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Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces

机译:土壤水分对裸露表面温度随时间变化的敏感性研究

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摘要

Land surface soil moisture (SSM) is a fundamental variable in the hydrological cycle and is an important parameter in investigations on water and energy balances at the Earth's surface. Many efforts have been made to derive SSM from remotely sensed thermal infrared data. Using the Noah land surface model (LSM) and the Gaussian emulation machine for sensitivity analysis (GEM-SA) software, a sensitivity study was conducted for bare soil to investigate the interrelationship between the evolution of land surface temperature (LST) and SSM. Based on the diurnal cycles of LST and net surface shortwave radiation, eight parameters intuitively related to SSM were defined, and a sensitivity analysis (SA) was performed in the presence and absence of atmospheric variation. The results provided insight into the relationships between the eight parameters and various environmental factors such as soil physical parameters, soil moisture, albedo, and atmospheric parameters. For instance, the results suggested that the surface air temperature had a significant effect on the LST, especially the maximum, minimum, and average daytime temperatures. For a given atmospheric forcing data set, the LST rising rate normalized by the difference in the net surface shortwave radiation during the mid-morning (T_N) was the parameter most sensitive to the SSM, contributing 80.72% to the total variance. In addition, the time at which the daily maximum temperature occurred (t_d), the daily minimum temperature, and the LST nocturnal decay coefficient were strongly related to the soil type. Using a linear combination of T_N and t_d a method was proposed to retrieve the SSM, and the coefficients of the linear model were found to be independent of the soil type for a given atmospheric condition. Compared with the actual SSM values used in the Noah LSM simulation, the root mean square error (RMSE) of the SSM retrieved from our proposed method was within 0.04 m~3 m~(-3) for all the 20 clear days evaluated in the present study.
机译:地表土壤水分(SSM)是水文循环中的基本变量,并且是研究地球表面水和能量平衡的重要参数。为了从遥感的热红外数据中导出SSM,已经做了很多努力。使用Noah地表模型(LSM)和高斯仿真机进行敏感性分析(GEM-SA)软件,对裸土进行了敏感性研究,以研究地表温度(LST)和SSM演变之间的相互关系。基于LST和净表面短波辐射的昼夜周期,定义了与SSM直观相关的八个参数,并在存在和不存在大气变化的情况下进行了敏感性分析(SA)。结果提供了对这八个参数与各种环境因素(例如土壤物理参数,土壤湿度,反照率和大气参数)之间关系的深入了解。例如,结果表明,地面空气温度对LST有显着影响,尤其是白天的最高,最低和平均温度。对于给定的大气强迫数据集,通过早晨中间的净表面短波辐射差异(T_N)归一化的LST上升速率是对SSM最敏感的参数,占总方差的80.72%。另外,日最高温度发生的时间(t_d),日最低温度和LST夜间衰变系数与土壤类型密切相关。使用T_N和t_d的线性组合,提出了一种检索SSM的方法,并且发现线性模型的系数与给定大气条件下的土壤类型无关。与Noah LSM模拟中使用的实际SSM值相比,我们在20年中评估的所有20个晴天,从我们的方法中检索到的SSM的均方根误差(RMSE)在0.04 m〜3 m〜(-3)以内。目前的学习。

著录项

  • 来源
    《International journal of remote sensing 》 |2013年第10期| 3314-3331| 共18页
  • 作者

    Wei Zhao; Zhao-Liang Li;

  • 作者单位

    State Key Laboratory of Resources Environment Information System, Institute of Geographic Sciences Natural Resources Research, Beijing 100101, China,LSIIT, UdS, CNRS, Illkirch 67412, France,Graduate University of Chinese Academy of Sciences, Beijing 100049, China;

    State Key Laboratory of Resources Environment Information System, Institute of Geographic Sciences Natural Resources Research, Beijing 100101, China,LSIIT, UdS, CNRS, Illkirch 67412, France;

  • 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
  • 关键词

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