准方法（特定于sUAS、特定于时间、特定于飞行和特定于传感器）。对于这种方法，在加利福尼亚州洛迪附近的葡萄园进行了一项为期三年的数据收集活动，使用犹他州立大学开发的名为“AggieAir”的sUAS，在不同时间（清晨、陆地卫星过境和下午中段）和季节条件下进行飞行。从这项研究的结果发现，尽管微测辐射热计相机的光谱响应为7.0至14.0μm，但无论时间和天气如何，都有可能考虑大气和sUAS运行条件的影响，以获取准确的地表温度数据。此外，还发现主要的大气校正参数（透过率和大气辐射率）在一天中显著变化。这些参数在清晨波动最大，在陆地卫星过境和下午中段部分稳定。在精度方面，估计的大气校正参数对于所有sUAS飞行都呈现出足够的统计数据（透过率的置信区间在±0.1以内，大气辐射率的置信区间在±1.2W/m²/sr/μm以内，均方根误差（RMSE）范围低于1.0W/m²/sr/μm）。此处报告的原始热图像与替代校准程序之间估计温度的差异，清晨为-5°C至10°C，陆地卫星过境和下午中段为0至20°C。
