Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City
The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the abi...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/2/332 |
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| author | Abdou Rachid Bah Hamidreza Norouzi Satya Prakash Reginald Blake Reza Khanbilvardi Cynthia Rosenzweig |
| author_facet | Abdou Rachid Bah Hamidreza Norouzi Satya Prakash Reginald Blake Reza Khanbilvardi Cynthia Rosenzweig |
| author_sort | Abdou Rachid Bah |
| collection | DOAJ |
| description | The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions. |
| first_indexed | 2024-03-09T22:38:16Z |
| format | Article |
| id | doaj.art-fbb7ecb536334b86be90cf7e5a190da3 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T22:38:16Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-fbb7ecb536334b86be90cf7e5a190da32023-11-23T18:45:59ZengMDPI AGAtmosphere2073-44332022-02-0113233210.3390/atmos13020332Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York CityAbdou Rachid Bah0Hamidreza Norouzi1Satya Prakash2Reginald Blake3Reza Khanbilvardi4Cynthia Rosenzweig5The Graduate Center, City University of New York, New York, NY 10016, USAThe Graduate Center, City University of New York, New York, NY 10016, USAIndia Meteorological Department, Ministry of Earth Sciences, New Delhi 110003, IndiaThe Graduate Center, City University of New York, New York, NY 10016, USACity College of New York, City University of New York, Manhattan, New York, NY 10031, USANASA Goddard Institute for Space Studies, New York, NY 10025, USAThe surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.https://www.mdpi.com/2073-4433/13/2/332land surface temperaturespatial downscaling methodsurface urban heat islandsatellite remote sensingGOES–Rlandsat–8 |
| spellingShingle | Abdou Rachid Bah Hamidreza Norouzi Satya Prakash Reginald Blake Reza Khanbilvardi Cynthia Rosenzweig Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City Atmosphere land surface temperature spatial downscaling method surface urban heat island satellite remote sensing GOES–R landsat–8 |
| title | Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City |
| title_full | Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City |
| title_fullStr | Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City |
| title_full_unstemmed | Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City |
| title_short | Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City |
| title_sort | spatial downscaling of goes r land surface temperature over urban regions a case study for new york city |
| topic | land surface temperature spatial downscaling method surface urban heat island satellite remote sensing GOES–R landsat–8 |
| url | https://www.mdpi.com/2073-4433/13/2/332 |
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