Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm
The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split...
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10002323/ |
| _version_ | 1797956398503428096 |
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| author | Mengmeng Wang Miao Li Zhengjia Zhang Tian Hu Guojin He Zhaoming Zhang Guizhou Wang Hua Li Junlei Tan Xiuguo Liu |
| author_facet | Mengmeng Wang Miao Li Zhengjia Zhang Tian Hu Guojin He Zhaoming Zhang Guizhou Wang Hua Li Junlei Tan Xiuguo Liu |
| author_sort | Mengmeng Wang |
| collection | DOAJ |
| description | The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of <1.75 K using the RBSW algorithm when the uncertainties in input parameters (i.e., AWV, emissivity, and at-sensor radiance) were considered. There is a marginal discrepancy between LST retrievals using the estimated AWV and moderate resolution imaging spectroradiometer AWV, with a difference in bias of −0.14 K and RMSE of 0.22 K, which indicates that the improved SWCVR method can provide an optional means to obtain AWV inputted in LST retrieval. With regard to the validation using the in situ measurements, the retrieved LST from Landsat 9 TIRS-2 data exhibits a bias of 0.44 K and RMSE of 1.98 K, respectively, showing a higher accuracy than the USGS Landsat LST product with bias of 1.21 and RMSE of 2.56 K. In conclusion, the proposed algorithm combining RBSW algorithm and improved SWCVR algorithm for LST retrieval from Landsat 9 has a good accuracy without dependence on external atmospheric data, and it is expected to be a reliable method for LST generation from Landsat 9 TIRS-2 data. |
| first_indexed | 2024-04-10T23:48:25Z |
| format | Article |
| id | doaj.art-b68ab217664042d39b84b8890a6ce6b7 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-10T23:48:25Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-b68ab217664042d39b84b8890a6ce6b72023-01-11T00:00:09ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01161100111210.1109/JSTARS.2022.323262110002323Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window AlgorithmMengmeng Wang0https://orcid.org/0000-0001-5379-5773Miao Li1Zhengjia Zhang2https://orcid.org/0000-0003-0930-6914Tian Hu3https://orcid.org/0000-0001-7280-1921Guojin He4https://orcid.org/0000-0001-7225-7276Zhaoming Zhang5https://orcid.org/0000-0002-8779-2738Guizhou Wang6https://orcid.org/0000-0002-2347-8416Hua Li7https://orcid.org/0000-0003-3834-2682Junlei Tan8https://orcid.org/0000-0001-9710-1868Xiuguo Liu9https://orcid.org/0000-0002-0045-9642School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaRemote Sensing and Natural Resources Modeling, Department ERIN, LIST, Belvaux, LuxembourgAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaThe thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of <1.75 K using the RBSW algorithm when the uncertainties in input parameters (i.e., AWV, emissivity, and at-sensor radiance) were considered. There is a marginal discrepancy between LST retrievals using the estimated AWV and moderate resolution imaging spectroradiometer AWV, with a difference in bias of −0.14 K and RMSE of 0.22 K, which indicates that the improved SWCVR method can provide an optional means to obtain AWV inputted in LST retrieval. With regard to the validation using the in situ measurements, the retrieved LST from Landsat 9 TIRS-2 data exhibits a bias of 0.44 K and RMSE of 1.98 K, respectively, showing a higher accuracy than the USGS Landsat LST product with bias of 1.21 and RMSE of 2.56 K. In conclusion, the proposed algorithm combining RBSW algorithm and improved SWCVR algorithm for LST retrieval from Landsat 9 has a good accuracy without dependence on external atmospheric data, and it is expected to be a reliable method for LST generation from Landsat 9 TIRS-2 data.https://ieeexplore.ieee.org/document/10002323/Land surface temperature (LST) retrievalLandsat 9 thermal infrared sensor-2 (TIRS-2) dataradiance-based split window (RBSW) algorithmsplit-window covariance-variance ratio (SWCVR) algorithm |
| spellingShingle | Mengmeng Wang Miao Li Zhengjia Zhang Tian Hu Guojin He Zhaoming Zhang Guizhou Wang Hua Li Junlei Tan Xiuguo Liu Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Land surface temperature (LST) retrieval Landsat 9 thermal infrared sensor-2 (TIRS-2) data radiance-based split window (RBSW) algorithm split-window covariance-variance ratio (SWCVR) algorithm |
| title | Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm |
| title_full | Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm |
| title_fullStr | Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm |
| title_full_unstemmed | Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm |
| title_short | Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm |
| title_sort | land surface temperature retrieval from landsat 9 tirs 2 data using radiance based split window algorithm |
| topic | Land surface temperature (LST) retrieval Landsat 9 thermal infrared sensor-2 (TIRS-2) data radiance-based split window (RBSW) algorithm split-window covariance-variance ratio (SWCVR) algorithm |
| url | https://ieeexplore.ieee.org/document/10002323/ |
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