Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces
Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces
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DOI：
10.1080/01431161.2012.716532
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发表时间：
2013-05
影响因子：
3.4
通讯作者：
Wei Zhao;Z. Li
中科院分区：
文献类型：
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作者：
Wei Zhao;Z. Li
地表土壤水分是水文循环的基本变量，是研究地表水和能量平衡的重要参数。人们已经做了许多努力，从遥感热红外数据获得SSM。利用Noah陆面模式（LSM）和Gaussian Simulation Machine for Sensitivity Analysis（GEM-SA）软件，研究了裸地地表温度（LST）演变与SSM的相互关系。基于LST和净地面短波辐射的日变化，直观地与SSM相关的8个参数被定义，并进行了敏感性分析（SA）的存在和不存在的大气变化。研究结果揭示了这8个参数与土壤物理参数、土壤水分、水分、大气参数等环境因子之间的关系。例如，结果表明，地面气温对LST有显着影响，尤其是最高、最低和白天平均气温。对于一个给定的大气强迫数据集，LST上升率归一化的净地面短波辐射在上午（TN）的差异是最敏感的参数SSM，贡献80.72%的总方差。此外，日最高温度发生的时间（td），日最低温度，和LST夜间衰减系数有很强的土壤类型。利用TN和td的线性组合，提出了一种方法来检索SSM，和线性模型的系数被发现是独立的土壤类型为给定的大气条件。与Noah LSM模拟中使用的实际SSM值相比，从我们提出的方法中检索的SSM的均方根误差（RMSE）在本研究中评估的所有20个晴天中均在0.04 m3 m-3以内。
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 m3 m−3 for all the 20 clear days evaluated in the present study.