scholarly journals Land Surface Temperature Retrieval from Fengyun-3D Medium Resolution Spectral Imager II (FY-3D MERSI-II) Data with the Improved Two-Factor Split-Window Algorithm

2021 ◽  
Vol 13 (24) ◽  
pp. 5072
Author(s):  
Wenhui Du ◽  
Zhihao Qin ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
Qiuyan Huang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cross Validation ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Medium Resolution ◽  
Split Window Algorithm ◽  
Spectral Imager

Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R2 of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy.

scholarly journals Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data

Sensors ◽  
2008 ◽  
Vol 8 (3) ◽  
pp. 1832-1845 ◽  
Author(s):  
Tangtang Zhang ◽  
Jun Wen ◽  
Rogier Van der Velde ◽  
Xianhong Meng ◽  
Zhenchao Li ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Thermal Data

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 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.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

1994 ◽  
Vol 99 (C3) ◽  
pp. 5219 ◽  
Author(s):  
William J. Emery ◽  
Yunyue Yu ◽  
Gary A. Wick ◽  
Peter Schluessel ◽  
Richard W. Reynolds
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

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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