scholarly journals Determination of Regional Land Surface Heat Flux Densities over Heterogeneous Landscape of HEIFE Integrating Satellite Remote Sensing with Field Observations.

2002 ◽  
Vol 80 (3) ◽  
pp. 485-501 ◽  
Author(s):  
Yaoming MA ◽  
Osamu TSUKAMOTO ◽  
Hirohiko ISHIKAWA ◽  
Zhongbo SU ◽  
Massimo MENENTI ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Land Surface ◽  
Surface Heat Flux ◽  
Satellite Remote Sensing ◽  
Surface Heat ◽  
Field Observations ◽  
Heterogeneous Landscape ◽  
Regional Land

Determination of regional land surface heat fluxes over a heterogeneous landscape of the Jiddah area of Saudi Arabia by using Landsat-7 ETM data

2007 ◽  
Vol 21 (14) ◽  
pp. 1892-1900 ◽  
Author(s):  
Yaoming Ma ◽  
Hui Tian ◽  
Hirohiko Ishikawa ◽  
Ryohji Ohba ◽  
Hiromasa Ueda ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Land Surface ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Surface Heat Fluxes ◽  
Heterogeneous Landscape ◽  
Landsat 7 ◽  
Regional Land ◽  
Land Surface Heat Fluxes

Surface heat transfer coefficient measurement by immersed temperature sensor and inverse modelling

2016 ◽  
Vol 83 (11) ◽  
Author(s):  
Mirko Javurek ◽  
Andreas Mittermair
Keyword(s):  
Heat Flux ◽  
Inverse Problem ◽  
Flux Density ◽  
Temperature Sensor ◽  
Surface Heat Flux ◽  
Heat Flux Density ◽  
Surface Heat ◽  
Problem Solution ◽  
Inverse Problem Solution

AbstractA transient surface heating or cooling process of a solid is considered. A procedure for the determination of surface temperature and surface heat flux density during such a process is presented using a submersed temperature sensor in the solid. From this measured temperature the surface temperature and surface heat flux density are calculated by inverse process modelling. This method is prone to errors since measurement errors are amplified in the inverse process modelling and can thus easily become unacceptably large. The LSQR regularisation algorithm is optimised for fast performance as well as less memory requirement and applied to the inverse problem solution. The proposed method allows to simulate an experimental setup and to determine the accuracy of the results gained from the simulated experiment. This is essential for the determination of the accuracy of a planned or existing test facility. The influence of process parameters like sensor depth, sensor noise level, sampling rate, heat flux density amplitude and cooling/heating process duration is investigated. In most cases it is very important to carefully adjust the process parameters in order to obtain reliable and accurate results. Additionally the proper selection of the regularisation parameter required for the inverse problem solution is analysed.

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