Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data
Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spect...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/13/7/1152 |
| _version_ | 1797407449906413568 |
|---|---|
| author | David Neil Bird Ellen Banzhaf Julius Knopp Wanben Wu Laurence Jones |
| author_facet | David Neil Bird Ellen Banzhaf Julius Knopp Wanben Wu Laurence Jones |
| author_sort | David Neil Bird |
| collection | DOAJ |
| description | Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities. |
| first_indexed | 2024-03-09T03:42:40Z |
| format | Article |
| id | doaj.art-a53b89428c5641ecbb6ebf4c8528c101 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T03:42:40Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-a53b89428c5641ecbb6ebf4c8528c1012023-12-03T14:39:04ZengMDPI AGAtmosphere2073-44332022-07-01137115210.3390/atmos13071152Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) DataDavid Neil Bird0Ellen Banzhaf1Julius Knopp2Wanben Wu3Laurence Jones4Institute for Climate, Energy and Society, JOANNEUM RESEARCH Forschungsgesellsch mbH, LIFE, Waagner-Biro-Strasse 100, Flrs. 9 & 10, A-8020 Graz, AustriaDepartment of Urban and Environmental Sociology, UFZ-Helmholtz Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, GermanyDepartment of Urban and Environmental Sociology, UFZ-Helmholtz Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, GermanyDepartment of Urban and Environmental Sociology, UFZ-Helmholtz Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, GermanyUK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor LL57 2UW, UKRemotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.https://www.mdpi.com/2073-4433/13/7/1152nature based solutions (NBS)environmental services (ES)land surface temperature (LST)LandsatMODISNormalized Difference Vegetation Index (NDVI) |
| spellingShingle | David Neil Bird Ellen Banzhaf Julius Knopp Wanben Wu Laurence Jones Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data Atmosphere nature based solutions (NBS) environmental services (ES) land surface temperature (LST) Landsat MODIS Normalized Difference Vegetation Index (NDVI) |
| title | Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data |
| title_full | Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data |
| title_fullStr | Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data |
| title_full_unstemmed | Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data |
| title_short | Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data |
| title_sort | combining spatial and temporal data to create a fine resolution daily urban air temperature product from remote sensing land surface temperature lst data |
| topic | nature based solutions (NBS) environmental services (ES) land surface temperature (LST) Landsat MODIS Normalized Difference Vegetation Index (NDVI) |
| url | https://www.mdpi.com/2073-4433/13/7/1152 |
| work_keys_str_mv | AT davidneilbird combiningspatialandtemporaldatatocreateafineresolutiondailyurbanairtemperatureproductfromremotesensinglandsurfacetemperaturelstdata AT ellenbanzhaf combiningspatialandtemporaldatatocreateafineresolutiondailyurbanairtemperatureproductfromremotesensinglandsurfacetemperaturelstdata AT juliusknopp combiningspatialandtemporaldatatocreateafineresolutiondailyurbanairtemperatureproductfromremotesensinglandsurfacetemperaturelstdata AT wanbenwu combiningspatialandtemporaldatatocreateafineresolutiondailyurbanairtemperatureproductfromremotesensinglandsurfacetemperaturelstdata AT laurencejones combiningspatialandtemporaldatatocreateafineresolutiondailyurbanairtemperatureproductfromremotesensinglandsurfacetemperaturelstdata |