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A Physics-Based Algorithm for the Simultaneous Retrieval of Land Surface Temperature and Emissivity From VIIRS Thermal Infrared Data

机译:基于物理的VIIRS热红外数据反演地表温度和发射率的算法

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Land surface temperature (LST) is a key climate variable for studying the energy and water balance of the earth surface and monitoring the effects of climate change. This paper presents a physics-based temperature emissivity separation (TES) algorithm for the simultaneous retrieval of LST and emissivity (LST&E) from the thermal infrared bands of the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS) payload. The new VIIRS LST&E product (VNP21) was developed to provide continuity with the Moderate-Resolution Imaging Spectroradiometer (MODIS) equivalent LST&E product (MxD21) product, which is available in Collection 6, and to address inconsistencies between the current MODIS and VIIRS split-window LST products. The TES algorithm uses full radiative transfer simulations to isolate the surface emitted radiance, and an emissivity calibration curve based on the variability in the surface radiance data to dynamically retrieve both LST and spectral emissivity. Furthermore, an improved water vapor scaling model was implemented to improve the accuracy and stability of the atmospheric correction for conditions with high atmospheric water vapor content. An independent assessment of the VIIRS LST retrievals was performed against in situ LST measurements over two dedicated validation sites at Lake Tahoe and Salton Sea in the Southwestern USA, while the VIIRS emissivity retrievals were evaluated with the latest ASTER Global Emissivity Dataset Version 3 (GEDv3). The bias and root-mean-square error (RMSE) in retrieved VIIRS LST were 0.50 and 1.40 K, respectively for the two sites combined, while mean emissivity differences between VIIRS and ASTER GEDv3 were 0.2%, 0.1%, and 0.3% for bands M14 ( 8.55 μm ), M15 ( 10.76 μm ), and M16 ( 12.01 μm ), respectively, with an RMSE of 1%. We further demonstrate close agreement between the MODIS and VIIRS TES algorithm LST products to within ~0.3 K difference, as opposed to the current MODIS and VIIRS split window products, which had an average difference of 3 K.
机译:地表温度(LST)是关键的气候变量,用于研究地球表面的能量和水平衡并监测气候变化的影响。本文介绍了一种基于物理学的温度发射率分离(TES)算法,用于从Suomi国家极地轨道合作伙伴的可见红外成像辐射计套件(VIIRS)有效载荷的热红外波段中同时检索LST和发射率(LST&E)。新VIIRS LST&E产品(VNP21)的开发旨在提供与中等分辨率成像光谱仪(MODIS)等效的LST&E产品(MxD21)产品的连续性,该产品可在集合6中使用,并解决了当前MODIS和VIIRS分window LST产品。 TES算法使用完整的辐射传递模拟来隔离表面发射的辐射,并使用基于表面辐射数据的可变性的发射率校准曲线来动态检索LST和光谱发射率。此外,为改善大气水汽含量高的条件下大气校正的准确性和稳定性,实施了改进的水蒸气比例模型。在美国西南部的太浩湖和萨尔顿海的两个专用验证站点上,针对原位LST测量对VIIRS LST取回进行了独立评估,同时使用最新的ASTER全球发射率数据集版本3(GEDv3)对VIIRS发射率取回进行了评估。 。检索到的VIIRS LST的偏差和均方根误差(RMSE),两个位点的总和分别为0.50和1.40 K,而VIIRS和ASTER GEDv3的平均发射率差异为0.2%,0.1%和0.3% M14(8.55μm),M15(10.76μm)和M16(12.01μm)分别具有1%的RMSE。我们进一步证明了MODIS和VIIRS TES算法LST产品之间的一致性接近0.3K以内,而当前的MODIS和VIIRS分割窗口产品的平均差异为3K。

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