Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment
Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/16/4076 |
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| author | Katarína Onačillová Michal Gallay Daniel Paluba Anna Péliová Ondrej Tokarčík Daniela Laubertová |
| author_facet | Katarína Onačillová Michal Gallay Daniel Paluba Anna Péliová Ondrej Tokarčík Daniela Laubertová |
| author_sort | Katarína Onačillová |
| collection | DOAJ |
| description | Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R<sup>2</sup> of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens. |
| first_indexed | 2024-03-09T12:38:47Z |
| format | Article |
| id | doaj.art-533c7768ea2b4ebf8d180f26f5b0004a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:38:47Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-533c7768ea2b4ebf8d180f26f5b0004a2023-11-30T22:20:44ZengMDPI AGRemote Sensing2072-42922022-08-011416407610.3390/rs14164076Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban EnvironmentKatarína Onačillová0Michal Gallay1Daniel Paluba2Anna Péliová3Ondrej Tokarčík4Daniela Laubertová5Institute of Geography, Faculty of Science, Pavol Jozef Šafárik University in Košice, 041 54 Košice, SlovakiaInstitute of Geography, Faculty of Science, Pavol Jozef Šafárik University in Košice, 041 54 Košice, SlovakiaDepartment of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech RepublicNexus Geographics—Girona Office, Joaquim Botet Sisó 6, 17003 Girona, SpainInstitute of Geography, Faculty of Science, Pavol Jozef Šafárik University in Košice, 041 54 Košice, SlovakiaInstitute of Geography, Faculty of Science, Pavol Jozef Šafárik University in Košice, 041 54 Košice, SlovakiaThermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R<sup>2</sup> of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens.https://www.mdpi.com/2072-4292/14/16/4076land surface temperaturedownscalingLandsat 8Sentinel-2urban heat islandGoogle Earth Engine |
| spellingShingle | Katarína Onačillová Michal Gallay Daniel Paluba Anna Péliová Ondrej Tokarčík Daniela Laubertová Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment Remote Sensing land surface temperature downscaling Landsat 8 Sentinel-2 urban heat island Google Earth Engine |
| title | Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment |
| title_full | Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment |
| title_fullStr | Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment |
| title_full_unstemmed | Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment |
| title_short | Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment |
| title_sort | combining landsat 8 and sentinel 2 data in google earth engine to derive higher resolution land surface temperature maps in urban environment |
| topic | land surface temperature downscaling Landsat 8 Sentinel-2 urban heat island Google Earth Engine |
| url | https://www.mdpi.com/2072-4292/14/16/4076 |
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