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This study presents an innovative integrated framework combining remote sensing, machine learning, and multi\u2010index interaction modeling to assess UHI risk patterns. Multi\u2010temporal Landsat satellite data from 2018\u20132024 were processed to derive seven key indices: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), Normalized Difference Built\u2010up Index (NDBI), Albedo, and Land Surface Temperature (LST). A supervised Support Vector Machine (SVM) classification was employed for Land Use Land Cover (LULC) mapping, achieving overall accuracies exceeding 85%. The novel contribution of this research lies in the development of a Risk Interaction Model that synthesizes multiple indices into four integrated risk classes: Vegetated &amp; Cool, Bare &amp; Hot, Water\u2010Buffered, and Urban Heat Hotspot zones. Results reveal distinct UHI patterns in the study area, with mean LST variations ranging from 18.72\u00b0C to 33.14\u00b0C over the study period. The framework successfully identified critical UHI hotspots concentrated in built\u2010up areas and provided actionable insights for urban climate adaptation strategies.<\/jats:p>","DOI":"10.1111\/tgis.70334","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:52:19Z","timestamp":1782971539000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Index Interaction of Remote Sensing and Machine Learning Framework for Urban Heat Island Risk Mapping"],"prefix":"10.1111","volume":"30","author":[{"given":"Jianshe","family":"Wang","sequence":"first","affiliation":[{"name":"School of Land Information and Management Henan College of Surveying and Mapping  Zhengzhou Henan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sardar Babakr","family":"Abdalla","sequence":"additional","affiliation":[{"name":"Department of Geography, College of Arts Salahaddin University  Erbil Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3084-1552","authenticated-orcid":false,"given":"Anum","family":"Liaqut","sequence":"additional","affiliation":[{"name":"Department of Geography University of the Punjab  Lahore Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aishah","family":"Alsehaim","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0613-4037","authenticated-orcid":false,"given":"Yahia","family":"Said","sequence":"additional","affiliation":[{"name":"Center for Scientific Research and Entrepreneurship Northern Border University  Arar Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayesha","family":"Shafique","sequence":"additional","affiliation":[{"name":"School of IoT Engineering, Wuxi Taihu University Jiangsu Key (Construction) Laboratory of Intelligent IoT Technology and Applications in Universities  Wuxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nyasha J.","family":"Kavhiza","sequence":"additional","affiliation":[{"name":"Department of Environmental Management, Institute of Environmental Engineering RUDN University  Moscow Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2021.103284"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2025.106842"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10708\u2010024\u201011203\u20106"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2024.123123"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs16050928"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gr.2023.12.015"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.01.011"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2025.3583853"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2025.3578637"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010024\u201076730\u20101"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2025.133225"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2020.100696"},{"key":"e_1_2_13_14_1","doi-asserted-by":"crossref","DOI":"10.1201\/9780429052729","volume-title":"Assessing the Accuracy of Remotely Sensed Data","author":"Congalton R. 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