Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province, Iran

2021 ◽  
Author(s):  
Arastou Zarei ◽  
Reza Shah-Hosseini ◽  
Sadegh Ranjbar ◽  
Mahdi Hasanlou
Keyword(s):  
Surface Temperature ◽  
Satellite Imagery ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Estimation ◽  
Non Linear ◽  
Tehran Province ◽  
Split Window Algorithm

scholarly journals A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5659
Author(s):  
Yue Jiang ◽  
WenPeng Lin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Thermal Infrared Remote Sensing ◽  
Retrieval Algorithms ◽  
Split Window Algorithm

In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.

A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data

2013 ◽  
Vol 34 (12) ◽  
pp. 4182-4199 ◽  
Author(s):  
Caixia Gao ◽  
Bo-Hui Tang ◽  
Hua Wu ◽  
Xiaoguang Jiang ◽  
Zhao-Liang Li
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Estimation ◽  
Split Window Algorithm

scholarly journals Use of Land Surface Temperature Estimation with Geographical Information Tools for Validation of Numerical Microclimate Studies at Urban Scale

2021 ◽  
Vol 312 ◽  
pp. 06004
Author(s):  
Mileyka Bustamante ◽  
Dafni Mora ◽  
Miguel Chen Austin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Geographical Information ◽  
Dynamic Simulations ◽  
Temperature Estimation ◽  
Historic Center ◽  
Information Tools ◽  
Landsat 7

For this study, different approaches found in the literature to obtain the Land Surface Temperature (LST) were evaluated through Geographic Information tools, to validate the temperature results obtained from dynamic simulations at urban scale with the Envi-met software. Here, the case study is an urban section of the Historic center of Panama City named Casco Antiguo. From the dynamic simulation results, the surface temperature was analyzed for March at 15:00. Concerning the results obtained for March, the Online Global Land Surface Temperature Estimation tool provided the best characteristics when validating the data obtained with Envi-met. It was found that the Landsat 7 images, applying the emissivity method ASTER and NDVI, provided data more stable and closer to the ones we wanted to validate.

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