Statistical estimation of next-day nighttime surface urban heat islands
文献类型：期刊论文
| 作者 | Lai, Jiameng4; Zhan, Wenfeng4,5; Quan, Jinling6; Bechtel, Benjamin1; Wang, Kaicun7; Zhou, Ji2; Huang, Fan4; Chakraborty, Tirthankar3; Liu, Zihan4; Lee, Xuhui3 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 176页码:182-195 |
| 关键词 | Surface urban heat island Thermal remote sensing Land surface temperature MODIS Support vector regression |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2021.04.009 |
| 英文摘要 | Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress. |
| WOS关键词 | THERMAL SATELLITE IMAGES ; LAND-COVER ; DIAGNOSTIC EQUATION ; ANTHROPOGENIC HEAT ; TEMPORAL TRENDS ; AIR-TEMPERATURE ; UNITED-STATES ; SENSED DATA ; CITY ; CHINA |
| 资助项目 | National Key R&D Program of China[2017YFA0603604] ; Fundamental Research Funds for the Central Universities[090414380024] ; Jiangsu Provincial Natural Science Foundation[BK20180009] ; National Natural Science Foundation of China[41671420] ; Jiangsu Provincial Graduate Student Innovation Project[KYCX19_0036] ; National Youth Talent Support Program of China |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000655474600014 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key R&D Program of China ; Fundamental Research Funds for the Central Universities ; Jiangsu Provincial Natural Science Foundation ; National Natural Science Foundation of China ; Jiangsu Provincial Graduate Student Innovation Project ; National Youth Talent Support Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/164037]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Ruhr Univ Bochum, Dept Geog, D-44801 Bochum, Germany 2.Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China 3.Yale Univ, Sch Forestry & Environm Studies, New Haven, CT 06511 USA 4.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210046, Jiangsu, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 7.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lai, Jiameng,Zhan, Wenfeng,Quan, Jinling,et al. Statistical estimation of next-day nighttime surface urban heat islands[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2021,176:182-195. |
| APA | Lai, Jiameng.,Zhan, Wenfeng.,Quan, Jinling.,Bechtel, Benjamin.,Wang, Kaicun.,...&Lee, Xuhui.(2021).Statistical estimation of next-day nighttime surface urban heat islands.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,176,182-195. |
| MLA | Lai, Jiameng,et al."Statistical estimation of next-day nighttime surface urban heat islands".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 176(2021):182-195. |
入库方式： OAI收割
来源：地理科学与资源研究所
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