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首页> 美国卫生研究院文献>Data in Brief >Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window split window algorithm and spectral radiance model
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Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window split window algorithm and spectral radiance model

机译：利用单窗口分割窗口算法和光谱辐射率模型对埃塞俄比亚十二个Wereda地表温度响应植被指数的时间序列分析数据

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In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as Split-window, Mono-Window (SW), Single-Channel (SH), among others. LST was estimated using Mono-Window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation.
机译：过去，传统上是使用气象数据进行年代际时间序列分析的。特别是，由于缺乏遥感技术，对地表温度进行年代际分析一直是一个主要问题。但是，如今，随着遥感技术和现代软件的最新发展，可以通过热带计算出陆面温度（LST）。 LST可以通过许多算法来估算，例如拆分窗口，单窗口（SW），单通道（SH）等。 LST是使用Landsat-5 TM上的Mono-Window算法，Landsat-7 ETM +以及Landsat-8 OLI / TIRS热红外（TIR）波段上的分割窗口算法估算的。通过使用归一化差异植被指数（NDVI）从红色和近红外（NIR）波段获得植被指数。 NDVI已被有效地用于植被监测和分析植被以响应气候变化（例如地表温度变化）。选择了埃塞俄比亚极易发生干旱的十二个Weredas（第三级行政区划）来调查年代际地表温度变化及其对周围环境的影响，特别是对植被的影响。基本数据源使用了1999年至2018年的三个不同传感器的10个Landsat图像。处理后的地表温度与植被指数显示出很强的相关性。较高的LST值表示NDVI较小，反之亦然，并且还可以识别出高温地区为贫瘠地区，而低温地区则被更多的植被覆盖。

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