Sagehen Creek UAS thermal sensing

Uncooled thermal infrared (TIR) imagers, commonly used on aircraft and small unmanned aircraft systems (UAS, “drones”), can provide high‐resolution surface temperature maps, but their accuracy is dependent on reliable calibration sources. A novel method for correcting surface temperature observations made by uncooled TIR imagers uses observations over melting snow, which provides a constant 0 °C reference temperature. This bias correction method is applied to remotely sensed surface temperature observations of forests and snow over two mountain study sites: Laret, Davos, Switzerland (27 March 2017) in the Alps, and Sagehen Creek, California, USA (21 April 2017) in the Sierra Nevada. Surface temperature retrieval errors that arise from temperature‐induced instrument bias, differences in image resolution, retrieval of mixed pixels, and variable view angles were evaluated for these forest snow scenes. Applying the melting snow‐based bias correction decreased the root‐mean‐square error by about 1 °C for retrieving snow, water, and forest canopy temperatures from airborne TIR observations. The influence of mixed pixels on surface temperature retrievals over forest snow scenes was found to depend on image resolution and the spatial distribution of forest stands. Airborne observations over the forests at Sagehen showed that near the edges of TIR images, at more than 20° from nadir, the snow surface within forest gaps smaller than 10 m was obscured by the surrounding trees. These off‐nadir views, with fewer mixed pixels, could allow more accurate airborne and satellite‐based observations of canopy surface temperatures. RAW and processed data can be found here: https://nevada.app.box.com/folder/172808842498非制冷热红外（Thermal Infrared, TIR）成像仪常搭载于航空器与小型无人机系统（Unmanned Aircraft Systems, UAS，俗称"无人机"），可生成高分辨率地表温度图，但其成像精度依赖可靠的定标源。本研究提出一种校正非制冷TIR成像仪地表温度观测值的新方法，该方法以融雪场景作为恒定0℃的参考温度源。 该偏差校正方法被应用于两处山地研究站点的森林与积雪地表温度遥感观测：其一为瑞士阿尔卑斯山区达沃斯拉雷特站点（2017年3月27日），其二为美国内华达山脉加利福尼亚州塞奇恩克里克站点（2017年4月21日）。针对上述森林积雪场景，研究评估了由温度诱导的仪器偏差、图像分辨率差异、混合像元反演以及可变观测视角所引发的地表温度反演误差。 相较于机载TIR观测反演积雪、水体与林冠温度的结果，采用融雪基偏差校正方法后，均方根误差（root-mean-square error）降低了约1℃。 研究发现，森林积雪场景下混合像元对地表温度反演的影响取决于图像分辨率与林分的空间分布格局。对塞奇恩林区的机载观测结果显示，在TIR图像边缘区域（观测天顶角大于20°），尺寸小于10m的林隙内积雪表面会被周边林木遮挡。这类偏离天底的观测视角因混合像元占比更低，可实现精度更高的机载与星载林冠地表温度观测。 原始数据与处理后数据可通过以下链接获取：https://nevada.app.box.com/folder/172808842498