{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T13:26:26Z","timestamp":1776691586021,"version":"3.51.2"},"reference-count":123,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Portuguese Foundation for Science and Technology","doi-asserted-by":"publisher","award":["CEECINST\/00077\/2021"],"award-info":[{"award-number":["CEECINST\/00077\/2021"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons\u2014winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.<\/jats:p>","DOI":"10.3390\/rs16030454","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T09:57:42Z","timestamp":1706090262000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3326-1146","authenticated-orcid":false,"given":"Mohammad","family":"Mansourmoghaddam","sequence":"first","affiliation":[{"name":"Center for Remote Sensing and GIS Research, Shahid Beheshti University, Tehran 1983969411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3694-6936","authenticated-orcid":false,"given":"Iman","family":"Rousta","sequence":"additional","affiliation":[{"name":"Department of Geography, Yazd University, Yazd 8915813135, Iran"},{"name":"Institute for Atmospheric Sciences-Weather and Climate, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6083-1517","authenticated-orcid":false,"given":"Hamidreza","family":"Ghafarian Malamiri","sequence":"additional","affiliation":[{"name":"Department of Geography, Yazd University, Yazd 8915813135, Iran"}]},{"given":"Mostafa","family":"Sadeghnejad","sequence":"additional","affiliation":[{"name":"Department of Geography and Geospatial Sciences, Kansas State University, 920 N17th Street, Manhattan, KS 66506-2904, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9235-8119","authenticated-orcid":false,"given":"Jaromir","family":"Krzyszczak","sequence":"additional","affiliation":[{"name":"Institute of Agrophysics, Polish Academy of Sciences, Do\u015bwiadczalna 4, 20-290 Lublin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3709-4103","authenticated-orcid":false,"given":"Carla Sofia Santos","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden"},{"name":"Polytechnic Institute of Coimbra, Applied Research Institute, Rua da Miseric\u00f3rdia, Lagar dos Corti\u00e7os, S. Martinho do Bispo, 3045-093 Coimbra, Portugal"},{"name":"Research Centre for Natural Resources, Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Bencanta, 3045-601 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tong, D., Chu, J., Han, Q., and Liu, X. (2022). How land finance drives urban expansion under fiscal pressure: Evidence from Chinese cities. 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