A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data
文献类型：期刊论文
| 作者 | Duan, Si-Bo2; Li, Zhao-Liang1,2; Leng, Pei2 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2017-06-15 |
| 卷号 | 195页码:107-117 |
| 关键词 | Land surface temperature All-weather Thermal infrared Passive microwave Subsurface temperature |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2017.04.008 |
| 通讯作者 | Li, Zhao-Liang(lizhaoliang@caas.cn) |
| 英文摘要 | Land surface temperature (LST) is an important parameter associated with the land-atmosphere interface. Satellite remote sensing is the most effective method of measuring LST at regional and global scales. Satellite thermal infrared (TIR) measurements are widely used to retrieve LST with high accuracy and high spatial resolution but are limited to cloud-free conditions due to their inability to penetrate clouds. By contrast, satellite passive microwave (PMW) measurements are capable of penetrating clouds and providing data regardless of the cloud conditions. However, PMW measurements have limitations, such as a low spatial resolution and low temperature retrieval accuracy. Furthermore, temperature retrieval from PMW measurements yields the subsurface temperature, which differs from the LST retrieved from TIR measurements (skin temperature). This study proposes a framework for the retrieval of all-weather LST at a high spatial resolution by combining the advantages of TIR and PMW measurements. Compared to the MODIS LST product, the all-weather LST reflects the spatial variations in LST accurately. In situ LST measurements at four sites in an arid area of northwest China were used to evaluate the accuracy of the all-weather LST. The root mean square error of the LST under cloud-free conditions was approximately 2 K, whereas that of the LST under cloudy conditions varied from 3.5 K to 4.4 K. The results indicate that the all-weather LST retrieval algorithm can provide an IST dataset with reasonable accuracy and a high spatial resolution under clear and cloudy conditions. (C) 2017 Elsevier Inc. All rights reserved. |
| WOS关键词 | SPLIT-WINDOW ALGORITHM ; MODIS DATA ; RECONSTRUCTION ; EMISSIVITY ; PRODUCT ; WATER ; LST ; DISAGGREGATION ; VALIDATION ; CYCLE |
| 资助项目 | National Natural Science Foundation of China[41231170] ; National Natural Science Foundation of China[41501406] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000402355700009 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/63561]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Zhao-Liang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Si-Bo,Li, Zhao-Liang,Leng, Pei. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data[J]. REMOTE SENSING OF ENVIRONMENT,2017,195:107-117. |
| APA | Duan, Si-Bo,Li, Zhao-Liang,&Leng, Pei.(2017).A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data.REMOTE SENSING OF ENVIRONMENT,195,107-117. |
| MLA | Duan, Si-Bo,et al."A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data".REMOTE SENSING OF ENVIRONMENT 195(2017):107-117. |
入库方式： OAI收割
来源：地理科学与资源研究所
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