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 The Optimum Method for Urban Land Surface Temperature Estimation

2020 ◽  
Author(s):  
Ekkaluk Salakkham ◽  
Pantip Piyatadsananon
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Optimal Method ◽  
Temperature Estimation ◽  
Retrieval Method ◽  
Window Method ◽  
Split Window Algorithm

Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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 Land Surface Temperature Retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, Using a Split-Window Algorithm

2019 ◽  
Vol 11 (6) ◽  
pp. 650 ◽  
Author(s):  
Yitong Zheng ◽  
Huazhong Ren ◽  
Jinxin Guo ◽  
Darren Ghent ◽  
Kevin Tansey ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northern China ◽  
Lower Sensitivity ◽  
Ground Validation ◽  
The Usa ◽  
Split Window Algorithm ◽  
Atmospheric Transmittance ◽  
Temperature Retrieval

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.

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