{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:27:32Z","timestamp":1764588452204,"version":"build-2065373602"},"reference-count":88,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Major Program of the National Natural Science Foundation of China","award":["No. 42293270","ZDBS-LY-DQC005","KPI001"],"award-info":[{"award-number":["No. 42293270","ZDBS-LY-DQC005","KPI001"]}]},{"name":"The Program of Frontier Sciences of the Chinese Academy of Sciences","award":["No. 42293270","ZDBS-LY-DQC005","KPI001"],"award-info":[{"award-number":["No. 42293270","ZDBS-LY-DQC005","KPI001"]}]},{"name":"The Key Project of Innovation LREIS","award":["No. 42293270","ZDBS-LY-DQC005","KPI001"],"award-info":[{"award-number":["No. 42293270","ZDBS-LY-DQC005","KPI001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With ongoing global warming, heatwave-related disasters are on the rise, exerting a multifaceted impact on both the natural ecosystem and human society. While temperature has been extensively studied in the effects of extreme heat on human health, humidity has often been ignored. It is crucial to consider the combined influence of temperature and humidity when assessing heatwave risks and safeguarding human well-being. This study, leveraging remote sensing products and reanalysis data, presented the first analysis of the spatiotemporal variations in global human-perceived heatwaves on a seasonal scale from 2000 to 2020, and further employed the Random Forest (RF) regression model to quantitatively assess the explanatory power of seven driving factors. The study found that since the 21st century (1) changes in Heat Index (HI) have varied significantly worldwide, with the majority of regions witnessing an increase, particularly at higher latitudes. The largest HI-increasing area was observed in the second quarter (S2), while the overall HI increase peaked in the third quarter (S3); (2) except for the decreasing area of none-risk regions, the regions under all other risk levels expanded (the proportion of high-risk areas in the world increased from 2.97% to 3.69% in S2, and from 0.04% to 0.35% in the fourth quarter (S4)); (3) aspect demonstrated the greatest explanatory power for the global heatwave distribution (0.69\u20130.76), followed by land-use coverage (LUCC, 0.48\u20130.55) and precipitation (0.16\u20130.43), while the explanatory power of slope and nighttime light (NTL) was rather low; (4) over the years, the explanatory power of each factor for heatwave distribution underwent a minor decrease without significant trend, but exhibited seasonal periodicity. Climatic factors and LUCC were most impactful in the first quarter (S1), while DEM and other human factors dominated in S2; and (5) interaction factors showed no significant trends over the years, but the explanatory power of DEM and slope increased notably when interacting with climate factor, aspect, and LUCC, respectively. The interactions between the aspect and LUCC with precipitation yielded the highest explanatory power (above 0.85) across all interactions. To effectively tackle heatwave risks, it is suggested to concentrate on high-latitude regions, reinforce land use and urban planning with eco-friendly strategies, scrutinize the interplay between precipitation and heatwaves, capitalize on topographic data for devising well-informed prevention measures, and tailor response strategies to accommodate seasonal fluctuations. This study offers valuable insights for enhancing climate change adaptation, disaster prevention, and mitigation strategies, ultimately contributing to the alleviation of extreme heatwaves and risk reduction.<\/jats:p>","DOI":"10.3390\/rs15143627","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T01:58:38Z","timestamp":1689904718000},"page":"3627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatiotemporal Variations of Global Human-Perceived Heatwave Risks and their Driving Factors Based on Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuwei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4434-1726","authenticated-orcid":false,"given":"Na","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1038\/nclimate3322","article-title":"Global risk of deadly heat","volume":"7","author":"Mora","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S0140-6736(20)32290-X","article-title":"The 2020 report of The Lancet Countdown on health and climate change: Responding to converging crises","volume":"397","author":"Watts","year":"2020","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1093\/qje\/qjac020","article-title":"Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits","volume":"137","author":"Carleton","year":"2022","journal-title":"Q. J. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/nature15725","article-title":"Global non-linear effect of temperature on economic production","volume":"527","author":"Burke","year":"2015","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1038\/nclimate1827","article-title":"Reductions in labour capacity from heat stress under climate warming","volume":"3","author":"Dunne","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.envint.2019.04.025","article-title":"Global heat stress on health, wildfires, and agricultural crops under different levels of climate warming","volume":"128","author":"Sun","year":"2019","journal-title":"Environ. Int."},{"key":"ref_7","first-page":"15","article-title":"Spatiotemporal Change Characteristics of Summer Heatwaves in China in 1961\u20132010","volume":"9","author":"Ye","year":"2013","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"L20714","DOI":"10.1029\/2012GL053361","article-title":"Increasing frequency, intensity and duration of observed global heatwaves and warm spells","volume":"39","author":"Perkins","year":"2012","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1038\/nclimate2410","article-title":"Rapid increase in the risk of extreme summer heat in Eastern China","volume":"4","author":"Sun","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"074006","DOI":"10.1088\/1748-9326\/11\/7\/074006","article-title":"Attributing human mortality during extreme heat waves to anthropogenic climate change","volume":"11","author":"Mitchell","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3861","DOI":"10.1073\/pnas.1617526114","article-title":"Communicating the deadly consequences of global warming for human heat stress","volume":"114","author":"Matthews","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1126\/science.1098704","article-title":"More intense, more frequent, and longer lasting heat waves in the 21st century","volume":"305","author":"Meehl","year":"2004","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"014001","DOI":"10.1088\/1748-9326\/aaa00e","article-title":"Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century","volume":"13","author":"Coffel","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_14","unstructured":"Coffel, E.D., de Sherbinin, A., Horton, R.M., Lane, K., Kienberger, S., and Wilhelmi, O. (2018). Resilience, Elsevier."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"eaaz4571","DOI":"10.1126\/sciadv.aaz4571","article-title":"A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes","volume":"6","author":"Alizadeh","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5096","DOI":"10.1002\/2016GL072281","article-title":"Revisiting summertime hot extremes in China during 1961\u20132015: Overlooked compound extremes and significant changes","volume":"44","author":"Chen","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106553","DOI":"10.1016\/j.atmosres.2022.106553","article-title":"Dependence of compound hot and dry extremes on individual ones across China during 1961\u20132014","volume":"283","author":"Feng","year":"2023","journal-title":"Atmospheric Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1038\/s41467-019-14233-8","article-title":"Anthropogenically-driven increases in the risks of summertime compound hot extremes","volume":"11","author":"Wang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"034042","DOI":"10.1088\/1748-9326\/ac4d4e","article-title":"Detectable anthropogenic influence on summer compound hot events over China from 1965 to 2014","volume":"17","author":"Wang","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1146\/annurev-publhealth-032315-021740","article-title":"Heat, human performance, and occupational health: A key issue for the assessment of global climate change impacts","volume":"37","author":"Kjellstrom","year":"2016","journal-title":"Annu. Rev. Public Health"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1002\/joc.2257","article-title":"Exceedance of heat index thresholds for 15 regions under a warming climate using the wet-bulb globe temperature","volume":"32","author":"Willett","year":"2010","journal-title":"Int. J. Clim."},{"key":"ref_22","first-page":"424","article-title":"Estimation of future global population exposure to heatwaves\u2014Based on the heat stress index","volume":"16","author":"Chen","year":"2020","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"36","DOI":"10.18306\/dlkxjz.2020.01.004","article-title":"Projection of heatwaves by the combined impact of humidity and temperature in China","volume":"39","author":"Chen","year":"2020","journal-title":"Prog. Geogr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nclimate2833","article-title":"Future temperature in south west Asia projected to exceed a threshold for human adaptability","volume":"6","author":"Pal","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1029\/2018EF000873","article-title":"Projected heat stress under 1.5 \u00b0C and 2 \u00b0C global warming scenarios creates unprecedented discomfort for humans in West Africa","volume":"6","author":"Sylla","year":"2018","journal-title":"Earths Future"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7477","DOI":"10.1038\/s41598-017-07536-7","article-title":"Humid heat waves at different warming levels","volume":"7","author":"Russo","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/16742834.2018.1471578","article-title":"Future changes in thermal comfort conditions over China based on multi-RegCM4 simulations","volume":"11","author":"Gao","year":"2018","journal-title":"Atmos. Ocean Sci. Lett."},{"key":"ref_28","first-page":"44","article-title":"Interannual variation of vegetation and precipitation in Pearl River Basin during 1982\u20131999","volume":"30","author":"Wang","year":"2011","journal-title":"J. Trop. Oceanogr."},{"key":"ref_29","first-page":"303","article-title":"Spatio-temporal changes of NDVI in the Pearl River Basin","volume":"25","author":"Wang","year":"2006","journal-title":"Ecol. Sci."},{"key":"ref_30","first-page":"711","article-title":"Spatial and temporal variation of the vegetation cover along Pearl River Basin based on SPOT4-VGT","volume":"34","author":"Jing","year":"2014","journal-title":"J. Guilin Univ. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer classification and regression tree techniques: Bagging and random forests for ecological prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, X., Kong, X., and Shen, Y.-J. (2022). Random Forest Model Has the Potential for Runoff Simulation and Attribution. Water, 14.","DOI":"10.3390\/w14132053"},{"key":"ref_35","first-page":"864","article-title":"Applicability of the random forest model in quantifying the attribution of runoff changes","volume":"30","author":"Wang","year":"2022","journal-title":"Chin. J. Eco-Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103799","DOI":"10.1016\/j.advwatres.2020.103799","article-title":"A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution","volume":"147","author":"Zeng","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guo, W., Ni, X., Mu, Y., Liu, T., and Zhang, J. (2023). Detection and Attribution of Alpine Inland Lake Changes by Using Random Forest Algorithm. Remote Sens., 15.","DOI":"10.3390\/rs15041144"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101011","DOI":"10.1016\/j.uclim.2021.101011","article-title":"Attribution of climate change and human activities to urban water level alterations and factors importance analysis in Central Taihu Basin","volume":"40","author":"Gao","year":"2021","journal-title":"Urban Clim."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108146","DOI":"10.1016\/j.agrformet.2020.108146","article-title":"Attribution of climate and human activities to vegetation change in China using machine learning techniques","volume":"294","author":"Shi","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e2020EF001815","DOI":"10.1029\/2020EF001815","article-title":"Uncovering the Past and Future Climate Drivers of Wheat Yield Shocks in Europe with Machine Learning","volume":"9","author":"Zhu","year":"2021","journal-title":"Earth\u2019s Future"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1017\/S095026881500014X","article-title":"Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections","volume":"143","author":"Gu","year":"2015","journal-title":"Epidemiol. Infect."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/JBHI.2023.3266587","article-title":"Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease","volume":"27","author":"Liang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1111\/risa.13510","article-title":"Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium","volume":"40","author":"Munck","year":"2020","journal-title":"Risk Anal."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tanui, C.K., Benefo, E.O., Karanth, S., and Pradhan, A.K. (2022). A Machine Learning Model for Food Source Attribution of Listeria monocytogenes. Pathogens, 11.","DOI":"10.3390\/pathogens11060691"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"110046","DOI":"10.1016\/j.ecolind.2023.110046","article-title":"Driving mechanisms of urbanization: Evidence from geographical, climatic, social-economic and nighttime light data","volume":"148","author":"Huang","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"155479","DOI":"10.1016\/j.scitotenv.2022.155479","article-title":"Drought and water-use efficiency are dominant environmental factors affecting greenness in the Yellow River Basin, China","volume":"834","author":"Qin","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wei, X., Huang, S., Huang, Q., Liu, D., Leng, G., Yang, H., Duan, W., Li, J., Bai, Q., and Peng, J. (2022). Analysis of Vegetation Vulnerability Dynamics and Driving Forces to Multiple Drought Stresses in a Changing Environment. Remote Sens., 14.","DOI":"10.3390\/rs14174231"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s40333-018-0056-4","article-title":"Attribution of explanatory factors for change in soil organic carbon density in the native grasslands of Inner Mongolia, China","volume":"10","author":"Jin","year":"2018","journal-title":"J. Arid. Land"},{"key":"ref_49","first-page":"1296","article-title":"Spatial variation of extreme temperature change on southern and northern slopes of Shaanxi section in Qinling Mountains during 1960\u20132013","volume":"73","author":"Zhang","year":"2018","journal-title":"Acta Geogr. Sin."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"681","DOI":"10.18306\/dlkxjz.2021.04.012","article-title":"Possible impacts of geographical factors on long-term trends of extreme temperature indices over northern Xinjiang, China","volume":"40","author":"Zhao","year":"2021","journal-title":"Prog. Geogr."},{"key":"ref_51","first-page":"265","article-title":"Remote sensing extraction of geothermal anomaly based on terrain effect correction","volume":"24","author":"Zhou","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_52","first-page":"39","article-title":"Study on the Relationship Between Temperature and other Meteorological Elements","volume":"10","author":"Xu","year":"2020","journal-title":"J. Agric. Catastropholgy"},{"key":"ref_53","first-page":"4682","article-title":"Quantitative Analysis with Geographical Detector on the Influence Factor of Temperature Variation in Northeast China","volume":"54","author":"Yu","year":"2015","journal-title":"Hubei Agric. Sci."},{"key":"ref_54","unstructured":"He, M. (2019). Assessing Heat Wave Risk in Beijing by Multi-Source Remote Sensing. [Master Dissertation, Nanjing University of Information Science and Technology]."},{"key":"ref_55","first-page":"926","article-title":"Study on the influence of land use evolution of scale, structure and pattern on urban thermal environment: A case study of Xi\u2019an","volume":"42","author":"Huang","year":"2022","journal-title":"Sci. Geogr. Sin."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"L14701","DOI":"10.1029\/2008GL034026","article-title":"Temperature trends at high elevations: Patterns across the globe","volume":"35","author":"Pepin","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_57","first-page":"319","article-title":"Identification of potentially dangerous glacial lakes in the northern Tien Shan","volume":"50","author":"Bolch","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1038\/ngeo866","article-title":"Consistent geographical patterns of changes in high-impact European heatwaves","volume":"3","author":"Fischer","year":"2010","journal-title":"Nat. Geosci."},{"key":"ref_59","first-page":"1","article-title":"The energetic basis of the urban heat island","volume":"108","author":"Oke","year":"1982","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.amepre.2008.08.021","article-title":"Climate change and extreme heat events","volume":"35","author":"Luber","year":"2008","journal-title":"Am. J. Prev. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2003.11.005","article-title":"Estimation of land surface temperature\u2013vegetation abundance relationship for urban heat island studies","volume":"89","author":"Weng","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_62","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor\u00e1nyi, A., Mu\u00f1oz Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Rozum, I. (2023). Copernicus Climate Change Service (C3S) Climate Data Store (CDS), European Centre for Medium-Range Weather Forecasts."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e2024792118","DOI":"10.1073\/pnas.2024792118","article-title":"Global urban population exposure to extreme heat","volume":"118","author":"Tuholske","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"164142","DOI":"10.1016\/j.scitotenv.2023.164142","article-title":"Future population exposure to heatwaves in 83 global megacities","volume":"888","author":"Wang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"108049","DOI":"10.1016\/j.envint.2023.108049","article-title":"Global future population exposure to heatwaves","volume":"178","author":"Wang","year":"2023","journal-title":"Environ. Int."},{"key":"ref_66","unstructured":"Rothfusz, L.P. (1990). The Heat Index \u201cEquation\u201d, National Oceanic and Atmospheric Administration. SR90-23."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1175\/1520-0450(1979)018<0861:TAOSPI>2.0.CO;2","article-title":"The assessment of sultriness. Part I: A temperature-humidity index based on human physiology and clothing science","volume":"18","author":"Steadman","year":"1979","journal-title":"J. Appl. Meteorol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1175\/1520-0450(2001)040<0762:OTDOAH>2.0.CO;2","article-title":"On the Definition of a Heat Wave","volume":"40","author":"Robinson","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.envpol.2019.05.132","article-title":"A spatiotemporal interpolation method for the assessment of pollutant concentrations in the Yangtze River estuary and adjacent areas from 2004 to 2013","volume":"252","author":"Wang","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_70","first-page":"2592","article-title":"Identifying the key factors influencing Chinese carbon intensity using machine learning, the random forest algorithm, and evolutionary analysis","volume":"74","author":"Liu","year":"2019","journal-title":"Acta Geogr. Sin."},{"key":"ref_71","first-page":"2057","article-title":"PM2.5 concentration influencing factors in china based on the random forest model","volume":"41","author":"Xia","year":"2020","journal-title":"Environ. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1038\/ngeo2234","article-title":"Recent Arctic amplification and extreme mid-latitude weather","volume":"7","author":"Cohen","year":"2014","journal-title":"Nat. Geosci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.gloplacha.2011.03.004","article-title":"Processes and impacts of Arctic amplification: A research synthesis","volume":"77","author":"Serreze","year":"2011","journal-title":"Glob. Planet. Chang."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1038\/nclimate1580","article-title":"Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings","volume":"2","author":"Yao","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_75","first-page":"98","article-title":"Vulnerability to Heat Waves and Adaptation: A Summary","volume":"28","author":"Yang","year":"2010","journal-title":"Sci. Technol. Rev."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1579\/0044-7447-29.3.157","article-title":"Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions","volume":"29","author":"Doll","year":"2000","journal-title":"Ambio"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ecolecon.2005.03.007","article-title":"Mapping regional economic activity from night-time light satellite imagery","volume":"57","author":"Doll","year":"2006","journal-title":"Ecol. Econ."},{"key":"ref_78","first-page":"865","article-title":"Comparative Studies of the harm characteristic of Hot-dry Wind and High Temperature HeatWaves","volume":"24","author":"Deng","year":"2009","journal-title":"Adv. Earth Sci."},{"key":"ref_79","first-page":"59","article-title":"Spatiotemporal characteristics of urbanization in China from the perspective of remotely sensed big data of nighttime light","volume":"21","author":"Ma","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1002\/joc.2402","article-title":"Simulating climate change in UK cities using a regional climate model, HadRM3","volume":"32","author":"McCarthy","year":"2011","journal-title":"Int. J. Clim."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1024458411589","article-title":"Climatic change in mountain regions: A review of possible impacts","volume":"59","author":"Beniston","year":"2003","journal-title":"Clim. Chang."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1038\/nclimate2563","article-title":"Elevation-dependent warming in mountain regions of the world","volume":"5","author":"Pepin","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Seto, K.C., Fragkias, M., G\u00fcneralp, B., and Reilly, M.K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0023777"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1175\/BAMS-84-9-1205","article-title":"The changing character of precipitation","volume":"84","author":"Trenberth","year":"2003","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1175\/JCLI-D-15-0567.1","article-title":"Variability of soil moisture and sea surface temperatures similarly important for warm-season land climate in the community earth system model","volume":"30","author":"Orth","year":"2017","journal-title":"J. Clim."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10584-012-0659-2","article-title":"Heat waves in the United States: Definitions, patterns and trends","volume":"118","author":"Smith","year":"2012","journal-title":"Clim. Chang."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1289\/ehp.1002313","article-title":"Heat Waves in the United States: Mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities","volume":"119","author":"Anderson","year":"2011","journal-title":"Environ. Health Perspect."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/s40641-015-0016-4","article-title":"Rising temperatures, human health, and the role of adaptation","volume":"1","author":"Hondula","year":"2015","journal-title":"Curr. Clim. Chang. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:16:10Z","timestamp":1760127370000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,21]]},"references-count":88,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143627"],"URL":"https:\/\/doi.org\/10.3390\/rs15143627","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,7,21]]}}}