Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product
Having a good knowledge of the uncertainty in the land surface temperature (LST) product will help to encourage its use in a wide number of applications, including urban heat islands, geothermal detection, and surface energy balance. Landsat 9 was launched on 27 September 2021 and provides an LST pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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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/9914619/ |
| _version_ | 1828204858874789888 |
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| author | Xiangchen Meng Jie Cheng Hao Guo Yahui Guo Beibei Yao |
| author_facet | Xiangchen Meng Jie Cheng Hao Guo Yahui Guo Beibei Yao |
| author_sort | Xiangchen Meng |
| collection | DOAJ |
| description | Having a good knowledge of the uncertainty in the land surface temperature (LST) product will help to encourage its use in a wide number of applications, including urban heat islands, geothermal detection, and surface energy balance. Landsat 9 was launched on 27 September 2021 and provides an LST product, which is generated by the radiative transfer equation algorithm and has a spatial resolution of 30 m. In this article, we evaluated the performance of the Landsat 9 LST product by using a temperature-based (T-based) method and cross-validation. The T-based validation results showed that the average bias at the surface radiation budget network and baseline surface radiation network sites was 0.24 K and that the corresponding root mean square error (RMSE) was 3.42 K. The Landsat 9 LST product was in good agreement with the Landsat 7/8 LSTs, with an average bias of 0.25/0.08 K, an RMSE of 0.51/1.04 K, and a mean absolute error of 0.38/0.64 K. The comparable performance of the Landsat 7/8/9 LST products can be explained by the consistent LST retrieval algorithm. The absolute differences in the LST between Landsat 9 LST and MOD11 (MOD21) LST images were between 0.01 (0.65) and 2.50 K (1.76 K), whereas the RMSE values were between 1.40 (1.80) and 3.65 K (3.26 K). The specific heat capacity and thermal inertia of the different land surface covers can explain the significant biases. The above evaluation results are consistent with the initial performance testing of thermal infrared sensor-2 (TIRS-2) by the National Aeronautics and Space Administration and the U.S. Geological Survey. Although the released Landsat 9 LST product showed good performance in the preliminary evaluation, the split-window algorithm may be a better option for Landsat 9 LST retrieval, as the TIRS-2 data addressed stray light incursion. Since there are no official validation results that have been published, this article provides a third-party performance evaluation of the Landsat 9 LST product and will benefit research fields that require Landsat series LST products. |
| first_indexed | 2024-04-12T12:30:38Z |
| format | Article |
| id | doaj.art-17bcdc9bf1cd4f0fa17874119d2a3056 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-12T12:30:38Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-17bcdc9bf1cd4f0fa17874119d2a30562022-12-22T03:33:03ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01158694870310.1109/JSTARS.2022.32127369914619Accuracy Evaluation of the Landsat 9 Land Surface Temperature ProductXiangchen Meng0https://orcid.org/0000-0002-0123-5405Jie Cheng1https://orcid.org/0000-0002-7620-4507Hao Guo2https://orcid.org/0000-0003-0036-8879Yahui Guo3https://orcid.org/0000-0002-0099-0759Beibei Yao4School of Geography and Tourism, Qufu Normal University, Rizhao, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaSchool of Geography and Tourism, Qufu Normal University, Rizhao, ChinaCollege of Water Sciences, Beijing Normal University, Beijing, ChinaSchool of Marxism, Qufu Normal University, Rizhao, ChinaHaving a good knowledge of the uncertainty in the land surface temperature (LST) product will help to encourage its use in a wide number of applications, including urban heat islands, geothermal detection, and surface energy balance. Landsat 9 was launched on 27 September 2021 and provides an LST product, which is generated by the radiative transfer equation algorithm and has a spatial resolution of 30 m. In this article, we evaluated the performance of the Landsat 9 LST product by using a temperature-based (T-based) method and cross-validation. The T-based validation results showed that the average bias at the surface radiation budget network and baseline surface radiation network sites was 0.24 K and that the corresponding root mean square error (RMSE) was 3.42 K. The Landsat 9 LST product was in good agreement with the Landsat 7/8 LSTs, with an average bias of 0.25/0.08 K, an RMSE of 0.51/1.04 K, and a mean absolute error of 0.38/0.64 K. The comparable performance of the Landsat 7/8/9 LST products can be explained by the consistent LST retrieval algorithm. The absolute differences in the LST between Landsat 9 LST and MOD11 (MOD21) LST images were between 0.01 (0.65) and 2.50 K (1.76 K), whereas the RMSE values were between 1.40 (1.80) and 3.65 K (3.26 K). The specific heat capacity and thermal inertia of the different land surface covers can explain the significant biases. The above evaluation results are consistent with the initial performance testing of thermal infrared sensor-2 (TIRS-2) by the National Aeronautics and Space Administration and the U.S. Geological Survey. Although the released Landsat 9 LST product showed good performance in the preliminary evaluation, the split-window algorithm may be a better option for Landsat 9 LST retrieval, as the TIRS-2 data addressed stray light incursion. Since there are no official validation results that have been published, this article provides a third-party performance evaluation of the Landsat 9 LST product and will benefit research fields that require Landsat series LST products.https://ieeexplore.ieee.org/document/9914619/Baseline surface radiation network (BSRN)Landsat 9land surface temperature (LST)MOD11MOD21surface radiation budget network (SURFRAD) |
| spellingShingle | Xiangchen Meng Jie Cheng Hao Guo Yahui Guo Beibei Yao Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Baseline surface radiation network (BSRN) Landsat 9 land surface temperature (LST) MOD11 MOD21 surface radiation budget network (SURFRAD) |
| title | Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product |
| title_full | Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product |
| title_fullStr | Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product |
| title_full_unstemmed | Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product |
| title_short | Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product |
| title_sort | accuracy evaluation of the landsat 9 land surface temperature product |
| topic | Baseline surface radiation network (BSRN) Landsat 9 land surface temperature (LST) MOD11 MOD21 surface radiation budget network (SURFRAD) |
| url | https://ieeexplore.ieee.org/document/9914619/ |
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