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首页> 外文期刊>International journal of remote sensing >Combining thermal inertia and a diurnal temperature difference cycle model to estimate thermal inertia from MSG-SEVIRI data
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Combining thermal inertia and a diurnal temperature difference cycle model to estimate thermal inertia from MSG-SEVIRI data

机译:结合热惯性和昼夜温差循环模型以根据MSG-SEVIRI数据估算热惯性

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摘要

Thermal inertia is an important parameter in geological and agricultural applications. In this study, we present a method that combines models of thermal inertia and the diurnal temperature difference cycle to estimate the thermal inertia from Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) data. This method can directly derive thermal inertia from MSG-SEVIRI brightness temperatures without the need to include the land surface temperature and emissivity. Two important parameters (the time of the maximum temperature and the diurnal temperature difference) that were input into the thermal inertia model were obtained by fitting the diurnal temperature difference cycle model to the diurnal cycle of land surface temperatures. The spatial distribution of thermal inertia shows that high thermal inertia values occur over vegetated areas, whereas low thermal inertia values occur over bare areas. The uncertainty in thermal inertia is investigated in terms of the uncertainties in the surface albedo, the time of the maximum temperature, and the diurnal temperature difference. The results indicate that the uncertainty in thermal inertia over vegetated areas is greater than that over bare areas. The consistency of the thermal inertia model is evaluated by analysing the difference in thermal inertia values on two consecutive days. The root mean square error of the thermal inertia differences under nearly identical surface and atmospheric conditions on two consecutive days is considered to be the error of the thermal inertia model.
机译:热惯性是地质和农业应用中的重要参数。在这项研究中,我们提出了一种结合热惯性和昼夜温差周期模型的方法,以根据Meteosat第二代旋转增强型可见光和红外成像仪(MSG-SEVIRI)数据估算热惯性。该方法可以直接从MSG-SEVIRI亮度温度推导出热惯性,而无需考虑地面温度和发射率。通过将昼夜温差周期模型拟合到地表温度的昼夜周期,获得了输入到热惯性模型中的两个重要参数(最高温度时间和昼夜温差)。热惯性的空间分布表明,在植被区域上发生高的热惯性值,而在裸露区域上发生低的热惯性值。根据表面反照率,最高温度的时间和昼夜温差的不确定性研究热惯性的不确定性。结果表明,植被区域热惯性的不确定性大于裸露区域的热惯性的不确定性。通过分析连续两天的热惯性值差异来评估热惯性模型的一致性。在连续两天的几乎相同的地面和大气条件下,热惯性差的均方根误差被认为是热惯性模型的误差。

著录项

  • 来源
    《International journal of remote sensing》 |2015年第20期|4808-4819|共12页
  • 作者单位

    Chinese Acad Agr Engn, Minist Agr, Remote Sensing Applicat Ctr, Beijing 100125, Peoples R China;

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agri Informat, Beijing 100081, Peoples R China;

    HeFei Univ Technol, Sch Comp & Informat, Hefei 230009, Peoples R China;

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agri Informat, Beijing 100081, Peoples R China;

    Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agri Informat, Beijing 100081, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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