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Spaceborne Thermal Remote Sensing for Characterization of the Land Surface Temperature of Manmade and Natural Features

机译:用于人类和自然特征的土地表面温度表征的星源载热敏遥感

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The changes in land surface temperature (LST) concerning time and space are mapped with the help of satellite remote sensing techniques. These measurements are used for determining several geophysical parameters including soil moisture, evapotranspiration, thermal inertia, and vegetation water stress. This study aims at calculating and analyzing the LST of manmade and natural features of Doon Valley, Uttarakhand, India. The study area includes the forest range of Doon Valley, agricultural areas, and urban settlements. Spaceborne multitemporal thermal bands of Landsat 8 were used to calculate the LST of various features of the study area. Split-window algorithm and emissivity-based algorithms were tested on the Landsat-8 data for LST calculation. The study also explored the effect of atmospheric correction on the temperature calculation. The land surface temperature determined using an emissivity based method that did not provide atmospheric correction was found to be less accurate as compared to the results by the split-window method. The LST for urban settlements is higher than the forest cover. A temporal analysis of the data shows an increase in the temperature for October 2018. The study shows the potential of the spaceborne thermal sensors for the multitemporal analysis of the LST measurement of manmade and natural features.
机译:若干时间和空间的陆地温度(LST)的变化是借助卫星遥感技术的映射。这些测量用于确定几种地球物理参数,包括土壤水分,蒸散,热惯性和植被水胁迫。本研究旨在计算和分析印度Doon Valley的Manad和自然特征的LST。该研究区包括Doon Valley,农业区域和城市定居点的森林范围。 Landsat 8的Spareborne Multi8型热带用于计算研究区域的各种特征的LST。在LST计算的Landsat-8数据上测试了分离窗口算法和基于发射率的算法。该研究还探讨了大气校正对温度计算的影响。使用不提供大气校正的基于发射率的方法测定的陆地表面温度被发现与通过分裂窗口方法的结果相比,不太准确。城市定居点的LST高于森林覆盖率。数据的时间分析显示了2018年10月的温度的增加。该研究表明了星载热传感器的潜力,用于MANAMADE和自然特征的LST测量的多型分析。

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