{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T20:11:38Z","timestamp":1784232698789,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019BD036"],"award-info":[{"award-number":["ZR2019BD036"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 \u00b0C, whereas December has the lowest (\u221219.12 \u00b0C). The ranking of SUHI intensity is as follows: summer (4.89 \u00b0C) &gt; winter with snow cover (1.94 \u00b0C) &gt; spring (1.16 \u00b0C) &gt; autumn (0.89 \u00b0C) &gt; winter without snow cover (\u22121.24 \u00b0C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.<\/jats:p>","DOI":"10.3390\/rs12183006","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T10:24:09Z","timestamp":1600165449000},"page":"3006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0477-5845","authenticated-orcid":false,"given":"Chaobin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengqin","family":"Yan","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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuelei","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuli","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1038\/nature01675","article-title":"Impact of urbanization and land-use change on climate","volume":"423","author":"Kalnay","year":"2003","journal-title":"Nature"},{"key":"ref_2","first-page":"115","article-title":"Land Use\/Land Cover changes dynamics and their effects on Surface Urban Heat Island in Bucharest, Romania","volume":"80","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/S1001-0742(08)60019-4","article-title":"A review on the generation, determination and mitigation of Urban Heat Island","volume":"20","author":"Rizwan","year":"2008","journal-title":"J. Environ. Sci."},{"key":"ref_4","first-page":"30","article-title":"Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures","volume":"67","author":"Deilami","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., and Sobrino, J. (2018). Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sens., 11.","DOI":"10.3390\/rs11010048"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/joc.859","article-title":"Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island","volume":"23","author":"Arnfield","year":"2003","journal-title":"Int. J. Climatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/S0140-6736(06)68079-3","article-title":"Climate change and human health: Present and future risks","volume":"367","author":"McMichael","year":"2006","journal-title":"Lancet"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.buildenv.2018.06.007","article-title":"Long-term impact of rapid urbanization on urban climate and human thermal comfort in hot-arid environment","volume":"142","author":"Mahmoud","year":"2018","journal-title":"Build. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.isprsjprs.2009.03.007","article-title":"Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends","volume":"64","author":"Weng","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2006.09.003","article-title":"Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery","volume":"106","author":"Yuan","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.scitotenv.2015.02.062","article-title":"A study of the hourly variability of the urban heat island effect in the Greater Athens Area during summer","volume":"517","author":"Kourtidis","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, C., Wang, R., Zhang, S., Ji, C., and Fu, X. (2019). Characterizing the Hourly Variation of Urban Heat Islands in a Snowy Climate City during Summer. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16142467"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3354\/cr008001","article-title":"Daily air temperature interpolated at high spatial resolution over a large mountainous region","volume":"8","author":"Dodson","year":"1997","journal-title":"Clim. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, C., Yan, F., and Zhang, S. (2020). Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city. J. Environ. Manag., 265.","DOI":"10.1016\/j.jenvman.2020.110563"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.isprsjprs.2019.08.012","article-title":"Comparison of surface and canopy urban heat islands within megacities of eastern China","volume":"156","author":"Hu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.ecolind.2016.09.009","article-title":"Comparison of the urban heat island intensity quantified by using air temperature and Landsat land surface temperature in Hangzhou, China","volume":"72","author":"Sheng","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ecolind.2018.01.044","article-title":"The influence of different data and method on estimating the surface urban heat island intensity","volume":"89","author":"Yao","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, F., Zhang, X., Murayama, Y., and Morimoto, T. (2020). Impacts of Land Cover\/Use on the Urban Thermal Environment: A Comparative Study of 10 Megacities in China. Remote Sens., 12.","DOI":"10.3390\/rs12020307"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, A., Yang, J., Xiao, X., Xia, J., Jin, C., and Li, X. (2020). Influences of urban spatial form on urban heat island effects at the community level in China. Sustain. Cities Soc., 53.","DOI":"10.1016\/j.scs.2019.101972"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/j.scitotenv.2018.12.308","article-title":"Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia","volume":"659","author":"Jamei","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_22","first-page":"104","article-title":"The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data","volume":"64","author":"Peres","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kalnay, E., Cai, M., Li, H., and Tobin, J. (2006). Estimation of the impact of land-surface forcings on temperature trends in eastern United States. J. Geophys. Res. Atmos., 111.","DOI":"10.1029\/2005JD006555"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., and Zhou, W. (2019). Spatial patterns and driving factors of surface urban heat island intensity: A comparative study for two agriculture-dominated regions in China and the USA. Sustain. Cities Soc., 48.","DOI":"10.1016\/j.scs.2019.101518"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alexander, C. (2020). Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int. J. Appl. Earth Obs. Geoinf., 86.","DOI":"10.1016\/j.jag.2019.102013"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2018.06.010","article-title":"Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas","volume":"215","author":"Peng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2017.02.020","article-title":"Spatio-temporal analysis of the relationship between 2D\/3D urban site characteristics and land surface temperature","volume":"193","author":"Berger","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9337","DOI":"10.1038\/s41598-017-09628-w","article-title":"Assessing the relationship between surface urban heat islands and landscape patterns across climatic zones in China","volume":"7","author":"Yang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3175","DOI":"10.1016\/j.rse.2011.07.003","article-title":"Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures","volume":"115","author":"Schwarz","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.scitotenv.2017.07.217","article-title":"Temporal trends of surface urban heat islands and associated determinants in major Chinese cities","volume":"609","author":"Yao","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, L., and Dirmeyer, P.A. (2016). Adapting observationally based metrics of biogeophysical feedbacks from land cover\/land use change to climate modeling. Environ. Res. Lett., 11.","DOI":"10.1088\/1748-9326\/11\/3\/034002"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1080\/01431169608949128","article-title":"The comparison of vegetation index and surface temperature composites for urban heat-island analysis","volume":"17","author":"Gallo","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2012.12.020","article-title":"Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach","volume":"131","author":"Deng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1080\/01431169508954549","article-title":"Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities","volume":"16","author":"Ridd","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3249","DOI":"10.1016\/j.rse.2011.07.008","article-title":"Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China","volume":"115","author":"Li","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhong, Q., Ma, J., Zhao, B., Wang, X., Zong, J., and Xiao, X. (2019). Assessing spatial-temporal dynamics of urban expansion, vegetation greenness and photosynthesis in megacity Shanghai, China during 2000\u20132016. Remote Sens. Environ., 233.","DOI":"10.1016\/j.rse.2019.111374"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1007\/s10980-016-0437-z","article-title":"People, landscape, and urban heat island: Dynamics among neighborhood social conditions, land cover and surface temperatures","volume":"31","author":"Huang","year":"2016","journal-title":"Landsc. Ecol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.infrared.2019.01.018","article-title":"Characteristics and trend analysis of the relationship between land surface temperature and nighttime light intensity levels over China","volume":"97","author":"Li","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.04.010","article-title":"Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China","volume":"152","author":"Huang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/j.scitotenv.2018.03.350","article-title":"Effects of urban form on the urban heat island effect based on spatial regression model","volume":"634","author":"Yin","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cheung, P.K., and Jim, C.Y. (2019). Effects of urban and landscape elements on air temperature in a high-density subtropical city. Build. Environ., 164.","DOI":"10.1016\/j.buildenv.2019.106362"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.uclim.2018.09.004","article-title":"Influence of vegetation on the morning land surface temperature in a tropical humid urban area","volume":"26","author":"Acero","year":"2018","journal-title":"Urban Clim."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.scs.2018.10.022","article-title":"Investigation into the differences among several outdoor thermal comfort indices against field survey in subtropics","volume":"44","author":"Fang","year":"2019","journal-title":"Sustain. Cities Soc."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, C., He, X., Yu, L., Yang, J., Yan, F., Bu, K., Chang, L., and Zhang, S. (2017). The Cooling Effect of Urban Parks and Its Monthly Variations in a Snow Climate City. Remote Sens., 9.","DOI":"10.3390\/rs9101066"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, C., He, X., Yan, F., Yu, L., Bu, K., Yang, J., Chang, L., and Zhang, S. (2017). Mapping the influence of land use\/land cover changes on the urban heat island effect\u2014A case study of Changchun, China. Sustainability, 9.","DOI":"10.3390\/su9020312"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Z."},{"key":"ref_49","unstructured":"USGS (2020, September 14). Landsat 8 Hand Book, Available online: https:\/\/www.usgs.gov\/media\/files\/landsat-8-data-users-handbook."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2004.02.003","article-title":"Land surface temperature retrieval from LANDSAT TM 5","volume":"90","author":"Sobrino","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_51","first-page":"589","article-title":"Study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"9","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating impervious surface distribution by spectral mixture analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.08.003","article-title":"Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery","volume":"93","author":"Wu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00704-005-0192-6","article-title":"Temporal characteristics of the Beijing urban heat island","volume":"87","author":"Liu","year":"2007","journal-title":"Theor. Appl. Climatol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1175\/JAMC-D-12-0125.1","article-title":"Spatial and Temporal Characteristics of Beijing Urban Heat Island Intensity","volume":"52","author":"Yang","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.landurbplan.2018.10.023","article-title":"Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature","volume":"184","author":"Masoudi","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.scitotenv.2018.06.209","article-title":"Patterns of land change and their potential impacts on land surface temperature change in Yangon, Myanmar","volume":"643","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.landurbplan.2014.11.007","article-title":"Impacts of urban biophysical composition on land surface temperature in urban heat island clusters","volume":"135","author":"Guo","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.scs.2018.10.049","article-title":"An approach to examining performances of cool\/hot sources in mitigating\/enhancing land surface temperature under different temperature backgrounds based on landsat 8 image","volume":"44","author":"He","year":"2019","journal-title":"Sustain. Cities Soc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ufug.2019.04.008","article-title":"Optimizing urban greenspace spatial pattern to mitigate urban heat island effects: Extending understanding from local to the city scale","volume":"41","author":"Li","year":"2019","journal-title":"Urban For. Urban Green."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yang, C., He, X., Wang, R., Yan, F., Yu, L., Bu, K., Yang, J., Chang, L., and Zhang, S. (2017). The Effect of Urban Green Spaces on the Urban Thermal Environment and Its Seasonal Variations. Forests, 8.","DOI":"10.3390\/f8050153"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Qi, J.-D., He, B.-J., Wang, M., Zhu, J., and Fu, W.-C. (2019). Do grey infrastructures always elevate urban temperature? No, utilizing grey infrastructures to mitigate urban heat island effects. Sustain. Cities Soc., 46.","DOI":"10.1016\/j.scs.2018.12.020"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.pce.2019.01.008","article-title":"The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing","volume":"110","author":"Zheng","year":"2019","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wu, C., Li, J., Wang, C., Song, C., Chen, Y., Finka, M., and la Rosa, D. (2019). Understanding the relationship between urban blue infrastructure and land surface temperature. Sci. Total Environ., 694.","DOI":"10.1016\/j.scitotenv.2019.133742"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.uclim.2018.01.004","article-title":"Potentials of meteorological characteristics and synoptic conditions to mitigate urban heat island effects","volume":"24","author":"He","year":"2018","journal-title":"Urban Clim."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:10:13Z","timestamp":1760177413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":65,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12183006"],"URL":"https:\/\/doi.org\/10.3390\/rs12183006","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}