A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products
The trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/983 |
| _version_ | 1797476820782678016 |
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| author | Yichen Yang Xuhui Lee |
| author_facet | Yichen Yang Xuhui Lee |
| author_sort | Yichen Yang |
| collection | DOAJ |
| description | The trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as the urban heat island (UHI). Here, we developed a fusion framework, characterized by a scale-separating process, to generate LST data with high spatiotemporal resolution. The scale-separating framework breaks the fusion task into three steps to address errors at multiple spatial scales, with a specific focus on intra-scene variations of LST. The framework was experimented with MODIS and Landsat LST data. It first removed inter-sensor biases, which depend on season and on land use type (urban versus rural), and then produced a Landsat-like sharpened LST map for days when MOIDS observations are available. The sharpened images achieved a high accuracy, with a RMSE of 0.91 K for a challenging heterogeneous landscape (urban area). A comparison between the sharpened LST and the air temperature measured with bicycle-mounted mobile sensors revealed the roles of impervious surface fraction and wind speed in controlling the surface-to-air temperature gradient in an urban landscape. |
| first_indexed | 2024-03-09T21:08:08Z |
| format | Article |
| id | doaj.art-628efaa008634b1cb0f4905d07a46a80 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:08Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-628efaa008634b1cb0f4905d07a46a802023-11-23T21:55:08ZengMDPI AGRemote Sensing2072-42922022-02-0114498310.3390/rs14040983A Scale-Separating Framework for Fusing Satellite Land Surface Temperature ProductsYichen Yang0Xuhui Lee1Yale School of the Environment, Yale University, 195 Prospect Street, New Haven, CT 06511, USAYale School of the Environment, Yale University, 195 Prospect Street, New Haven, CT 06511, USAThe trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as the urban heat island (UHI). Here, we developed a fusion framework, characterized by a scale-separating process, to generate LST data with high spatiotemporal resolution. The scale-separating framework breaks the fusion task into three steps to address errors at multiple spatial scales, with a specific focus on intra-scene variations of LST. The framework was experimented with MODIS and Landsat LST data. It first removed inter-sensor biases, which depend on season and on land use type (urban versus rural), and then produced a Landsat-like sharpened LST map for days when MOIDS observations are available. The sharpened images achieved a high accuracy, with a RMSE of 0.91 K for a challenging heterogeneous landscape (urban area). A comparison between the sharpened LST and the air temperature measured with bicycle-mounted mobile sensors revealed the roles of impervious surface fraction and wind speed in controlling the surface-to-air temperature gradient in an urban landscape.https://www.mdpi.com/2072-4292/14/4/983spatiotemporal data fusionland surface temperaturescaledeep-learningurban heat islandNew Haven |
| spellingShingle | Yichen Yang Xuhui Lee A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products Remote Sensing spatiotemporal data fusion land surface temperature scale deep-learning urban heat island New Haven |
| title | A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products |
| title_full | A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products |
| title_fullStr | A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products |
| title_full_unstemmed | A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products |
| title_short | A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products |
| title_sort | scale separating framework for fusing satellite land surface temperature products |
| topic | spatiotemporal data fusion land surface temperature scale deep-learning urban heat island New Haven |
| url | https://www.mdpi.com/2072-4292/14/4/983 |
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