Localized Downscaling of Urban Land Surface Temperature-A Case Study in Beijing, China
文献类型：期刊论文
| 作者 | Li, Nana2; Wu, Hua1; Ouyang, Xiaoying3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-05-01 |
| 卷号 | 14期号:10页码:16 |
| 关键词 | urban land surface temperature downscaling random forest building morphology optimal local-window size stepwise downscaling Beijing area |
| DOI | 10.3390/rs14102390 |
| 通讯作者 | Li, Nana(nnli@ium.cn) |
| 英文摘要 | High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 x 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 x 3, 5 x 5, and 11 x 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution. |
| WOS关键词 | REFLECTANCE ; RESOLUTION ; ALGORITHM |
| 资助项目 | National Natural Science Foundation of China[42171337] ; National Natural Science Foundation of China[42171370] ; Beijing Municipal Science and Technology Commission[Z201100008220002] ; Beijing Key Laboratory of Urban Spatial Information Engineering[20210210] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000803534900001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission ; Beijing Key Laboratory of Urban Spatial Information Engineering |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178362]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Nana |
| 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.China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Nana,Wu, Hua,Ouyang, Xiaoying. Localized Downscaling of Urban Land Surface Temperature-A Case Study in Beijing, China[J]. REMOTE SENSING,2022,14(10):16. |
| APA | Li, Nana,Wu, Hua,&Ouyang, Xiaoying.(2022).Localized Downscaling of Urban Land Surface Temperature-A Case Study in Beijing, China.REMOTE SENSING,14(10),16. |
| MLA | Li, Nana,et al."Localized Downscaling of Urban Land Surface Temperature-A Case Study in Beijing, China".REMOTE SENSING 14.10(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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