{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:37:41Z","timestamp":1775803061966,"version":"3.50.1"},"reference-count":105,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"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>The Surface Urban Heat Islands (SUHI) phenomenon has adverse environmental consequences on human activities, biophysical and ecological systems. In this study, Land Surface Temperature (LST) from Landsat and Sentinel-2 satellites is used to investigate the contribution of potential factors that generate the SUHI phenomenon. We employ Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) techniques to model the main temporal and spatial SUHI patterns of Cartago, Colombia, for the period 2001\u20132020. We test and evaluate the performance of three different emissivity models to retrieve LST. The fractional vegetation cover model using Sentinel-2 data provides the best results with R2 = 0.78, while the ASTER Global Emissivity Dataset v3 and the land surface emissivity model provide R2 = 0.27 and R2 = 0.26, respectively. Our SUHI model reveals that the factors with the highest impact are the Normalized Difference Water Index (NDWI) and the Normalized Difference Build-up Index (NDBI). Furthermore, we incorporate a weighted Na\u00efve Bayes Machine Learning (NBML) algorithm to identify areas prone to extreme temperatures that can be used to define and apply normative actions to mitigate the negative consequences of SUHI. Our NBML approach demonstrates the suitability of the new SUHI model with uncertainty within 95%, against the 88% given by the Support Vector Machine (SVM) approach.<\/jats:p>","DOI":"10.3390\/rs13214256","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"4256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Remote Sensing Approach for Surface Urban Heat Island Modeling in a Tropical Colombian City Using Regression Analysis and Machine Learning Algorithms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4871-3726","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Garz\u00f3n","sequence":"first","affiliation":[{"name":"Department of Surveying and Cartography Engineering, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"},{"name":"Programa de Ingenier\u00eda Topogr\u00e1fica y Geom\u00e1tica, Universidad del Quind\u00edo, Armenia 630004, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-6874","authenticated-orcid":false,"given":"I\u00f1igo","family":"Molina","sequence":"additional","affiliation":[{"name":"Department of Surveying and Cartography Engineering, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"given":"Jes\u00fas","family":"Velasco","sequence":"additional","affiliation":[{"name":"Department of Surveying and Cartography Engineering, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6779-4341","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Calabia","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"The energetic basis of the urban heat island","volume":"108","author":"Oke","year":"1982","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.ecolind.2012.01.001","article-title":"Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators\u2014An application for the city of Leipzig (Germany)","volume":"18","author":"Schwarz","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100817","DOI":"10.1016\/j.uclim.2021.100817","article-title":"Evaluation and application of a low-cost measurement network to study intra-urban temperature differences during summer 2018 in Bern, Switzerland","volume":"37","author":"Gubler","year":"2021","journal-title":"Urban Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112682","DOI":"10.1016\/j.rse.2021.112682","article-title":"On the land emissivity assumption and Landsat-derived surface urban heat islands: A global analysis","volume":"265","author":"Chakraborty","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1080\/01431161.2012.716548","article-title":"Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing","volume":"34","author":"Sobrino","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","unstructured":"(2019, October 18). WMO Essential Climate Variables. Available online: https:\/\/public.wmo.int\/en\/programmes\/global-climate-observing-system\/essential-climate-variables."},{"key":"ref_8","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.A. (2019). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sens., 11.","DOI":"10.3390\/rs11010048"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.ecolind.2020.107230","article-title":"Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area","volume":"122","author":"Sekertekin","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/j.envsoft.2010.06.011","article-title":"Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression","volume":"25","author":"Li","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1068\/a3768","article-title":"Testing the importance of the explanatory variables in a mixed geographically weighted regression model","volume":"38","author":"Mei","year":"2006","journal-title":"Environ. Plan. A"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.uclim.2013.07.004","article-title":"Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM + data","volume":"5","author":"Senanayake","year":"2013","journal-title":"Urban Clim."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.scs.2016.03.009","article-title":"Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use\/Land Cover in Tehran","volume":"23","author":"Bokaie","year":"2016","journal-title":"Sustain. Cities Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.scs.2017.03.013","article-title":"Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China","volume":"32","author":"Chen","year":"2017","journal-title":"Sustain. Cities Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jafrearsci.2016.11.027","article-title":"Effect of land use\/cover change on land surface temperatures\u2014The Nile Delta, Egypt","volume":"126","author":"Hereher","year":"2017","journal-title":"J. Afr. Earth Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, Y., Xiang, Y., and Zhang, Y. (2019). Urban design factors influencing surface urban heat island in the high-density city of guangzhou based on the local climate zone. Sensors, 19.","DOI":"10.20944\/preprints201906.0010.v1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, J., Wang, J., Xia, X., Lin, R., Wang, Y., Zhou, M., and Fu, D. (2021). Characterization of urban heat islands using city lights: Insights from modis and viirs dnb observations. Remote Sens., 13.","DOI":"10.3390\/rs13163180"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12639","DOI":"10.3390\/rs61212639","article-title":"Characterization of land transitions patterns from multivariate time series using seasonal trend analysis and principal component analysis","volume":"6","author":"Parmentier","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Firozjaei, M.K., Alavipanah, S.K., Liu, H., Sedighi, A., Mijani, N., Kiavarz, M., and Weng, Q. (2019). A PCA-OLS model for assessing the impact of surface biophysical parameters on land surface temperature variations. Remote Sens., 11.","DOI":"10.3390\/rs11182094"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"134307","DOI":"10.1016\/j.scitotenv.2019.134307","article-title":"Estimating Barcelona\u2019s metropolitan daytime hot and cold poles using Landsat-8 Land Surface Temperature","volume":"699","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1038\/s41598-021-81455-6","article-title":"Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air","volume":"11","author":"Shams","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wicki, A., and Parlow, E. (2017). Multiple regression analysis for unmixing of surface temperature data in an urban environment. Remote Sens., 9.","DOI":"10.3390\/rs9070684"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Y., Hou, H., Murayama, Y., Wang, R., and Hu, T. (2021). Quantifying the cooling effect and scale of large inner-city lakes based on landscape patterns: A case study of hangzhou and nanjing. Remote Sens., 13.","DOI":"10.3390\/rs13081526"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Deng, Y., Chen, R., Xie, Y., Xu, J., Yang, J., and Liao, W. (2021). Exploring the impacts and temporal variations of different building roof types on surface urban heat island. Remote Sens., 13.","DOI":"10.3390\/rs13142840"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"150130","DOI":"10.1016\/j.scitotenv.2021.150130","article-title":"An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: A heatwave event in Naples","volume":"805","author":"Oliveira","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hassan, T., Zhang, J., Prodhan, F.A., Pangali Sharma, T.P., and Bashir, B. (2021). Surface urban heat islands dynamics in response to lulc and vegetation across south asia (2000\u20132019). Remote Sens., 13.","DOI":"10.3390\/rs13163177"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"N\u00fa\u00f1ez-Peir\u00f3, M., Mavrogianni, A., Symonds, P., S\u00e1nchez-Guevara S\u00e1nchez, C., and Neila Gonz\u00e1lez, F.J. (2021). Modelling long-term urban temperatures with less training data: A comparative study using neural networks in the city of Madrid. Sustainability, 13.","DOI":"10.3390\/su13158143"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102341","DOI":"10.1016\/j.scs.2020.102341","article-title":"Discerning the success of sustainable planning: A comparative analysis of urban heat island dynamics in Korean new towns","volume":"61","author":"Kwak","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40537-018-0113-z","article-title":"Investigating important urban characteristics in the formation of urban heat islands: A machine learning approach","volume":"5","author":"Yoo","year":"2018","journal-title":"J. Big Data"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Voelkel, J., and Shandas, V. (2017). Towards systematic prediction of urban heat islands: Grounding measurements, assessing modeling techniques. Climate, 5.","DOI":"10.3390\/cli5020041"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100739","DOI":"10.1016\/j.uclim.2020.100739","article-title":"Mapping urban temperature using crowd-sensing data and machine learning","volume":"35","author":"Zumwald","year":"2021","journal-title":"Urban Clim."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"102542","DOI":"10.1016\/j.scs.2020.102542","article-title":"Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh","volume":"64","author":"Kafy","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shi, H., Xian, G., Auch, R., Gallo, K., and Zhou, Q. (2021). Urban Heat Island and its regional impacts using remotely sensed thermal data\u2014A review of recent developments and methodology. Land, 10.","DOI":"10.3390\/land10080867"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alves, E., Anjos, M., and Galvani, E. (2020). Surface urban heat island in middle city: Spatial and temporal characteristics. Urban Sci., 4.","DOI":"10.3390\/urbansci4040054"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.isprsjprs.2020.07.021","article-title":"A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications","volume":"168","author":"Chakraborty","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, Y., Tsou, J.Y., and Li, Y. (2017). Surface urban heat island analysis of shanghai (China) based on the change of land use and land cover. Sustainability, 9.","DOI":"10.3390\/su9091538"},{"key":"ref_37","unstructured":"(2020, December 12). DANE Departamento Administrativo Nacional de Estad\u00edstica, Available online: https:\/\/www.dane.gov.co\/."},{"key":"ref_38","unstructured":"(2018, January 15). Municipio de Cartago Valle del Cauca\u2014Alcald\u00eda de Cartago, Available online: http:\/\/www.cartago.gov.co\/pot-vigente."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/S0034-4257(00)00171-1","article-title":"A Comparative study of land surface emissivity retrieval from NOAA data","volume":"75","author":"Sobrino","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/0034-4257(96)00039-9","article-title":"Mapping land surface emissivity from NDVI: Application to European, African, and South American areas","volume":"57","author":"Valor","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"(2020, January 07). USGS Landsat 8 (L8) Data Users Handbook, Available online: https:\/\/www.usgs.gov\/media\/files\/landsat-8-data-users-handbook."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tarawally, M., Xu, W., Hou, W., and Mushore, T.D. (2018). Comparative analysis of responses of land surface temperature to long-term land use\/cover changes between a coastal and Inland City: A case of Freetown and Bo Town in Sierra Leone. Remote Sens., 10.","DOI":"10.3390\/rs10010112"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"16815","DOI":"10.1029\/97JD01496","article-title":"Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect","volume":"102","author":"Kaufman","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"Naval Research Laboratory, 4555 Overlook Ave","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/2150704X.2014.915434","article-title":"Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance","volume":"5","author":"Baig","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_48","first-page":"256","article-title":"Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis","volume":"11","author":"Zhang","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Karnieli, A., Ohana-Levi, N., Silver, M., Paz-Kagan, T., Panov, N., Varghese, D., Chrysoulakis, N., and Provenzale, A. (2019). Spatial and seasonal patterns in vegetation growth-limiting factors over Europe. Remote Sens., 11.","DOI":"10.3390\/rs11202406"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.buildenv.2012.06.013","article-title":"The interaction of rivers and urban form in mitigating the Urban Heat Island effect: A UK case study","volume":"58","author":"Hathway","year":"2012","journal-title":"Build. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.1080\/01431169508954625","article-title":"Simulating the relationship between thermal emissivity and the Normalized Difference Vegetation Index","volume":"16","author":"Olioso","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s004840050054","article-title":"Some simple relationships between land-surface emissivity, greenness and the plant cover fraction for use in satellite remote sensing","volume":"41","author":"Wittich","year":"1997","journal-title":"Int. J. Biometeorol."},{"key":"ref_54","first-page":"348","article-title":"Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city","volume":"19","author":"Mallick","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2006.07.014","article-title":"Neural network estimation of LAI, fAPAR, fCover and LAI \u00d7 Cab, from top of canopy MERIS reflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_56","unstructured":"Weiss, M., and Baret, F. (2019, December 15). S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER Version 1.1. Available online: https:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_L2B_V1.1.pdf."},{"key":"ref_57","unstructured":"(2020, June 18). USGS Landsat 8 Thermal Infrared Sensor (TIRS) Calibration Notices, Available online: https:\/\/www.usgs.gov\/land-resources\/nli\/landsat\/landsat-8-oli-and-tirs-calibration-notices."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sekertekin, A., and Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens., 12.","DOI":"10.3390\/rs12020294"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Barsi, J.A., Schott, J.R., Palluconi, F.D., and Hook, S.J. (2005, January 22). Validation of a web-based atmospheric correction tool for single thermal band instruments. Proceedings of the Earth Observing Systems X, SPIE, San Diego, CA, USA.","DOI":"10.1117\/12.619990"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1080\/01431160500306906","article-title":"The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM + data","volume":"28","author":"Yue","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"7966","DOI":"10.1002\/2015GL065564","article-title":"The aster global emissivity dataset (ASTER GED): Mapping Earth\u2019s emissivity at 100 meter spatial scale","volume":"42","author":"Hulley","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Park, J., Jang, S., Hong, R., Suh, K., and Song, I. (2020). Development of land cover classification model using AI based fusionnet network. Remote Sens., 12.","DOI":"10.3390\/rs12193171"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.14358\/PERS.76.10.1159","article-title":"Land cover classification in a complex urban-rural landscape with quickbird imagery","volume":"76","author":"Lu","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"647","DOI":"10.3390\/rs70100647","article-title":"A practical split-window algorithm for estimating land surface temperature from landsat 8 data","volume":"7","author":"Du","year":"2015","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s11707-016-0570-7","article-title":"Land surface temperature retrieval from Landsat 8 data and validation with geosensor network","volume":"11","author":"Tan","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1109\/TGRS.2010.2063034","article-title":"Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research","volume":"49","author":"Hulley","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TGRS.2007.904834","article-title":"Land surface emissivity retrieval from different VNIR and TIR sensors","volume":"46","author":"Sobrino","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/LGRS.2014.2312032","article-title":"Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data","volume":"11","author":"Sobrino","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TGRS.2008.2007125","article-title":"Revision of the single-channel algorithm for land surface temperature retrieval from landsat thermal-infrared data","volume":"47","author":"Cristobal","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Rasul, A., Balzter, H., Smith, C., Remedios, J., Adamu, B., Sobrino, J., Srivanit, M., and Weng, Q. (2017). A review on remote sensing of urban heat and cool islands. Land, 6.","DOI":"10.3390\/land6020038"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Machidon, A.L., Del Frate, F., Picchiani, M., Machidon, O.M., and Ogrutan, P.L. (2020). Geometrical approximated principal component analysis for hyperspectral image analysis. Remote Sens., 12.","DOI":"10.3390\/rs12111698"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Adame-Campos, R.L., Ghilardi, A., Gao, Y., Paneque-G\u00e1lvez, J., and Mas, J.F. (2019). Variables selection for aboveground biomass estimations using satellite data: A comparison between relative importance approach and stepwise Akaike\u2019s information criterion. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8060245"},{"key":"ref_75","unstructured":"Hanssens, D.M., Parsons, L.J., and Schultz, R.L. (2002). Parameter estimation and model testing. International Series in Quantitative Marketing, Springer."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Harrell, F. (2015). Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis, Springer. [2nd ed.].","DOI":"10.1007\/978-3-319-19425-7"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"23","DOI":"10.7815\/ijorcs.22.2012.018","article-title":"Multiple linear regression models in outlier detection","volume":"2","author":"Rahman","year":"2012","journal-title":"Int. J. Res. Comput. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, Y., Xie, S., Qin, Q., Wu, S., and Luo, B. (2020). Outlier detection based on residual histogram preference for geometric multi-model fitting. Sensors, 20.","DOI":"10.3390\/s20113037"},{"key":"ref_79","unstructured":"Wilks, D.S. (2019). Statistical Methods in the Atmospheric Sciences, Elsevier. [4th ed.]."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Noi, P.T., and Kappas, M. (2017). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_81","first-page":"101990","article-title":"A new supervised classifier exploiting spectral-spatial information in the Bayesian framework","volume":"86","author":"Barca","year":"2020","journal-title":"Int J. Appl Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Tian, S., Zhang, X., Tian, J., and Sun, Q. (2016). Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China. Remote Sens., 8.","DOI":"10.3390\/rs8110954"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Lv, Z.Y., He, H., Benediktsson, J.A., and Huang, H. (2016). A generalized image scene decomposition-based system for supervised classification of very high resolution remote sensing imagery. Remote Sens., 8.","DOI":"10.3390\/rs8100814"},{"key":"ref_84","first-page":"135","article-title":"Image classification using na\u00efve bayes classifier","volume":"4","author":"Park","year":"2016","journal-title":"Int. J. Comput. Sci. Electron. Eng."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Judah, A., and Hu, B. (2019). The integration of multi-source remotely-sensed data in support of the classification of wetlands. Remote Sens., 11.","DOI":"10.3390\/rs11131537"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.ins.2019.08.071","article-title":"Class-specific attribute value weighting for Naive Bayes","volume":"508","author":"Zhang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_87","first-page":"186","article-title":"The data model concept in statistical mapping","volume":"7","author":"Jenks","year":"1967","journal-title":"Int. Yearb. Cartogr."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s00704-011-0517-6","article-title":"Local regression models for spatial interpolation of urban heat island-an example from Wroc\u0142aw, SW Poland","volume":"108","author":"Szymanowski","year":"2012","journal-title":"Theor. Appl. Climatol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.3390\/rs4113596","article-title":"A quantitative approach for analyzing the relationship between Urban Heat Islands and Land Cover","volume":"4","author":"Ogashawara","year":"2012","journal-title":"Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.scs.2017.05.005","article-title":"An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables","volume":"32","author":"Zhang","year":"2017","journal-title":"Sustain. Cities Soc."},{"key":"ref_91","first-page":"024518","article-title":"Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index","volume":"13","author":"Guha","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"115","DOI":"10.5194\/isprs-archives-XLIII-B5-2020-115-2020","article-title":"Spatial disaggregation of Landsat-derived land surface temperature over a heterogeneous urban landscape using planetscope image derivatives","volume":"43","author":"Cruz","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1080\/01431161.2018.1460513","article-title":"Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product","volume":"40","author":"Duan","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1109\/TGRS.2018.2824828","article-title":"An operational land surface temperature product for landsat thermal data: Methodology and validation","volume":"56","author":"Malakar","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Liu, C., and Li, Y. (2018). Spatio-temporal features of urban heat island and its relationship with land use\/cover in mountainous city: A case study in Chongqing. Sustainability, 10.","DOI":"10.20944\/preprints201805.0167.v1"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Nill, L., Ullmann, T., Kneisel, C., Sobiech-Wolf, J., and Baumhauer, R. (2019). Assessing spatiotemporal variations of landsat land surface temperature and multispectral indices in the Arctic Mackenzie Delta Region between 1985 and 2018. Remote Sens., 11.","DOI":"10.3390\/rs11192329"},{"key":"ref_97","first-page":"946","article-title":"Spatial and temporal distribution of urban heat islands","volume":"605\u2013606","author":"Gleriani","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1080\/22797254.2018.1474494","article-title":"Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy","volume":"51","author":"Guha","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ibrahim, G.R.F. (2017). Urban land use land cover changes and their effect on land surface temperature: Case study using Dohuk City in the Kurdistan Region of Iraq. Climate, 5.","DOI":"10.3390\/cli5010013"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Bonafoni, S., and Keeratikasikorn, C. (2018). Land surface temperature and urban density: Multiyear modeling and relationship analysis using modis and landsat data. Remote Sens., 10.","DOI":"10.3390\/rs10091471"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Molina, I., Martinez, E., Morillo, C., Velasco, J., and Jara, A. (2016). Assessment of data fusion algorithms for earth observation change detection processes. Sensors, 16.","DOI":"10.3390\/s16101621"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Renard, F., Alonso, L., Fitts, Y., Hadjiosif, A., and Comby, J. (2019). Evaluation of the effect of urban redevelopment on surface urban heat islands. Remote Sens., 11.","DOI":"10.3390\/rs11030299"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Alshayeb, M.J., and Chang, J.D. (2018). Variations of PV panel performance installed over a vegetated roof and a conventional black roof. Energies, 11.","DOI":"10.3390\/en11051110"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"138182","DOI":"10.1016\/j.scitotenv.2020.138182","article-title":"Using green to cool the gre: Modelling the cooling effect of green spaces with a high spatial resolution","volume":"724","author":"Grilo","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_105","unstructured":"U.S. Environmental Protection Agency (2021, June 01). \u201cCool Pavements\u201d. Reducing Urban Heat Islands: Compendium of Strategies, Available online: https:\/\/www.epa.gov\/heat-islands\/heat-island-compendium."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4256\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:21:45Z","timestamp":1760167305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,22]]},"references-count":105,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214256"],"URL":"https:\/\/doi.org\/10.3390\/rs13214256","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,22]]}}}