Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures
文献类型：期刊论文
| 作者 | Xia, Haiping1; Chen, Yunhao1; Li, Ying1; Quan, Jinling2 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2019-04-01 |
| 卷号 | 224页码:259-274 |
| 关键词 | Temperature disaggregation Thermal sharpening Spatiotemporal fusion Heterogeneous region TPS interpolation |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2019.02.006 |
| 通讯作者 | Chen, Yunhao(cyh@bnu.edu.cn) |
| 英文摘要 | High-spatiotemporal-resolution land surface temperatures (LSTs) are required in various environmental applications. However, due to the trade-off between the spatial and temporal resolutions in remote sensing, such data are still unavailable. Many studies have been conducted to resolve this dilemma, but difficulties remain in generating high-spatiotemporal-resolution (i.e., diurnal, e.g., 30 m resolution) LSTs. Accordingly, this study proposes a weighted combination of kernel-driven and fusion-based methods (termed CKFM) to enhance the resolution of time series LSTs; the kernel-driven process can obtain abundant spatial details from visible bands, while the fusion-based process is applied for its spatiotemporal prediction ability. CKFM contains three parts. First, a kernel-driven method is applied to predict high-resolution LSTs via a regression relationship between simulated medium-resolution LSTs and kernels. Second, a fusion-based method is applied to predict the medium-resolution LST, the result of which is used for thin plate spline (TPS) downscaling. Finally, the results of the kernel-driven and fusion-based processes are combined via weights calculated from error estimations. Compared with existing thermal sharpening methods, CKFM has the following strengths: (1) it fully utilizes the available visible and thermal bands from multiple sensors, thereby obtaining spatial details in a variety of ways; (2) it downscales LSTs in a dynamic manner; and (3) it is suitable for heterogeneous regions. CKFM is tested with Landsat 8 and MODIS data and successfully downscales the 1 km resolution MODIS LSTs into 30 m resolution data. In both visual and quantitative evaluations, CKFM is more accurate and robust than the kernel-driven method with an improvement of 0.1-0.6 K, and it reconstructs more spatial details than the fusion-based process. Based on these characteristics, CKFM is a promising method for generating daily high spatial resolution LSTs for various environmental studies. |
| WOS关键词 | URBAN HEAT-ISLAND ; EXTREME LEARNING-MACHINE ; REFLECTANCE FUSION ; SATELLITE IMAGES ; ENERGY FLUXES ; RETRIEVAL ; INDEX ; ALGORITHM ; MODEL ; TM |
| 资助项目 | National Natural Science Foundation of China[41471348] ; National Natural Science Foundation of China[41771448] ; Project of State Key Laboratory of Earth Surface Processes and Resource Ecology[2017-ZY-03] ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People's Republic of China[UDC2017030212] ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People's Republic of China[UDC201650100] ; Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture[UDC2017030212] ; Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture[UDC201650100] ; Beijing Laboratory of Water Resources Security |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000462421200019 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Natural Science Foundation of China ; Project of State Key Laboratory of Earth Surface Processes and Resource Ecology ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People's Republic of China ; Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture ; Beijing Laboratory of Water Resources Security |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/48597]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Yunhao |
| 作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xia, Haiping,Chen, Yunhao,Li, Ying,et al. Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures[J]. REMOTE SENSING OF ENVIRONMENT,2019,224:259-274. |
| APA | Xia, Haiping,Chen, Yunhao,Li, Ying,&Quan, Jinling.(2019).Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures.REMOTE SENSING OF ENVIRONMENT,224,259-274. |
| MLA | Xia, Haiping,et al."Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures".REMOTE SENSING OF ENVIRONMENT 224(2019):259-274. |
入库方式： OAI收割
来源：地理科学与资源研究所
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