Abstract Land surface temperature (LST) values retrieved from satellite measurements in the thermal infrared (TIR) may be strongly affected by spatial anisotropy. Different parametric approaches have been proposed to simulate such effects. These are relatively simple models requiring few input data and therefore appropriate to simulate directional effects in satellite LST retrievals over large areas. The purpose of this study is to consistently evaluate the performance of two parametric models (the so-called Kernel and Hotspot models), and to assess their respective potential to correct directional effects on LST for a wide range of surface conditions, in terms of tree coverage, vegetation density, surface emissivity. We also propose an optimization of the correction of directional effects through a synergistic use of both models. The Kernel model allows an effective simulation of LST directionality associated with shadowing effects and emissivity anisotropy, but results show that it significantly underestimates the amplitude of the angular corrections. The Hotspot model performs better in simulating anisotropy related to shadowing effects. However, it is unable to account for emissivity anisotropy, showing lower performance than the Kernel model for nighttime data and for low tree coverage. The combined Kernel-Hotspot model provides corrections on LST directionality with reliable quality, with particularly improved performance during nighttime and for low tree densities.

中文翻译：

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评估参数模型纠正对局部到全球遥感 LST 的方向影响的潜力

摘要 从热红外 (TIR) 卫星测量中获取的地表温度 (LST) 值可能会受到空间各向异性的强烈影响。已经提出了不同的参数方法来模拟这种效果。这些是相对简单的模型，需要很少的输入数据，因此适用于模拟大面积卫星 LST 反演中的方向效应。本研究的目的是一致地评估两个参数模型（所谓的内核和热点模型）的性能，并评估它们各自在广泛的表面条件下纠正 LST 方向影响的潜力，就树而言覆盖率、植被密度、表面发射率。我们还建议通过协同使用两种模型来优化方向效应的校正。核模型可以有效模拟与阴影效应和发射率各向异性相关的 LST 方向性，但结果表明它大大低估了角度校正的幅度。Hotspot 模型在模拟与阴影效果相关的各向异性方面表现更好。但是，它无法考虑发射率各向异性，在夜间数据和低树木覆盖率方面表现出低于内核模型的性能。组合的 Kernel-Hotspot 模型以可靠的质量提供对 LST 方向性的校正，尤其是在夜间和低树密度下的性能得到改善。但结果表明，它大大低估了角度校正的幅度。Hotspot 模型在模拟与阴影效果相关的各向异性方面表现更好。但是，它无法考虑发射率各向异性，在夜间数据和低树木覆盖率方面表现出低于内核模型的性能。组合的 Kernel-Hotspot 模型以可靠的质量提供对 LST 方向性的校正，尤其是在夜间和低树密度下的性能得到改善。但结果表明，它大大低估了角度校正的幅度。Hotspot 模型在模拟与阴影效果相关的各向异性方面表现更好。但是，它无法考虑发射率各向异性，在夜间数据和低树木覆盖率方面表现出低于内核模型的性能。组合的 Kernel-Hotspot 模型以可靠的质量提供对 LST 方向性的校正，尤其是在夜间和低树密度下的性能得到改善。