| dc.description.abstract | Remote sensing technology has been widely applied in many aspects such as atmosphere, oceanography, hydrology, agriculture, and urban planning. The energy interaction between land surface and atmosphere is closely related to these fields of study. Therefore, increased effort has been devoted to the surface energy budget estimation from remote sensing data during the past few decades.
The proposed methodology for measuring regional surface heat fluxes, including soil heat flux, sensible heat flux and latent heat flux, by using multi-spectral remote sensing imagery and auxiliary data was based on the principle of surface energy and radiation balance. Two regional types are included in this study: the subtropical plain- Taiwan’s Chiayi Plain as represented, and the tropical mountain watershed- MaeSa Watershed in ChingMai, Thailand as represented. In Chiayi Plain, the correlation coefficient of MODIS-retrieved latent heat flux with in situ corresponding observations exceeded 0.78, and the estimation error of evaporate fraction retrieved from airborne image was even only 1%. If the spatial resolution of remote sensing data is fine enough, the model can provide high accuracy in the surface heat flux estimation. In MaeSa Watershed, three modifications, including incoming shortwave radiation, atmospheric transmissivity, and surface skin temperature, are recommended to improve the accurate evapotranspiration estimation. The correlation coefficient between our proposed model with DEM_ASTER and the in situ measurements was improved from 56% to 75%, and the RMSEs for soil heat, sensible heat and latent heat fluxes from our proposed model with DEM_ASTER were lower than those from the flat surface model and the SEBAL Mountain model.
For the surface heat flux applications, MODIS satellite-observed surface skin temperature and land surface heat fluxes were used to analyze 14 land cover types. For water bodies, vegetation, and artificial buildings, there was a gradual decrease in latent heat flux, but a gradual increase in sensible heat flux and surface temperature. The results demonstrated that enlarging wet surface such as evergreen broadleaf or water bodies can effectively reduce the temperature rising. Furthermore, we used the sensible heat flux to quantify the magnitude of urban heat island effect and compared the latent heat flux with the Global Vegetation Moisture Index (GVMI). The outcome suggested that the sensible heat flux and the latent heat flux can be considered as indicators of the urban heat island effect and the drought prediction system, respectively.
This study focused on the tropical and subtropical surface energy budget extracted from the remote sensing data, at a regional scale. The consequences will provide an important experience for continental-scale or global-scale models which can then be used for global weather prediction and transnational water management in the future.
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