Abstract Land surface temperature (LST) is a vital parameter for the achievement of the surface energy budget and in thorough investigations of water cycle processes. Lightweight thermal infrared (TIR) sensors onboard unmanned aerial vehicles (UAVs) are rapidly becoming key instruments for extracting high-resolution LSTs given the flexibility they offer in capturing different scales. With this expansion, there has been increasing concern regarding the growing demand to obtain a mapping of normalized LST given the directional anisotropy (DA) of surface fine-scale emissions. To date, this topic suffers from a lack of deep analysis and practical solutions for characterizing the DA of fine-scale TIR data from UAV measurements over tree and crop canopies. In this paper, the first objective was to understand the pattern of brightness temperatures (BTs) DAs at a high spatial resolution by using UAV-based multiangle observations and three-dimensional (3D) radiative transfer model simulations. This study highlighted the need for first performing an angular normalization of the BTs of fine-scale pixels prior to any application, as these were easily affected by adjacent pixels and displayed broad spatial variability from 0.5 °C to 5.0 °C due to 3D occlusion. The second objective of the present study was to appraise the reliability of a modified kernel-driven model, in comparison to the model from which it was derived, with an additional kernel designed to mimic the adjacency effect, plus, a quadratic function used to simplify the estimate of the directional emissivity kernel. The root mean square error of the best fit between the measured UAV dataset and the modified kernel-driven model was approximately 0.65 °C, which proves its efficiency since the DA indexes of the BTs were about 1.40 °C. This outlined the role of the model to normalize from directional effects the camera image pixels and thereby deliver fine-scale BTs. In addition, results from LESS simulations also demonstrated the good performance of the modified kernel-driven model for simulating the DAs of thermal emissions for both tree and row-planted scenes. Index Terms—Land surface temperature, UAV, directional anisotropy, high spatial resolution.

中文翻译：

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基于无人机测量模拟树木和作物冠层上细尺度 TIR 发射的方向各向异性

摘要 地表温度 (LST) 是实现地表能量收支和深入研究水循环过程的重要参数。无人机 (UAV) 上的轻型热红外 (TIR) 传感器正迅速成为提取高分辨率 LST 的关键工具，因为它们在捕获不同尺度方面提供了灵活性。随着这种扩展，考虑到表面细尺度排放的方向各向异性 (DA)，人们越来越担心获得归一化 LST 映射的需求不断增长。迄今为止，该主题缺乏深入分析和实用解决方案来表征来自无人机在树木和作物冠层上测量的精细 TIR 数据的 DA。在本文中，第一个目标是通过使用基于无人机的多角度观测和三维 (3D) 辐射传输模型模拟，以高空间分辨率了解亮温 (BT) DA 的模式。这项研究强调了在任何应用之前首先对精细像素的 BT 进行角度归一化的必要性，因为这些很容易受到相邻像素的影响，并且由于 3D 遮挡而显示出从 0.5°C 到 5.0°C 的广泛空间变化。本研究的第二个目标是评估修改后的内核驱动模型的可靠性，与派生该模型的模型相比，该模型具有旨在模拟邻接效应的附加内核，以及用于简化的二次函数方向发射率核的估计。测量的无人机数据集与修改后的内核驱动模型之间的最佳拟合的均方根误差约为 0.65 °C，这证明了其效率，因为 BT 的 DA 指数约为 1.40 °C。这概述了模型从方向效应中归一化相机图像像素的作用，从而提供精细尺度的 BT。此外，LESS 模拟的结果还证明了改进的内核驱动模型在模拟树木和行种植场景的热排放 DA 方面的良好性能。索引词——地表温度、无人机、方向各向异性、高空间分辨率。这概述了模型从方向效应中归一化相机图像像素的作用，从而提供精细尺度的 BT。此外，LESS 模拟的结果还证明了改进的内核驱动模型在模拟树木和行种植场景的热排放 DA 方面的良好性能。索引词——地表温度、无人机、方向各向异性、高空间分辨率。这概述了模型从方向效应中归一化相机图像像素的作用，从而提供精细尺度的 BT。此外，LESS 模拟的结果还证明了改进的内核驱动模型在模拟树木和行种植场景的热排放 DA 方面的良好性能。索引词——地表温度、无人机、方向各向异性、高空间分辨率。