4.6 Article

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

Journal

INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 34, Issue 9-10, Pages 3314-3331

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2012.716532

Keywords

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Funding

  1. European Commission, CEOP-AEGIS project [212921]
  2. National Natural Science Foundation of China [40971199]
  3. China international Science and Technology Cooperation project [0819]

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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.

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