Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario
文献类型：期刊论文
| 作者 | Luo, Jiamin1,3; Yao, Yuan1,2,3; Yin, Qiuyan1,3 |
| 刊名 | SENSORS
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 23期号:22页码:25 |
| 关键词 | surface urban heat island land surface temperature spatiotemporal fusion spatial downscaling |
| DOI | 10.3390/s23229206 |
| 通讯作者 | Yao, Yuan(yaoyuan@cdu.edu.cn) |
| 英文摘要 | Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 degrees C (suburban area as 50% of the urban area) and 2.32 degrees C (suburban area as 100% of the urban area) in 2002 to 4.93 degrees C and 5.07 degrees C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect. |
| WOS关键词 | REFLECTANCE FUSION ; TEMPERATURE ; VEGETATION ; RESOLUTION ; LANDSAT ; MODEL ; PRECIPITATION ; PREDICTION ; IMPACTS ; DENSITY |
| 资助项目 | Natural Science Foundation of Sichuan Province in China[2023NSFSC0752] ; National Earth Observation Data Center Foundation[NODAOP2021009] ; Open Fund of Sichuan Provincial Key Laboratory of Artificial Intelligence[2021RYJ03] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001113975300001 |
| 出版者 | MDPI |
| 资助机构 | Natural Science Foundation of Sichuan Province in China ; National Earth Observation Data Center Foundation ; Open Fund of Sichuan Provincial Key Laboratory of Artificial Intelligence |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200340]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yao, Yuan |
| 作者单位 | 1.Chengdu Univ, Sch Architecture & Civil Engn, Chengdu 610106, 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 3.Chengdu Univ, Key Lab Pattern Recognit & Intelligent Informat Pr, Minist Agr, Chengdu 610106, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Jiamin,Yao, Yuan,Yin, Qiuyan. Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario[J]. SENSORS,2023,23(22):25. |
| APA | Luo, Jiamin,Yao, Yuan,&Yin, Qiuyan.(2023).Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario.SENSORS,23(22),25. |
| MLA | Luo, Jiamin,et al."Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario".SENSORS 23.22(2023):25. |
入库方式： OAI收割
来源：地理科学与资源研究所
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