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.

Retrieval of FY-4A Land Surface Temperature for Operational Application

2020 ◽  
Author(s):  
Lixin Dong ◽  
Na Xu ◽  
Lin Chen
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Observation Data ◽  
Ecological Civilization ◽  
New Variety ◽  
Quantitative Remote Sensing ◽  
Mean Square Errors

<p>The FY-4A satellite is the first quantitative remote sensing satellite of a three-axis stabilization structure on geostationary orbit launched by China in 2016. The capability and efficiency are improved with the new variety of instruments in earth observation. The thermal infrared (TIR) channels of Advanced Geosynchronous Radiation Imager (AGRI) which one of the new instruments onboard FY-4A provides high-frequency, high-precision, and quantitative observation data to obtain diurnal variation of land surface temperature (LST). In this paper, nine candidate split window LST algorithms were applied to evaluate the applicability of the different algorithm for FY-4A, and the Ulivieri & Cannizzaro (1985) algorithm was selected. Then different algorithms in day and night for dry and wet atmosphere conditions are developed and optimized to improve the accuracy of retrieved FY-4A LST. Then, the operational products of FY-4A LST are produced in clear sky. Results show that the diurnal variation characteristics of LST can be efficiently obtained. And the validation shows the following: 1) the root mean square errors (RMSE) is 3.5 K in day and 4.24 K in night when compared with the Himawari8 LST product; 2) compared with ground-measured data, the average accuracy of our algorithm is 2.5 K in day and 3.56 K in night. Sensitivity analysis shows that emissivity is lower sensitive to the algorithm accuracy, and the atmospheric water vapor content is higher sensitive to the algorithm accuracy with the view angle increased. In general, our algorithm exhibits good accuracy and is an easy retrieval approach to produce LST operational products on a regional scale. And the FY-4A LST operational products will help to the weather services, climate change, land–atmosphere interaction research, and ecological civilization construction, and so on.</p>

scholarly journals Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

2019 ◽  
Vol 11 (2) ◽  
pp. 155 ◽  
Author(s):  
Xiangchen Meng ◽  
Jie Cheng ◽  
Shaohua Zhao ◽  
Sihan Liu ◽  
Yunjun Yao
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Satellite System ◽  
Landsat 8 ◽  
Comparable Accuracy ◽  
Split Window Algorithm ◽  
Mean Square Errors ◽  
Radiative Transfer Modeling ◽  
Polar Satellite

Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and −0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and −0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.

scholarly journals SOIL MOISTURE ANALYSIS USING MULTISPECTRAL DATA IN NORTH CENTRAL PART OF MONGOLIA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 485-491
Author(s):  
E. Natsagdorj ◽  
T. Renchin ◽  
P. De Maeyer ◽  
B. Tseveen ◽  
C. Dari ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Research Work ◽  
Agricultural Area ◽  
Forest Steppe ◽  
Landsat Satellite

<p><strong>Abstract.</strong> Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.</p>

Plot-scale retrieval of land surface temperature and emissivity estimation

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Kaniska Mallick ◽  
Ivonne Trebs
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
The Earth ◽  
Downwelling Longwave Radiation

&lt;p&gt;The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth&amp;#8217;s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth&amp;#8217;s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using&amp;#160; satellites.&amp;#160; At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST&amp;#160; using the grey body equation :&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4&lt;/sup&gt; + (1 &amp;#8722; &amp;#949;) R&lt;sub&gt; ldw&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; &lt;/sub&gt;(1)&lt;br&gt;where R&lt;sub&gt;lup&lt;/sub&gt; is the upwelling longwave radiation, R&lt;sub&gt;ldw&lt;/sub&gt; is the downwelling longwave radiation, &amp;#949; is the surface emissivity, &lt;em&gt;T&lt;sub&gt;s&lt;/sub&gt;&amp;#160; &lt;/em&gt;is the surface temperature and &amp;#963;&amp;#160; is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to:&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4 &lt;/sup&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&amp;#160; (2)&lt;br&gt;Despite&amp;#160; widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct &amp;#949; needed for in-situ LST retrievals using tower-based measurements.&lt;br&gt;The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST&amp;#160; obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.&lt;/p&gt;

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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