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Calculation of Land Surface Emissivity and Retrieval of Land Surface Temperature Based on a Spectral Mixing Model
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.infrared.2020.103333
C.L. Yin , F. Meng , Q.R. Yu

Abstract Land surface emissivity (LSE) is a key parameter for the retrieval of land surface temperature (LST). Using the linear spectral mixing model (LSMM) to calculate the LSE can estimate the pixel composition at the sub-pixel level, which effectively solves the problem of mixed pixels when using the Landsat thermal infrared band to calculate the surface specific emissivity. In this paper, Landsat OLI/TIRS was used as the data source, surface emissivity was calculated using the LSMM of mixed pixels, and the LST was then obtained using the radiative transfer equation algorithm. At the same time, three threshold methods of the normalized difference vegetation index (NDVI) were used to calculate surface emissivity and then retrieve the LST. By comparing and analysing the inversion results and verifying the accuracy of the measured ground data, the surface emissivity and LST results obtained by the four methods were similar overall. Specifically, the maximum and average values of LST obtained by the LSMM were the highest, while those obtained by the Sobrino threshold method were the lowest, with an average difference of 0.63 °C. The difference between the LST inversion results of the LSMM and the other three methods were calculated separately, and the maximum change in LST reached 2.82 °C. All three ΔLST results showed high values in densely urbanised areas and low values in vegetation-covered areas. For urban areas with complex structures, the LSMM can be used to estimate the abundance of each component in a pixel at the sub-pixel scale, which can significantly improve the calculation accuracy of surface emissivity and lead to better LST inversion results.

中文翻译:

基于光谱混合模型的地表发射率计算与地表温度反演

摘要 地表发射率(LSE)是反演地表温度(LST)的关键参数。使用线性光谱混合模型(LSMM)计算LSE可以在亚像素级别估计像素组成,有效解决了使用Landsat热红外波段计算地表比发射率时混合像素的问题。本文以Landsat OLI/TIRS为数据源,利用混合像素的LSMM计算地表发射率,然后利用辐射传递方程算法得到LST。同时,利用归一化植被指数(NDVI)的三种阈值方法计算地表发射率,然后反演LST。通过对比分析反演结果,验证实测地面数据的准确性,四种方法获得的表面发射率和 LST 结果总体相似。具体来说,LSMM得到的LST最大值和平均值最高,而Sobrino阈值法得到的最低,平均相差0.63℃。分别计算LSMM与其他三种方法的LST反演结果的差异,LST的最大变化达到2.82℃。所有三个 ΔLST 结果都显示在密集城市化地区的值高,而在植被覆盖的地区值低。对于结构复杂的城市地区,LSMM可用于在亚像素尺度上估计像素中各分量的丰度,可显着提高地表发射率的计算精度,得到更好的LST反演结果。
更新日期:2020-08-01
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