scholarly journals USING A SPLIT-WINDOW ALGORITHM FOR THE RETRIEVAL OF THE LAND SURFACE TEMPERATURE VIA LANDSAT-8 OLI/TIRS

2021 ◽  
pp. 30-42
Author(s):  
Chavarit AUNTARIN ◽  
Poramate CHUNPANG ◽  
Wutthisat CHOKKUEA ◽  
Teerawong LAOSUWAN
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
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 Thermal Remote Sensing: A tool to Determine Temporal Land Surface Temperature in Hawassa City, Ethiopia

2020 ◽  
Author(s):  
Mikias Biazen Molla
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Remote Sensing ◽  
The City

Abstract This investigation was conducted for the estimation of the temporal land surface temperature value using thermal remote sensing of Landsat-8 (OLI) Data in Hawassa City Administration, Ethiopia. Satellite datasets of Landsat-7 (ETM+) for 22nd March 2002 and Landsat-8 (OLI) of 22nd March 2019 were taken for this study. Different algorisms were used to estimate the Normalized Difference Vegetation Index threshold from the Red and Near-Infrared band and the ground earth's surface emissivity esteem is legitimately recovered from the thermal infrared by coordinating with the outcome got from MODIS information. The land use land cover map of the city was prepared with better accuracy using the on-screen classification technique. The spatial distribution of surface temperature of the city range from 6.62°C to 22.54°C with a mean of 14.58°C and a standard deviation of 11.25 in the year of march 22nd 2002. The LST result derived from Landsat 8 for March 22nd, 2019, ranges from 11.97°C to 35.5°C with a mean of 23.735 °C and a standard deviation of 16.64. In both years the higher LST values correspond to built-up/settlement and bare/open lands of the city; whereas, lower LST values were observed in vegetation (trees/woodlot, shrubs, and grass forested) area. Urban expansion (built-up area roads, and another impervious surface), decline in vegetation levels due to deforestation and increasing population density. Increasing an evergreen tree and green space coverage, design and develop city parks and rehabilitate the existing degraded natural environments are among the recommended strategy to reduce the rate of LST.

scholarly journals Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

2021 ◽  
Vol 10 (04) ◽  
pp. 131-149
Author(s):  
Yaw A. Twumasi ◽  
Edmund C. Merem ◽  
John B. Namwamba ◽  
Olipa S. Mwakimi ◽  
Tomas Ayala-Silva ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Two Cities

scholarly journals URBANIZATION AND IT IMPACTS TO LAND SURFACE TEMPERATURE ON SMALL MEDIUM SIZE CITY FOR YEAR 1991, 2011 AND 2018: CASE STUDY KOTA KINABALU

2020 ◽  
Vol 6 (1) ◽  
pp. 58-76
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hot Spot ◽  
Medium Size ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Landsat 5 Tm

Transformation of land cover vegetation toward urban areas causes the temperature at urban higher to compare to suburban and rural areas, namely urban heat island (UHI) effect. The UHI has a negative impact, such a stroke heat, air pollution, green gasses emission, and electric consumption. UHI studies at a tropical country still limited due to the containment of cloud cover. Besides that, studies only focus on big cities which have residents above than 2 million. The outcome this studied important to enhance our knowledge of urban heat effect at small-medium cities and guidelines to policymaker and urban planner to discover there has effectively taken to decrease the effect of urban heat at the hot spot area. The main goal of this research about to discovered influence of urban growth and selected urban index, namely the Normalized Difference Built Index (NDBI) to LST. NDBI is an index which denotes intensity of urban built up. In the first step, we generate the LST and NDBI from Landsat 8 OLI at year 2018 and Landsat 5 TM for the year 2011 and 1991. Second, we applied the unsupervised classification of Landsat 8 OLI and Landsat 5 TM to generate the land cover maps for the years 1991, 2011, and 2018. Third of our method to examine the relationship between Land surface temperature (LST) and NDBI.  The higher value NDBI is a hot spot, and the low value is a cold spot. In the last step, we applied for Change Detection analysis using GIS to examine the land cover change between 1991 and 2018.  Our results show the higher the value of NDBI and LST at the centre of the city and the lowest value at vegetation land cover. The transformation of land cover vegetation to urban increase at countryside area and out-of-town and significantly increase of distribution of UHI. On another hand, the shows positive relationships between LST and NDBI. The output of the study provides a guideline for policymakers and town designers to develop to toward city zero carbon, sustainable and health.

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.

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