A General Framework for Retrieving Land Surface Emissivity and Temperature Using Sensors With Split-Window Thermal Infrared Channels: A Case Study With Landsat 9
文献类型：期刊论文
| 作者 | Li, Xiu-Juan1; Wu, Hua1,3; Ni, Li2; Cheng, Yuan-Liang1; Zhang, Xing-Xing1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 62页码:5008012 |
| 关键词 | Landsat Land surface temperature Land surface Accuracy Temperature sensors Spatial resolution Soil Grasslands Atmospheric modeling Vegetation mapping Land surface emissivity (LSE) land surface temperature (LST) Landsat 9 machine learning split-window (SW) method |
| DOI | 10.1109/TGRS.2024.3498913 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Land surface temperature (LST) and emissivity (LSE) are the crucial parameters for thermal infrared (TIR) remote sensing. However, the coupling of the two parameters presents a challenge to achieving high-accuracy retrieval, particularly for sensors with only one or two TIR channels. Following the launch of Landsat 9, there has been a rapid increase in demand for methods to accurately estimate LSE and LST for sensors with high spatial resolution but limited TIR channels. Therefore, this article proposes a two-step framework to retrieve LSE and LST for Landsat 9 only using data of its own. First, the data in visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) channels of Landsat 9 were used to retrieve LSEs based on a machine learning method. Subsequently, the split-window (SW) method was employed to retrieve LST based on the estimated LSEs. As a result, the retrieved LSE exhibits high accuracy across the cross and direct validation, with RMSEs all below 0.01 for the two TIR channels. For LST, the retrieved result was validated by the existing products and in situ LSTs from surface radiation budget (SURFRAD), demonstrating excellent accuracies, with RMSE of 1.86 K, which is superior to the LST product of Landsat 9, with RMSE of 2.14 K. Therefore, the proposed framework is feasible for LSE and LST retrieval without support of auxiliary data from other origins, which is of great significance for the sensors with limited TIR channels to produce accurate LSE and LST products. |
| WOS关键词 | BROAD-BAND EMISSIVITY ; LONG-TERM ; MODIS ; ALGORITHM ; VALIDATION ; RESOLUTION ; PRODUCTS ; MACHINE |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001373843000029 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210493]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wu, Hua |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 3.Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 610054, Sichuan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xiu-Juan,Wu, Hua,Ni, Li,et al. A General Framework for Retrieving Land Surface Emissivity and Temperature Using Sensors With Split-Window Thermal Infrared Channels: A Case Study With Landsat 9[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:5008012. |
| APA | Li, Xiu-Juan,Wu, Hua,Ni, Li,Cheng, Yuan-Liang,&Zhang, Xing-Xing.(2024).A General Framework for Retrieving Land Surface Emissivity and Temperature Using Sensors With Split-Window Thermal Infrared Channels: A Case Study With Landsat 9.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,5008012. |
| MLA | Li, Xiu-Juan,et al."A General Framework for Retrieving Land Surface Emissivity and Temperature Using Sensors With Split-Window Thermal Infrared Channels: A Case Study With Landsat 9".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):5008012. |
入库方式： OAI收割
来源：地理科学与资源研究所
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