Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product
文献类型：期刊论文
| 作者 | Duan, Si-Bo1; Li, Zhao-Liang1,2; Wang, Chenguang3; Zhang, Shuting1; Tang, Bo-Hui2; Leng, Pei1; Gao, Mao-Fang1 |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2019 |
| 卷号 | 40期号:5-6页码:1763-1778 |
| ISSN号 | 0143-1161 |
| DOI | 10.1080/01431161.2018.1460513 |
| 通讯作者 | Li, Zhao-Liang(lizhaoliang@caas.cn) |
| 英文摘要 | Land-surface temperature (LST) is an important parameter in the climatological, hydrological, ecological, and meteorological studies. The Thermal Infrared Sensor (TIRS) on board the Landsat 8 is a key instrument to collect thermal infrared (TIR) data. The Landsat series sensors provide continuously acquired collection of space-based TIR data. In this study, we proposed a method for retrieving LST from Landsat 8 TIRS single-channel data. The National Centers for Environmental Prediction reanalysis data in conjunction with the Moderate Resolution Transmittance Code 5 were used to correct atmospheric effects. The ASTER Global Emissivity Database product was used to correct the effects of surface emissivity. In situ LST measurements were collected by eight and four SI-111 infrared radiometers in the study areas A and B, respectively. The in situ LST was used to validate the retrieved LST. For the study area A (sands), the bias varies from approximately -1.3 to 1.7K, and the root mean square error (RMSE) from approximately 1.2 to 2.1K. For the study area B (grasslands/snow), the bias ranges from approximately -1.0 to 0.4K, and the RMSE from approximately 1.1 to 1.5K. To further compare the retrieved LST and the in situ LST at coarser pixel scale, all of the retrieved LST and the in situ LST were, respectively, averaged as the corresponding LST at 1km pixel scale (e.g. Moderate Resolution Imaging Spectroradiometer). The biases of the differences between the two averaged LST at 1km pixel scale for all TIRS scenes are approximately -0.2 and -0.5K for the study areas A and B, respectively, and the RMSE values are approximately 1.2 and 1.0K for the study area A and B, respectively. These results indicate that the proposed method can be used to retrieve LST from single-channel TIR data with a reasonable accuracy. |
| WOS关键词 | SPLIT-WINDOW ALGORITHM ; DIFFERENCE VEGETATION INDEX ; PHYSICS-BASED ALGORITHM ; ATMOSPHERIC CORRECTION ; MODIS DATA ; EMISSIVITY ; IMAGERY ; VALIDATION ; COVER ; WATER |
| 资助项目 | National Natural Science Foundation of China[41501406] ; National Natural Science Foundation of China[41231170] |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000464043900011 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/48228]  |
| 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 Agr Sci, Key Lab Agr Remote Sensing, Minist Agr, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Shanxi Univ, Sch Environm & Resources, Taiyuan, Shanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Si-Bo,Li, Zhao-Liang,Wang, Chenguang,et al. Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019,40(5-6):1763-1778. |
| APA | Duan, Si-Bo.,Li, Zhao-Liang.,Wang, Chenguang.,Zhang, Shuting.,Tang, Bo-Hui.,...&Gao, Mao-Fang.(2019).Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(5-6),1763-1778. |
| MLA | Duan, Si-Bo,et al."Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.5-6(2019):1763-1778. |
入库方式： OAI收割
来源：地理科学与资源研究所
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