scholarly journals Correction: Rozenstein, O., et al. Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm. Sensors 2014, 14, 5768–5780

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11277-11277 ◽  
Author(s):  
Offer Rozenstein ◽  
Zhihao Qin ◽  
Yevgeny Derimian ◽  
Arnon Karnieli
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Split Window Algorithm

scholarly journals Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1778 ◽  
Author(s):  
Md Qutub Uddin Sajib ◽  
Tao Wang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cross Validation ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Parameters ◽  
Moderate Resolution ◽  
Split Window Algorithm

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1.19 ° C . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of 1.50 ° C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2.47 ° C and 4.11 ° C , respectively. For Du et al.’s algorithm, the w range of 0.0 to 6.3 g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

scholarly journals Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm

Sensors ◽  
2014 ◽  
Vol 14 (4) ◽  
pp. 5768-5780 ◽  
Author(s):  
Offer Rozenstein ◽  
Zhihao Qin ◽  
Yevgeny Derimian ◽  
Arnon Karnieli
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Split Window Algorithm

scholarly journals A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

2015 ◽  
Vol 7 (1) ◽  
pp. 647-665 ◽  
Author(s):  
Chen Du ◽  
Huazhong Ren ◽  
Qiming Qin ◽  
Jinjie Meng ◽  
Shaohua Zhao
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
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.

scholarly journals Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5049 ◽  
Author(s):  
Lei Wang ◽  
Yao Lu ◽  
Yunlong Yao
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Atmospheric Temperature ◽  
Landsat 8 ◽  
Input Parameters ◽  
Successful Launch ◽  
Urban Heat ◽  
Split Window Algorithm

The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.

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