{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T05:46:28Z","timestamp":1776231988595,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool\u2013heat and heat\u2013cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.<\/jats:p>","DOI":"10.3390\/rs13214338","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"4338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":160,"title":["Spatial Variability and Temporal Heterogeneity of Surface Urban Heat Island Patterns and the Suitability of Local Climate Zones for Land Surface Temperature Characterization"],"prefix":"10.3390","volume":"13","author":[{"given":"Ziqi","family":"Zhao","sequence":"first","affiliation":[{"name":"Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China"},{"name":"Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China"},{"name":"Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8983-8613","authenticated-orcid":false,"given":"Ayyoob","family":"Sharifi","sequence":"additional","affiliation":[{"name":"Graduate School of Humanities and Social Science, Graduate School of Advances Science and Engineering, Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Higashi Hiroshima 739-8530, Japan"}]},{"given":"Xin","family":"Dong","sequence":"additional","affiliation":[{"name":"Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China"},{"name":"Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China"}]},{"given":"Lidu","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Applied Ecology, Chinese Academy Sciences, Shenyang 110016, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8841-0711","authenticated-orcid":false,"given":"Bao-Jie","family":"He","sequence":"additional","affiliation":[{"name":"Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China"},{"name":"Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2018). 68% of the World Population Projected to Live in Urban Areas by 2050. 2018 Revision of World Urbanization Prospects, United Nations."},{"key":"ref_2","unstructured":"Department of Economic and Social Affairs, Sustainable Development, United Nations (2021). 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