{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T11:11:48Z","timestamp":1768907508812,"version":"3.49.0"},"reference-count":125,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"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>Urban micro-climate plays an important role in human activities and in ensuring public health. For instance, the urban heat island effect is crucial to the thermal comfort of citizens and tourists, similar to the urban cool island effect\u2019s importance on human and infrastructure resilience. Approximately 35% of global big cities are located in drylands. While existing research has focused on the spatial and temporal changes of surface urban cooling island intensity (SUCII) in drylands in the past, there is a gap in predicting the future spatiotemporal changes in SUCII for cities within these dryland regions. This study aims to forecast the spatiotemporal dynamics of daytime SUCII of representative growing cities with a dry and cold climate. Kerman and Zahedan cities, which are undergoing large urbanization and have harsh hot summer climates, were selected as the study area. Landsat 5 and 8 images and products were utilized for six timestamps within the timeframe of 1986\u20132023. Various methods, including a random forest algorithm, spectral indices, Cellular Automata-Markov (CA-Markov) model, the cross-tabulation model, and spatial overlay and zonal statistics, were employed to assess and model the spatiotemporal changes in SUCII. Initially, historical land cover maps, land surface temperature (LST), surface biophysical characteristics, and SUCII data were prepared, and their spatiotemporal changes were evaluated. Then, projected maps for these variables for the year 2045 were produced. The results indicated that the built-up areas, bare lands, and green spaces of Kerman (Zahedan) city in 1986 were 26.6 km2 (17.6 km2), 103 km2 (92.5 km2), and 44.4 km2 (5.6 km2), respectively, and these values reached 99.3 km2 (41.9 km2), 61.2 km2 (70.7 km2), and 13.5 km2 (3.2 km2) in 2023. The built-up lands area of Kerman (Zahedan) city is expected to increase by approximately 26% (36%) by 2045, while bare land and green space are expected to decrease by about 32% (20%) and 39% (31%), respectively. The greatest rise in average LST of Kerman (Zahedan) city is associated with the conversion of green spaces to barren land, resulting in a notable increase of 5.5 \u00b0C (4.3 \u00b0C) in 1986\u20132023. The conversion of barren land to built-up land in Kerman (Zahedan) city has led to a decrease of 4.6 \u00b0C (3.8 \u00b0C) in LST. The SUCII of Kerman (Zahedan) city for 1986, 1994, 2001, 2008, 2015, and 2023 were \u22120.3 \u00b0C (0.9 \u00b0C), \u22120.8 \u00b0C (0.4 \u00b0C), \u22121.4 \u00b0C (\u22120.5 \u00b0C), \u22121.9 \u00b0C (\u22121.5 \u00b0C), \u22122.6 \u00b0C (\u22122.5 \u00b0C), and \u22123.2 \u00b0C (\u22123.4 \u00b0C), respectively. The projected SUCII in Kerman (Zahedan) city for 2045 is about \u22124.3 \u00b0C (\u22124.5 \u00b0C), indicating an increasing trend in SUCII in the future. The area of zones without SUCII in Kerman (Zahedan) city decreased by 44.8 Km2 (54.8 Km2) from 1986 to 2023, while the areas of low, medium, and high SUCII classes increased by 9.1 Km2 (9.9 Km2), 10.9 Km2 (11.9 Km2), and 24.8 Km2 (33.1 Km2), respectively. The area of non-SUCII and high SUCII classes of Kerman (Zahedan) city in 2045 is expected to decrease by 31.5 Km2 (12.0 Km2) and increase by 51.2 Km2 (9.5 Km2) compared with 2023. The findings of this research indicate that the physical growth of cities in drylands can lead to the moderation of LST, contrary to mechanisms in humid and wet regions.<\/jats:p>","DOI":"10.3390\/rs16234416","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T04:02:18Z","timestamp":1732593738000},"page":"4416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Forecasting Spatiotemporal Dynamics of Daytime Surface Urban Cool Islands in Response to Urbanization in Drylands: Case Study of Kerman and Zahedan Cities, Iran"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-9162","authenticated-orcid":false,"given":"Mohammad Karimi","family":"Firozjaei","sequence":"first","affiliation":[{"name":"Tourism Faculty, University of Tehran, Tehran 1417964743, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3290-1351","authenticated-orcid":false,"given":"Naeim","family":"Mijani","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, Western University, London, ON N6A 5C2, Canada"}]},{"given":"Solmaz","family":"Fathololoumi","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-2935","authenticated-orcid":false,"given":"Jamal Jokar","family":"Arsanjani","sequence":"additional","affiliation":[{"name":"Geoinformatics Research Group, Department of Planning and Development, Aalborg University Copenhagen, A.C. 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