Remote Sensing Applications and Decision Support

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

[+] Author Affiliations
Nana Li

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Beijing 100101, China

Tsinghua University, Department of Hydraulic Engineering, State Key Laboratory of Hydroscience and Engineering, Beijing 100084, China

Joint Center for Global Change Studies, Beijing 100875, China

Li Jia, Jing Lu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Beijing 100101, China

Joint Center for Global Change Studies, Beijing 100875, China

Massimo Menenti

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Beijing 100101, China

Delft University of Technology, Department of Geosciences and Remote Sensing, Stevinweg 1, Delft 2628 CN, The Netherlands

Jie Zhou

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, State Key Laboratory of Remote Sensing Science, Beijing 100101, China

J. Appl. Remote Sens. 11(1), 016028 (Feb 17, 2017). doi:10.1117/1.JRS.11.016028
History: Received April 20, 2016; Accepted January 10, 2017
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Abstract.  The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as “HM model”) and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situG0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from 7 to 0.5  K in LST amplitude and from 300 to 300  Jm2K1s0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Nana Li ; Li Jia ; Jing Lu ; Massimo Menenti and Jie Zhou
"Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin", J. Appl. Remote Sens. 11(1), 016028 (Feb 17, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.016028


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