An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application
Urban land surface temperature (LST) is critical for understanding urban thermal environments and expansion. Compared with natural surfaces, urban areas have complex geometric structures causing multiple scattering and adjacency effects. This study combines the XGBoost algorithm with an improved tem...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2459346 |
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| author | Jinglun Li Kun Li Yonggang Qian Xianhui Dou Qijin Han Jian Zeng Hang Zhao Qiongqiong Lan Zhaopeng Xu Jiayi Bai Baoan Wei Xining Liu Feng Wang Juntao Yang Yueming Wang |
| author_facet | Jinglun Li Kun Li Yonggang Qian Xianhui Dou Qijin Han Jian Zeng Hang Zhao Qiongqiong Lan Zhaopeng Xu Jiayi Bai Baoan Wei Xining Liu Feng Wang Juntao Yang Yueming Wang |
| author_sort | Jinglun Li |
| collection | DOAJ |
| description | Urban land surface temperature (LST) is critical for understanding urban thermal environments and expansion. Compared with natural surfaces, urban areas have complex geometric structures causing multiple scattering and adjacency effects. This study combines the XGBoost algorithm with an improved temperature and emissivity separation (TES) algorithm to retrieve urban LST and land surface emissivity (LSE) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data. Three aspects are included: firstly, sing the XGBoost algorithm to estimate urban canopy bright temperature (BT) from 5 ASTER TIR top of atmosphere (TOA) BTs; secondly, employing an improved TES algorithm based on sky view factor (SVF) to separate LST and LSE; finally, the accuracy of the proposed algorithm is evaluated from simulation data. Simulation results show that the root mean squared errors (RMSEs) of the urban canopy BTs are about 0.2 K and 1.2 K using the XGBoost algorithm and split window (SW) algorithm, respectively. The urban LST and LSE RMSEs using the proposed algorithm are approximately 0.36 K and 0.022, respectively. Applied to Beijing and Wuhan, China, the algorithm yields slightly lower urban LST compared to ASTER level-2 products, with LST/LSE RMSEs of 0.72 K and 0.017, respectively. |
| format | Article |
| id | doaj-art-cde5539327814b8983a45c857b35f067 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-cde5539327814b8983a45c857b35f0672025-01-30T02:05:59ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2025.2459346An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its applicationJinglun Li0Kun Li1Yonggang Qian2Xianhui Dou3Qijin Han4Jian Zeng5Hang Zhao6Qiongqiong Lan7Zhaopeng Xu8Jiayi Bai9Baoan Wei10Xining Liu11Feng Wang12Juntao Yang13Yueming Wang14Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, People’s Republic of ChinaChina Center for Resources Satellite Data and Application, Beijing, People’s Republic of ChinaChina Center for Resources Satellite Data and Application, Beijing, People’s Republic of ChinaChina Center for Resources Satellite Data and Application, Beijing, People’s Republic of ChinaChina Center for Resources Satellite Data and Application, Beijing, People’s Republic of ChinaChina Center for Resources Satellite Data and Application, Beijing, People’s Republic of ChinaShaanxi Meteorological Observation Centre, Xi’an, People’s Republic of ChinaThe Fourth Topographic Survey Team, Ministry of Natural Resources, Harbin, People’s Republic of ChinaChina Sanya Institute of South China Sea Geology, Guangzhou Marine Geological Survey, Sanya, People’s Republic of ChinaThe Fourth Topographic Survey Team, Ministry of Natural Resources, Harbin, People’s Republic of ChinaThe Fourth Topographic Survey Team, Ministry of Natural Resources, Harbin, People’s Republic of ChinaInner Mongolia North Heavy Industries Group Corp. LTD, Inner Mongolia, People’s Republic of ChinaUrban land surface temperature (LST) is critical for understanding urban thermal environments and expansion. Compared with natural surfaces, urban areas have complex geometric structures causing multiple scattering and adjacency effects. This study combines the XGBoost algorithm with an improved temperature and emissivity separation (TES) algorithm to retrieve urban LST and land surface emissivity (LSE) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data. Three aspects are included: firstly, sing the XGBoost algorithm to estimate urban canopy bright temperature (BT) from 5 ASTER TIR top of atmosphere (TOA) BTs; secondly, employing an improved TES algorithm based on sky view factor (SVF) to separate LST and LSE; finally, the accuracy of the proposed algorithm is evaluated from simulation data. Simulation results show that the root mean squared errors (RMSEs) of the urban canopy BTs are about 0.2 K and 1.2 K using the XGBoost algorithm and split window (SW) algorithm, respectively. The urban LST and LSE RMSEs using the proposed algorithm are approximately 0.36 K and 0.022, respectively. Applied to Beijing and Wuhan, China, the algorithm yields slightly lower urban LST compared to ASTER level-2 products, with LST/LSE RMSEs of 0.72 K and 0.017, respectively.https://www.tandfonline.com/doi/10.1080/17538947.2025.24593463-D structureSVFimproved TES algorithmXGBoostUrban LST/LSE retrieval |
| spellingShingle | Jinglun Li Kun Li Yonggang Qian Xianhui Dou Qijin Han Jian Zeng Hang Zhao Qiongqiong Lan Zhaopeng Xu Jiayi Bai Baoan Wei Xining Liu Feng Wang Juntao Yang Yueming Wang An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application International Journal of Digital Earth 3-D structure SVF improved TES algorithm XGBoost Urban LST/LSE retrieval |
| title | An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application |
| title_full | An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application |
| title_fullStr | An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application |
| title_full_unstemmed | An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application |
| title_short | An urban land surface temperature and emissivity separation algorithm from ASTER TIR data and its application |
| title_sort | urban land surface temperature and emissivity separation algorithm from aster tir data and its application |
| topic | 3-D structure SVF improved TES algorithm XGBoost Urban LST/LSE retrieval |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2459346 |
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