scholarly journals Land Surface Temperature Retrieval of Guwahati City and Suburbs, Assam, India using Landsat Data

Author(s):  
Kamal Kumar Talukdar ◽  
2013 ◽  
Vol 785-786 ◽  
pp. 1333-1336
Author(s):  
Xiao Feng Yang ◽  
Xing Ping Wen

Land surface temperature (LST) is important factor in global climate change studies, radiation budgets estimating, city heat and others. In this paper, land surface temperature of Guangzhou metropolis was retrieved from two MODIS imageries obtained at night and during the day respectively. Firstly, pixel values were calibrated to spectral radiances according to parameters from header files. Then, the brightness temperature was calculated using Planck function. Finally, The brightness temperature retrieval maps were projected and output. Comparing two brightness temperature retrieval maps, it is concluded that the brightness temperature retrieval are more accurate at night than during the day. Comparing the profile line of brightness temperature from north to south, the brightness temperature increases from north to south. Temperature different from north to south is larger at night than during the day. The average temperature nears 18°C at night and the average temperature nears 26°C during the day, which is consistent with the surface temperature observed by automatic weather stations.


2020 ◽  
Vol 26 (2) ◽  
Author(s):  
Pâmela Suélen Käfer ◽  
Silvia Beatriz Alves Rolim ◽  
Lucas Ribeiro Diaz ◽  
Nájila Souza da Rocha ◽  
María Luján Iglesias ◽  
...  

2014 ◽  
Vol 1010-1012 ◽  
pp. 1276-1279 ◽  
Author(s):  
Yin Tai Na

The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.


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