Determining the Most Suitable Method of Extracting the Surface Temperature Using

Document Type : علمی - پژوهشی


1 M.Sc. in Land Use Planning, faculty of Environment, University of Birjand

2 Assistant Prof., Faculty of Environment, University of Birjand


The world is warming and the world's population is moving to cities. These two truths do not seem to be related; But a phenomenon called urban heat island connects the two. UHI is one of the most common urban climate phenomena in which some urban areas, especially urban centers, become several degrees warmer than the surrounding areas. Studying this phenomenon and examining its mechanism is very important for urban planning. In the present study, in order to estimate LST, four single-channel Landsat algorithms, single window, Planck equation and radiation transfer equation in QGIS software environment between 2000 and 2019 in summer and winter seasons in Birjand city have been used. The effect of land use change on the thermal island has also been investigated. In the present study, ground surface temperature in Birjand city was first extracted using Landsat 7 ETM + satellite imagery and Landsat 8 TIRS / OLI sensors in 2000 and 2019 by four methods. In order to investigate the general ability of algorithms to calculate the surface temperature, the statistical indices of mean square error, Nash-Sutcliffe coefficient, mean absolute error and coefficient of determination were used. The results showed that the Landsat single-channel algorithm for calculating the surface temperature in Birjand is more accurate than other algorithms.


Alavipanah, S.K., 2008, Thermal Remote Sensing and Its Application in Earth Sciences, University of Tehran. bookview.aspx?bookid=1432045.
Aliabadi, K. & Soltanifard, H., 2017, Extracting Vegetation and the Urban Structure of Mashhad Using Newton Interpolation Polynomial and its Relationship with Land Surface Temperature (LST), Iranian Journal of Remote Sensing & GIS, 8(1), PP. 95-108.
Asghari, S.S., Emami H. 2018. Monitoring the Land Surface Temperature and Examining the Relationship between Land Use and Land Surface Temperature Using from OLI and ETM+ Sensor Images, (Case Study: Ardabil city), Journal of Geographical Sciences, 19(53), PP. 195-215. (In Persian). doi:
Atzberger, C., 2013, Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs, Remote Sensing, 5(2), PP. 949-981.
Balcik, F.B. & Ergene, E.M., 2016, Determining the Impacts of Land Cover/Use Categories on Land Surface Temperature Using Landsat8-Oli, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.
Barsi, J.A., Barker, J.L., Schott, J.R., 2003, An Atmospheric Correction Parameter Calculator for a Single Thermal Band Earth-Sensing Instrument, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (IEEE Cat. No. 03CH37477), In., PP.25-21 July 2003, Vol. 2005: PP. 3014-3016.
Chander, G., & Groeneveld, D.P., 2009, Intra‐Annual NDVI Validation of the Landsat 5 TM Radiometric Calibration, International Journal of Remote Sensing, 30(6), PP. 1621-1628.
Chen, A., Yao, L., Sun, R., & Chen, L., 2014, How Many Metrics Are Required to Identify the Effects of the Landscape Pattern on Land Surface Temperature? Ecological Indicators, 45, PP. 424–433.
Duana, S.-B., Li, Zh.L., Wang, Ch., Zhang, Sh., Tang, B.-H., Leng, P., & Gao, M., 2018, Land-Surface Temperature Retrieval from Landsat 8 Single-Channel Thermal Infrared Data in Combination with NCEP Reanalysis Data and ASTER GED Product, International Journal of Remote Sensing, 40(4), PP. 1-16.
Ebrahimi Heravi, B., Rangzan, K., Riahi Bakhtiari, H.R., & TaghiZadeh, A., 2016, Introducing the Most Appropriate Method to Extract Land Surface Temperature Using Landsat 8 Satellite Images in Karaj Metropolitan, Iranian Journal of Remote Sencing & GIS, 8(3), PP. 59-76.
Faizizadeh, B., Didehban, Kh., & Gholamnia, Kh., 2016, Estimation of Earth Surface Temperature Using Landsat 8 Satellite Images and Separate Window Algorithm (Case Study: Mahabad Watershed), Research Quarterly of Geographical Data (SEPEHR), 25(98), PP. 171-181.
Farina, A., & Pieretti, N., 2012, The Soundscape Ecology: A New Frontier of Landscape Research and Its Application to Islands and Coastal Systems, Journal of Marine and Island Cultures, 1(1), PP. 21-26.
Fekrat, H., Asgharisaraskanrood, S. & Alavipanah, S.K., 2020, Estimation of Ardabil Land Surface Temperature Using Landsat Images and Accuracy Assessment of Land Surface Temperature Estimation Methods with Ground Truth Data, Journal of RS and GIS for Natural Resources, 11(4), PP. 114-136. 2020.676476.
García-Santos, V., Cuxart, J., Martínez-Villagrasa, D., Jiménez, M.A., Simó, G., 2018, Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-Tirs Sensor Data, Remote Sensing, 10(9), PP. 1450.
Hennig, C., & Viroli, C., 2015, Quantile-Based Classifiers, 1-12.
Hoseinzadeh, A., Kashki, A., Karami, M. & Javidi Sabaghian, R., 2021, Estimating Land Surface Temperature Changes Using Landsat Satellite Imagery and Three Algorithms, Mono Window, Single Channel and Planck, Case Study of Bojnourd Plain, Environmental Researches, 12(23), PP. 13-26.
Izadfar, H. & Malian, A., 2015, Evaluation of Spatial-Temporal Changes of Thermal Islands of Qazvin City Using Landsat Satellite Series Images, The First International Climate Change Conference,
Jahangiri Manesh, P. 2016, Evaluation of Thermal Islands in the Metropolis of Tehran and the Solution to Reduce the Speed of Expansion of these Islands in the City, International Monthly Road and Construction, 10, PP. 61-77.
Jiménez-Munoz, J.C. & Sobrino, J.A. 2003, A generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data, Journal of Geophysical Research: Atmospheres, 108(22).
Kaviani, M.R., 1998, Microclimatology (1st ed.), Publications of Tehran Hemat Organization.
Lamaro, A.A., Mariñelarena, A., Torrusio, S.E. & Sala, S.E., 2013, Water Surface Temperature Estimation from Landsat 7 ETM+ thermal Infrared Data Using the Generalized Single-Channel Method: Case Study of Embalse del Río Tercero (Córdoba, Argentina), Advances in Space Research, 51(3), PP. 492-500. 10.1016/j.asr.2012.09.032.
Lazzarini, M., Marpu, P.R. and Ghedira, H. (2013). Temperature-land cover interactions: The inversion of urban heat island phenomenon in desert city areas. Remote Sensing of Environment. 130: 136-152.
Liu, L. & Zhang, Y., 2011, Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong, Remote Sensing, 3(7), PP. 1535-1552.
Mai, N.T.H., Hoa, N.T., Nga, T.V.T., Linh, L.D., Chau, T.T.C., Sinh, D.X., ... & Schultsz, C., 2008, Streptococcus Suis Meningitis in Adults in Vietnam, Clinical Infectious Diseases, 46(5), 659-667.
Maleki, S., Shojaeean, A. & Farahmand, Gh., 2018, Assessment of Temporal-Spatial Variability of Heat Islands in Relation to Urban Uses - Case Study: Urmia City, Sepehr Scientific-Research Quarterly Magazine of Geographical Information, 27(105), PP. 183-197. 10.22131/sepehr.2018.31488.
Mease, D., Wyner, A.J. & Buja, A., 2007, Boosted Classification Trees and Class Probability/Quantile Estimation, Journal of Machine Learning Research, 8, PP. 409-439.
Meteorology of South Khorasan province, 2018, Climate Certificate of Birjand Meteorological Station,
Mohammadi, A.R., Khodabandehlou, B. & Babaie, P., 2021, Evaluation of Landuses Temperature Changes in Zanjan in the Period 2013 to 2019 Using Comparison of Land Surface Temperature Estimation Algorithms, Geographical Planning of Space, 11(41), PP. 127-144.
Naseri, N., 2020, Estimating the Land surface temperature using the Single Channel algorithm and examining the impact of land use on temperature changes (Case study: Malayer county). Environmental science studies, 5(2), 2477–2482. article_106509.html.
Ndossi, M. & Avdan, U., 2016, Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps from Landsat: A PyQGIS Plugin, Remote Sensing, 2016, 8, P. 413.
Oke, T.R., 1997, Urban Climates and Global Environmental Change, In: R.D. Thompson & A. Perry (eds.) Applied Climatology: Principles & Practices, New York, NY: Rout Ledge, PP. 273-287.
Qin, Z., Karnieli, A. & Berliner, P., 2001, A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region, International Journal of Remote Sensing, 22(18), PP. 3719-3746.
Rozenstein, O., Qin, Zh., Derimian, Y. & Karnieli, A., 2014, Derivationof Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm, Sensors, 14(4), PP. 5768-5780.
Santamouris, M. & Kolokotsa, D., 2016, Urban Climate Mitigation, First published 2016 by Routledge, New York.
Sekertekin, A. & 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, P. 294.
Senanayake, I.P., Welivitiya, W.D.D.P. & Nadeeka, P.M., 2013, Remote Sensing Based Analysis of Urban Heat Islands with Vegetation Cover in Colombo City, Sri Lanka Using Landsat-7 ETM+ Data, Urban Climate, 5, PP. 19-35.
Shabifar, M., Asari, M., Kouchakzadeh, M., & Mirlotfi, M.. (2010). Lysimetric Evaluation of Common Methods of Calculaying Standard Grass Reference Crop Evapotranspiration in Greenhouse. Iranian Journal of Water Research in Agriculture (Formerly Soil and Water Sciences), 24(1), 13-19. SID.
Shakiba, A., Ziaianfirozabadi, P., Ashorloo, D. & Namdari, S., 2009, Analysis Of Relationship between Land Use/Cover and Urban Heat Island, Using ETM+, Remote Sensing & GIS, 1(1), PP. 39-56.
Sinha, S., Pandey, P.C., Sharma, L.K., Nathawat, M.S., Kumar, P. & Kanga, S., 2014, Remote Estimation of Land Surface Temperature for Different LULC Features of a Moist Deciduous Tropical Forest Region, In: P.K. Srivastava, S. Mukherjee, M. Gupta, T. Islam (ed.s), Remote Sensing Applications in Environmental Research. Springer International Publishing, Cham, PP. 57-68.
Song, Y. & Wu, C., 2016, Examining the Impact of Urban Biophysical Composition and Neighboring Environment on Surface Urban Heat Island Effect, Advances in Space Research, 57(1), PP. 96-109.
Tang, B.H., Shao, K., Li, Z.-L., Wu, H. & Tang, R., 2015, An Improved NDVI-Based Threshold Method for Estimating Land Surface Emissivity Using MODIS Satellite Data, International Journal of Remote Sensing, 36, PP. 4864-4878.
Vlassova, L., Perez-Cabello, F., Nieto, H., Martín, P., Riaño, D. & De La Riva, J., 2014, Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling, RemoteSensing, 6(5), PP. 4345-4368. doi: 10.3390/rs6054345.
Yu, X., Guo, X. & Wu, Z., 2014, Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method, Remote Sensing, 6(10), PP. 9829-9852.
Yuan, F. & Bauer, M.E., 2007, Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery, Remote Sensing of Environment, 106(3), PP. 375-386.
Zhang, J., Wang, Y. & Li, Y., 2006, A C++ Program for Retrieving Land Surface Temperature from the Data of Landsat TM/ETM+ Band6, Computers & Geosciences, 32(10), PP.1796-1805.