{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:46:31Z","timestamp":1762609591845,"version":"build-2065373602"},"reference-count":86,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Sichuan Province","award":["2023NSFSC0752","NODAOP2021009","2021RYJ03"],"award-info":[{"award-number":["2023NSFSC0752","NODAOP2021009","2021RYJ03"]}]},{"name":"the National Earth Observation Data Center Foundation","award":["2023NSFSC0752","NODAOP2021009","2021RYJ03"],"award-info":[{"award-number":["2023NSFSC0752","NODAOP2021009","2021RYJ03"]}]},{"name":"the Open Fund of Sichuan Provincial Key Laboratory of Artificial Intelligence","award":["2023NSFSC0752","NODAOP2021009","2021RYJ03"],"award-info":[{"award-number":["2023NSFSC0752","NODAOP2021009","2021RYJ03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u00b0C (suburban area as 50% of the urban area) and 2.32 \u00b0C (suburban area as 100% of the urban area) in 2002 to 4.93 \u00b0C and 5.07 \u00b0C, 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.<\/jats:p>","DOI":"10.3390\/s23229206","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T08:19:43Z","timestamp":1700122783000},"page":"9206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0415-351X","authenticated-orcid":false,"given":"Jiamin","family":"Luo","sequence":"first","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"},{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu 610106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"},{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu 610106, China"},{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4102-7233","authenticated-orcid":false,"given":"Qiuyan","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"},{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu 610106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101052","DOI":"10.1016\/j.uclim.2021.101052","article-title":"Land use\/land cover change and its impact on surface urban heat island and urban thermal comfort in a metropolitan city","volume":"41","author":"Naikoo","year":"2022","journal-title":"Urban Clim."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109368","DOI":"10.1016\/j.buildenv.2022.109368","article-title":"Impact of urbanization on land surface temperature and surface urban heat Island using optical remote sensing data: A case study of Jeju Island, Republic of Korea","volume":"222","author":"Moazzam","year":"2022","journal-title":"Build. 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