Atmospheric sensitivity on land surface temperature retrieval using single channel thermal infrared remote sensing data: Comparison among models

2010 ◽  
Author(s):  
Limin Zhao ◽  
Qingjiu Tian ◽  
Wei Wan ◽  
Tao Yu ◽  
Xingfa Gu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Remote Sensing Data ◽  
Thermal Infrared Remote Sensing ◽  
Data Comparison ◽  
Infrared Remote Sensing ◽  
Temperature Retrieval

Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data

2014 ◽  
Vol 1010-1012 ◽  
pp. 1276-1279 ◽  
Author(s):  
Yin Tai Na
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Urban Heat ◽  
Split Window Algorithm ◽  
Temperature Retrieval

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.

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.

scholarly journals Application of Infrared Remote Sensing and Magnetotelluric Technology in Geothermal Resource Exploration: A Case Study of the Wuerhe Area, Xinjiang

2021 ◽  
Vol 13 (24) ◽  
pp. 4989
Author(s):  
Hong Chen ◽  
Xingbing Xie ◽  
Enqin Liu ◽  
Lei Zhou ◽  
Liangjun Yan
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Geothermal Resource ◽  
Magnetotelluric Data ◽  
Thermal Infrared Remote Sensing ◽  
Infrared Remote Sensing

As a new green energy source, geothermal resource’s exploration, development, and utilization are an important direction in geophysical exploration at present. In this study, the actual land surface temperature was inferred based on the thermal infrared band of Landsat8 remote-sensing images, and the information about the surface anomalies and their spatial distribution was obtained through a multifactor analysis. In addition, three magnetotelluric sounding profiles were deployed in the study area, and the geo-electric sections in the study area were obtained through inversion of the measured data. Then, based on the inverse geo-electric information and the land surface temperature anomaly information, we analyzed and verified the geothermal resource genesis of the thermal anomaly area and inferred the favorable geothermal resource area in the study area. The results show that these two methods can be used to compare and analyze the possible distribution of the geothermal resources in the study area in two dimensions: the spatial distribution on the surface and the vertical distribution in the subsurface. Moreover, the results of the geothermal anomalies inferred from the thermal infrared remote sensing and the geo-electric results inferred from the magnetotelluric data are in good agreement. This study demonstrates that the integrated application of thermal infrared remote sensing and magnetotelluric technology is a promising tool for geothermal exploration.

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