{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:24:27Z","timestamp":1774952667469,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T00:00:00Z","timestamp":1567900800000},"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>Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, \u22120.70, and \u22120.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA\u2013OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA\u2013OLS with the local optimization was found to be more efficient than the one with the regional optimization.<\/jats:p>","DOI":"10.3390\/rs11182094","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T04:12:40Z","timestamp":1568002360000},"page":"2094","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A PCA\u2013OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-9162","authenticated-orcid":false,"given":"Mohammad Karimi","family":"Firozjaei","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran"},{"name":"Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany"}]},{"given":"Seyed Kazem","family":"Alavipanah","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran"},{"name":"Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3090-7338","authenticated-orcid":false,"given":"Hua","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Political Science and Geography, Old Dominion University, Norfolk, VA 23529, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4272-2859","authenticated-orcid":false,"given":"Amir","family":"Sedighi","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran"}]},{"given":"Naeim","family":"Mijani","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0335-3795","authenticated-orcid":false,"given":"Majid","family":"Kiavarz","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2498-0934","authenticated-orcid":false,"given":"Qihao","family":"Weng","sequence":"additional","affiliation":[{"name":"Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1016\/j.rse.2008.07.009","article-title":"A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.scitotenv.2018.09.027","article-title":"Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate","volume":"650","author":"Weng","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.rse.2018.12.008","article-title":"A practical method for reducing terrain effect on land surface temperature using random forest regression","volume":"221","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2012.02.015","article-title":"A two-source trapezoid model for evapotranspiration (ttme) from satellite imagery","volume":"121","author":"Long","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/15481603.2016.1258971","article-title":"Estimation of hourly and daily evapotranspiration and soil moisture using downscaled lst over various urban surfaces","volume":"54","author":"Jiang","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.isprsjprs.2009.03.007","article-title":"Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends","volume":"64","author":"Weng","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2003.11.005","article-title":"Estimation of land surface temperature\u2014Vegetation abundance relationship for urban heat island studies","volume":"89","author":"Weng","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ecolind.2018.03.052","article-title":"Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-markov chain modelling: A case of babol city, iran","volume":"91","author":"Firozjaei","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_9","first-page":"338","article-title":"A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data","volume":"52","author":"Leng","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6136","DOI":"10.3390\/rs6076136","article-title":"Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images","volume":"6","author":"Vlassova","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1111\/j.1745-5871.2010.00686.x","article-title":"Dynamics of land surface temperature in response to land-use\/cover change","volume":"49","author":"Zhou","year":"2011","journal-title":"Geogr. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.landurbplan.2014.11.007","article-title":"Impacts of urban biophysical composition on land surface temperature in urban heat island clusters","volume":"135","author":"Guo","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2014.07.003","article-title":"Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes","volume":"96","author":"Ghosh","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2016.11.010","article-title":"Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: A case study using aster data over a steep-sided valley in morocco","volume":"189","author":"Merlin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.proenv.2010.10.062","article-title":"Analysis of the impact of land use\/land cover change on land surface temperature with remote sensing","volume":"2","author":"Jiang","year":"2010","journal-title":"Procedia Environ. Sci."},{"key":"ref_16","first-page":"203","article-title":"Assessment of land use land cover changes and its impact on variations of land surface temperature in asansol-durgapur development region","volume":"22","author":"Choudhury","year":"2019","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_17","first-page":"417","article-title":"Monitoring agricultural drought in the lower mekong basin using modis ndvi and land surface temperature data","volume":"18","author":"Son","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1175\/2009JCLI3329.1","article-title":"Global warming pattern formation: Sea surface temperature and rainfall","volume":"23","author":"Xie","year":"2010","journal-title":"J. Clim."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1080\/15481603.2018.1548080","article-title":"Statistical analysis of surface urban heat island intensity variations: A case study of babol city, iran","volume":"56","author":"Weng","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.scs.2018.01.024","article-title":"The impact of urban compactness, comfort strategies and energy consumption on tropical urban heat island intensity: A review","volume":"40","author":"Giridharan","year":"2018","journal-title":"Sustain. Cities Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.buildenv.2014.08.029","article-title":"Temporal and spatial variability of urban heat island and thermal comfort within the rotterdam agglomeration","volume":"83","author":"Jacobs","year":"2015","journal-title":"Build. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jenvman.2006.07.016","article-title":"The impact of land use and land cover changes on land surface temperature in a karst area of china","volume":"85","author":"Xiao","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_23","first-page":"018","article-title":"Relationships of LST to NDBI and NDVI in changsha-zhuzhou-xiangtan area based on MODIS data","volume":"2","author":"Li","year":"2009","journal-title":"Sci. Geogr. Sin."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2011.05.027","article-title":"Downscaling land surface temperature for urban heat island diurnal cycle analysis","volume":"117","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/LGRS.2013.2257668","article-title":"Downscaling geostationary land surface temperature imagery for urban analysis","volume":"10","author":"Keramitsoglou","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3094","DOI":"10.1080\/01431161.2014.903442","article-title":"Downscaling modis lst in the east african mountains using elevation gradient and land-cover information","volume":"35","author":"Maeda","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/JSTARS.2016.2519099","article-title":"Evaluation of disaggregation methods for downscaling modis land surface temperature to landsat spatial resolution in barrax test site","volume":"9","author":"Bisquert","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2012.12.014","article-title":"Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats","volume":"131","author":"Zhan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2016.03.006","article-title":"Downscaling land surface temperatures at regional scales with random forest regression","volume":"178","author":"Hutengs","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sismanidis, P., Keramitsoglou, I., Bechtel, B., and Kiranoudis, C. (2016). Improving the downscaling of diurnal land surface temperatures using the annual cycle parameters as disaggregation kernels. Remote Sens., 9.","DOI":"10.3390\/rs9010023"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1080\/01431161.2018.1466082","article-title":"The impact of the terrain effect on land surface temperature variation based on landsat-8 observations in mountainous areas","volume":"40","author":"He","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cao, C., Pan, X., Li, X., and Zhu, X. (2017). Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression. Remote Sens., 9.","DOI":"10.3390\/rs9080789"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.measurement.2018.04.092","article-title":"Thermal sharpening of land surface temperature maps based on the impervious surface index with the tsharp method to aster satellite data: A case study from the metropolitan kuala lumpur, malaysia","volume":"125","author":"Sattari","year":"2018","journal-title":"Measurement"},{"key":"ref_34","first-page":"178","article-title":"Evaluating a thermal image sharpening model over a mixed agricultural landscape in india","volume":"13","author":"Jeganathan","year":"2011","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.3390\/rs4113287","article-title":"A data mining approach for sharpening thermal satellite imagery over land","volume":"4","author":"Gao","year":"2012","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"304","DOI":"10.3390\/s150100304","article-title":"An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from modis thermal infrared band data","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1080\/01431169308953962","article-title":"A comparative analysis of standardised and unstandardised principal components analysis in remote sensing","volume":"14","author":"Eklundh","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","unstructured":"Henebry, G.M., and Rieck, D.R. (1996, January 31\u201331). Applying principal components analysis to image time series: Effects on scene segmentation and spatial structure. Proceedings of the IGARSS\u201996. 1996 International Geoscience and Remote Sensing Symposium, Lincoln, NE, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1080\/01431168508948511","article-title":"Standardized principal components","volume":"6","author":"Singh","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","unstructured":"Jensen, J.R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall Press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Deng, J., Huang, Y., Chen, B., Tong, C., Liu, P., Wang, H., and Hong, Y. (2019). A methodology to monitor urban expansion and green space change using a time series ofmulti-sensor spot and sentinel-2a images. Remote Sens., 11.","DOI":"10.3390\/rs11101230"},{"key":"ref_43","first-page":"1307","article-title":"Long sequence time series evaluation using standardized principal components","volume":"59","author":"Eastman","year":"1993","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.1109\/TGRS.2003.817274","article-title":"Comparison of single-year and multiyear ndvi time series principal components in cold temperate biomes","volume":"41","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bell\u00f3n, B., B\u00e9gu\u00e9, A., Lo Seen, D., de Almeida, C., and Sim\u00f5es, M. (2017). A remote sensing approach for regional-scale mapping of agricultural land-use systems based on ndvi time series. Remote Sens., 9.","DOI":"10.3390\/rs9060600"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(96)00068-5","article-title":"Application of standardized principal component analysis to land-cover characterization using multitemporal avhrr data","volume":"58","author":"Hirosawa","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1080\/01431160902929263","article-title":"Temporal and spatial patterns of ndvi and their relationship to precipitation in the loess plateau of china","volume":"31","author":"Wang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s11273-015-9416-4","article-title":"Principal component analysis applied to a time series of modis images: The spatio-temporal variability of the pantanal wetland, brazil","volume":"23","author":"Penatti","year":"2015","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"Pca-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"17","DOI":"10.5194\/isprs-archives-XLII-4-W4-17-2017","article-title":"Monitoring spatiotemporal changes of heat island in babol city due to land use changes","volume":"42","author":"Panah","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cities.2019.05.001","article-title":"A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model","volume":"93","author":"Firozjaei","year":"2019","journal-title":"Cities"},{"key":"ref_52","unstructured":"USGS (2018, June 01). United States Geological Survey, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_53","unstructured":"LAADS DAAC (2018, June 01). Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center, Available online: https:\/\/ladsweb.nascom.nasa.gov."},{"key":"ref_54","unstructured":"(2018, June 01). Mazandaran Meteorological Organization. Available online: http:\/\/www.Mazmet.Ir\/en."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and van den Bosch, J. (2014, January 24\u201327). Modtran\u00ae 6: A major upgrade of the modtran\u00ae radiative transfer code. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077573"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for landsat mss, tm, etm+, and eo-1 ali sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"12619","DOI":"10.3390\/rs61212619","article-title":"Radiometric cross calibration of landsat 8 operational land imager (oli) and landsat 7 enhanced thematic mapper plus (etm+)","volume":"6","author":"Mishra","year":"2014","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.aeolia.2018.09.001","article-title":"Effect of environmental policies in combating aeolian desertification over sejzy plain of iran","volume":"35","author":"Moghaddam","year":"2018","journal-title":"Aeolian Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.14358\/PERS.72.10.1137","article-title":"Landsat-7 long-term acquisition plan","volume":"72","author":"Arvidson","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in landsat etm+ slc-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"9829","DOI":"10.3390\/rs6109829","article-title":"Land surface temperature retrieval from landsat 8 tirs\u2014Comparison between radiative transfer equation-based method, split window algorithm and single channel method","volume":"6","author":"Yu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"11607","DOI":"10.3390\/rs61111607","article-title":"Landsat-8 thermal infrared sensor (tirs) vicarious radiometric calibration","volume":"6","author":"Barsi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Mu\u00f1oz, J.C., and Sobrino, J.A. (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res. Atmos., 108.","DOI":"10.1029\/2003JD003480"},{"key":"ref_65","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_66","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_67","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (savi)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"Ndwi\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(00)00205-4","article-title":"Narrowband to broadband conversions of land surface albedo i: Algorithms","volume":"76","author":"Liang","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1590\/1807-1929\/agriambi.v20n1p3-8","article-title":"Procedures for calculation of the albedo with oli-landsat 8 images: Application to the brazilian semi-arid","volume":"20","author":"Silva","year":"2016","journal-title":"Rev. Bras. Eng. Agr\u00edcola Ambient."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/01431160110106113","article-title":"Derivation of a tasselled cap transformation based on landsat 7 at-satellite reflectance","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","unstructured":"Liu, Q., Liu, G., Huang, C., Liu, S., and Zhao, J. (2014, January 13\u201318). A tasseled cap transformation for landsat 8 oli toa reflectance images. Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/01431161.2014.995274","article-title":"Comparison of tasselled cap transformations based on the selective bands of landsat 8 oli toa reflectance images","volume":"36","author":"Liu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10661-009-0815-y","article-title":"Evaluation of significant sources influencing the variation of water quality of kandla creek, gulf of katchchh, using pca","volume":"163","author":"Dalal","year":"2010","journal-title":"Environ. Monit. Assess."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/S1352-2310(02)00994-9","article-title":"An examination of the relationship between certain meteorological parameters and surface ozone variations in the baltimore\u2013washington corridor","volume":"37","author":"Vukovich","year":"2003","journal-title":"Atmos. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1016\/j.watres.2006.08.030","article-title":"Assessment of seasonal variations in surface water quality","volume":"40","author":"Ouyang","year":"2006","journal-title":"Water Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/014311699213659","article-title":"Monitoring land-cover changes: A comparison of change detection techniques","volume":"20","author":"Mas","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.buildenv.2017.05.027","article-title":"High-resolution spectral mapping of urban thermal properties with unmanned aerial vehicles","volume":"121","author":"Gaitani","year":"2017","journal-title":"Build. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez-Jim\u00e9nez, R., Ramos-Bernal, R.N., Romero-Calcerrada, R., Arrogante-Funes, P., Tizapa, S.S., and Novillo, C.J. (2017). Thresholding algorithm optimization for change detection to satellite imagery. Colorimetry and Image Processing, IntechOpen.","DOI":"10.5772\/intechopen.71002"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4080","DOI":"10.1109\/TGRS.2011.2128874","article-title":"Modeling urban heat islands and their relationship with impervious surface and vegetation abundance by using aster images","volume":"49","author":"Weng","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2011). Principal Component Analysis, Springer.","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Moutinho, L., and Hutcheson, G.D. (2011). Ordinary least-squares regression. The SAGE Dictionary of Quantitative Management Research, Sage.","DOI":"10.4135\/9781446251119"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Yang, Y., Li, X., Pan, X., Zhang, Y., and Cao, C. (2017). Downscaling land surface temperature in complex regions by using multiple scale factors with adaptive thresholds. Sensors, 17.","DOI":"10.3390\/s17040744"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1016\/j.asr.2009.01.023","article-title":"Surface temperature estimation in singhbhum shear zone of india using landsat-7 etm+ thermal infrared data","volume":"43","author":"Srivastava","year":"2009","journal-title":"Adv. Space Res."},{"key":"ref_86","first-page":"552","article-title":"Geothermal area detection using landsat etm+ thermal infrared data and its mechanistic analysis\u2014A case study in Tengchong, China","volume":"13","author":"Qin","year":"2011","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"5230","DOI":"10.1080\/01431161.2019.1579385","article-title":"An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (SEBAL)","volume":"40","author":"Firozjaei","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2013.03.023","article-title":"Development and verification of a non-linear disaggregation method (nl-distrad) to downscale modis land surface temperature to the spatial scale of landsat thermal data to estimate evapotranspiration","volume":"135","author":"Bindhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.agrformet.2006.05.012","article-title":"Analytical integrated functions for daily solar radiation on slopes","volume":"139","author":"Allen","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_90","unstructured":"Kalogirou, S.A. (2013). Solar Energy Engineering: Processes and Systems, Academic Press."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2019.04.008","article-title":"Normalization of the temporal effect on the modis land surface temperature product using random forest regression","volume":"152","author":"Zhao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1080\/01431161.2012.713142","article-title":"Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using eo-1 hyperion hyperspectral data","volume":"34","author":"Ahmed","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Noi, P.T., Degener, J., and Kappas, M. (2017). Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of modis lst data. Remote Sens., 9.","DOI":"10.3390\/rs9050398"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1037\/a0016973","article-title":"An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests","volume":"14","author":"Strobl","year":"2009","journal-title":"Psychol. Methods"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2094\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:17:40Z","timestamp":1760188660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,8]]},"references-count":94,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11182094"],"URL":"https:\/\/doi.org\/10.3390\/rs11182094","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,8]]}}}