利用Landsat 8图像测定地球表面温度:对格拉纳达市算法的比较研究

IF 0.4 Q4 REMOTE SENSING Revista de Teledeteccion Pub Date : 2021-07-21 DOI:10.4995/RAET.2021.14538
David Hidalgo-García
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引用次数: 0

摘要

近几十年来,使用卫星图像已成为确定地表温度(LST)的最常用方法之一。其中之一是通过使用Landsat 8图像,这需要使用单通道(MC)和双通道(BC)算法。在本研究中,通过使用五种Landsat 8算法确定了中等城市格拉纳达(西班牙)一年的地表温度,然后将其与环境温度进行比较。很少有研究将数据源与同一大都市的季节变化进行比较,再加上其地理位置、高污染和显著的热变化,使其成为本研究开展的合适场所。通过统计分析,得到回归系数R2、均方误差(RMSE)、平均误差偏差(MBE)和标准差(SD)。得到的平均结果表明,与MC(-5.6°C)相比,BC算法得出的LST(1.0°C)最接近环境温度,尽管根据城市的覆盖范围和季节周期,城市不同区域之间已经证实了重要的变化。因此,BC算法最适合于恢复所研究城市的地表温度。
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Determinación de la temperatura de la superficie terrestre mediante imágenes Landsat 8: Estudio comparativo de algoritmos sobre la ciudad de Granada
The use of satellite images has become, in recent decades, one of the most common ways to determine the Land Surface Temperature (LST). One of them is through the use of Landsat 8 images that requires the use of single-channel (MC) and two-channel (BC) algorithms. In this study, the LST of a medium-sized city, Granada (Spain) has been determined over a year by using five Landsat 8 algorithms that are subsequently compared with ambient temperatures. Few studies compare the data source with the seasonal variations of the same metropolis, which together with its geographical location, high pollution and the significant thermal variations it experiences make it a suitable place for the development of this research. As a result of the statistical analysis process, the regression coefficients R2, mean square error (RMSE), mean error bias (MBE) and standard deviation (SD) were obtained. The average results obtained reveal that the LST derived from the BC algorithms (1.0 °C) are the closest to the ambient temperatures in contrast to the MC (-5.6 °C), although important variations have been verified between the different zones of the city according to its coverage and seasonal periods. Therefore, it is concluded that the BC algorithms are the most suitable for recovering the LST of the city under study.
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来源期刊
Revista de Teledeteccion
Revista de Teledeteccion REMOTE SENSING-
CiteScore
1.80
自引率
14.30%
发文量
11
审稿时长
10 weeks
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