An analytical study on urban indices and land surface temperature
Abstract
Any urban landscape needs to investigate the rising trend of land surface temperature (LST) with its surface materials. The present study analyzes the relationship of LST with three urban indices namely normalized difference built-up index (NDBI), urban index (UI), and built-up index (BUI) (by Pearson correlation coefficient method) using nine Landsat 8 OLI and TIRS data of May from 2013 to 2021 in a tropical Indian city, Raipur. Results show that the mean LST of the city was above 40 oC in 2013 but it is controlled in successive years by executing some eco-friendly activities. All the indices build a moderate to strong positive correlation with LST. NDBI is the least deviating index and it generates the best correlation. As surface materials are directly responsible for the rise of LST, suitable ecological planning is necessary for long-term urban thermal sustainability.
Keyword : built-up index (BUI), Landsat, land surface temperature (LST), normalized difference built-up index (NDBI), urban index (UI)
How to Cite
Guha, S., & Govil, H. (2024). An analytical study on urban indices and land surface temperature. Journal of Environmental Engineering and Landscape Management, 32(3), 231–240. https://doi.org/10.3846/jeelm.2024.21835
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abir, F. A., & Saha, R. (2021). Assessment of land surface temperature and land cover variability during winter: A spatio-temporal analysis of Pabna municipality in Bangladesh. Environmental Challenges, 4, Article 100167. https://doi.org/10.1016/j.envc.2021.100167
Arshad, A., Zhang, W., Zaman, M. A., Dilawar, A., & Sajid, Z. (2019). Monitoring the impacts of spatio-temporal land-use changes on the regional climate of city Faisalabad, Pakistan. Annals of GIS, 25(1), 57–70. https://doi.org/10.1080/19475683.2018.1543205
Artis, D. A., & Carnahan, W. H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12(4), 313–329.
Bonafoni, S. (2015). Spectral index utility for summer urban heating analysis. Journal of Applied Remote Sensing, 9(1), Article 096030. https://doi.org/10.1117/1.JRS.9.096030
Cai, X., Yang, J., Zhang, Y., Xiao, X., & Xia, J. (2023). Cooling island effect in urban parks from the perspective of internal park landscape. Humanities and Social Sciences Communications, 10, Article 674. https://doi.org/10.1057/s41599-023-02209-5
Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62, 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Chen, S., Haase, D., Qureshi, S., & Firozjaei, M. K. (2022). Integrated land use and urban function impacts on land surface temperature: Implications on urban heat mitigation in Berlin with eight-type spaces. Sustainable Cities and Society, 83, Article 103944. https://doi.org/10.1016/j.scs.2022.103944
Chen, S., Yang, J., Yu, W., Ren, J., & Xia, J. C. (2023). Relationship between urban spatial form and seasonal land surface temperature under different grid scales. Sustainable Cities and Society, 89, Article 104374. https://doi.org/10.1016/j.scs.2022.104374
Chen, W., Liu, L., Zhang, C., Wang, J., Wang, J., & Pan, Y. (2004, September 20–24). Monitoring the seasonal bare soil areas in Beijing using multi-temporal TM images. In Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (pp. 3379–3382), Anchorage, AK, USA.
Ezimand, K., Azadbakht, M., & Aghighi, H. (2021). Analyzing the effects of 2D and 3D urban structures on LST changes using remotely sensed data. Sustainable Cities and Society, 74, Article 103216. https://doi.org/10.1016/j.scs.2021.103216
Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205–214. https://doi.org/10.1016/j.rse.2015.12.040
Ghaderizadeh, S., Abbasi-Moghadam, D., Sharifi, A., Tariq, A., & Qin, S. (2022). Multiscale Dual-Branch residual spectral–spatial network with attention for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5455–5467. https://doi.org/10.1109/JSTARS.2022.3188732
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., Briggs, J. M., & Grimm, N. (2008). Global change and the ecology of cities. Science, 319(5864), 756–760. https://doi.org/10.1126/science.1150195
Guha, S., & Govil, H. (2022). Annual assessment on the relationship between land surface temperature and six remote sensing indices using Landsat data from 1988 to 2019. Geocarto International, 37(15), 4292–4311. https://doi.org/10.1080/10106049.2021.1886339
Guha, S., & Govil, H. (2021). A long-term monthly analytical study on the relationship of LST with normalized difference spectral indices. European Journal of Remote Sensing, 54(1), 487–512. https://doi.org/10.1080/22797254.2021.1965496
Guha, S., Govil, H., Gill, N., & Dey, A. (2021). A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quaternary International, 575–576, 249–258. https://doi.org/10.1016/j.quaint.2020.06.041
Guha, S., Govil, H., Taloor, A. K., Gill, N., & Dey, A. (2022). Land surface temperature and spectral indices: A seasonal study of Raipur City. Geodesy and Geodynamics, 13(1), 72–82. https://doi.org/10.1016/j.geog.2021.05.002
Hao, X., Li, W., & Deng, H. (2016). The oasis effect and summer temperature rise in arid regions-case study in Tarim Basin. Scientific Reports, 6, Article 35418. https://doi.org/10.1038/srep35418
Hussain, S., Qin, S., Nasim, W., Bukhari, M. A., Mubeen, M., Fahad, S., Raza, A., Abdo, H. G., Tariq, A., Mousa, B. G, Mumtaz, F., & Aslam, M. (2022). Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020. Atmosphere, 13(10), Article 1609. https://doi.org/10.3390/atmos13101609
Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A., & Qin, S. (2022). Modeling and predicting land use land cover spatiotemporal changes: A case study in Chalus Watershed, Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5496–5513. https://doi.org/10.1109/JSTARS.2022.3189528
Kawamura, M., Jayamana, S., & Tsujiko, Y. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. International Archives of Photogrammetry and Remote Sensing, 31(B7), 321–326.
Lee, J., Lee, S. S., & Chi, K. H. (2010). Development of an urban classification method using a built-up index [Conference presentation]. Sixth WSEAS International Conference on Remote Sensing, Iwate Prefectural University, Japan.
Majeed, M., Tariq, A., Anwar, M. M., Khan, A. M., Arshad, F., Mumtaz, F., Farhan, M., Zhang, L., Zafar, A., Aziz, M., Abbasi, S., Rahman, G., Hussain, S., Waheed, M., Fatima, K., & Shaukat, S. (2021). Monitoring of land use–land cover change and potential causal factors of climate change in Jhelum District, Punjab, Pakistan, through GIS and multi-temporal satellite data. Land, 10(10), Article 1026. https://doi.org/10.3390/land10101026
Nichol, J. E. (2005). Remote sensing of urban heat islands by day and night. Photogrammetric Engineering & Remote Sensing, 71, 613–621. https://doi.org/10.14358/PERS.71.5.613
Office of the Surveyor General of India. (n.d.). http://www.surveyofindia.gov.in/
Oke, T. R. (1988). The urban energy balance. Progress in Physical Geography: Earth Environment, 12(4), 471–508. https://doi.org/10.1177/030913338801200401
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108, 1–24. https://doi.org/10.1002/qj.49710845502
Ogunjobi, K. O., Adamu, Y., Akinsanola, A. A., & Orimoloye, I. R. (2018). Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria. Royal Society Open Science, 5, Article 180661. https://doi.org/10.1098/rsos.180661
Orimoloye, I. R., & Ololade, O. O. (2020). Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology. International Journal of Environmental Science and Technology, 17, 4465–4480. https://doi.org/10.1007/s13762-020-02789-8
Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43, 39–47.
Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators, 122, Article 107230. https://doi.org/10.1016/j.ecolind.2020.107230
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Sobrino, J. A., Raissouni, N., Li, Z. L. (2001). A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment, 75(2), 256–266. https://doi.org/10.1016/S0034-4257(00)00171-1
Tariq, A., & Shu, H. (2020). CA-Markov Chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), Article 3402. https://doi.org/10.3390/rs12203402
Tariq, A., & Mumtaz, F. (2023). Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data. Environmental Science and Pollution Research, 30, 23908–23924. https://doi.org/10.1007/s11356-022-23928-3
Tariq, A., Yan, J., Gagnon, A. S., Khan, M. R., & Mumtaz, F. (2022a). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science, 26(3), 302–320. https://doi.org/10.1080/10095020.2022.2100287
Tariq, A., Yan, J., & Mumtaz, F. (2022b). Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. Physics and Chemistry of the Earth, 128, Article 103286. https://doi.org/10.1016/j.pce.2022.103286
Tariq, A., Mumtaz, F., Zeng, X., Baloch, M. Y. J., & Moazzam, M. F. U. (2022c). Spatio-temporal variation of seasonal heat islands mapping of Pakistan during 2000–2019, using day-time and night-time land surface temperatures MODIS and meteorological stations data. Remote Sensing Applications: Society and Environment, 27, Article 100779. https://doi.org/10.1016/j.rsase.2022.100779
Tariq, A., Riaz, I., Ahmad, Z., Yang, B., Amin, M., Kausar, R., Andleeb, S., Farooqi, M. A., & Rafiq, M. (2020). Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environmental of Earth Sciences, 79, Article 40. https://doi.org/10.1007/s12665-019-8766-2
Tomlinson, C. J., Chapman, L., Trones, J. E., & Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: A review. Meteorological Applications, 18, 296–306. https://doi.org/10.1002/met.287
Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119–132. https://doi.org/10.1016/j.isprsjprs.2017.01.001
U.S. Geological Survey. (n.d.). https://earthexplorer.usgs.gov/
Weng, Q. H., Lu, D. S., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
Willie, Y. A., Pillay, R., Zhou, L., & Orimoloye, I. R. (2019). Monitoring spatial pattern of land surface thermal characteristics and urban growth: A case study of King Williams using remote sensing and GIS. Earth Science Informatics, 12, 447–464. https://doi.org/10.1007/s12145-019-00391-2
Zhang, R., Yang, J., & Xia, J. C. (2023). Optimal allocation of local climate zones based on heat vulnerability perspective. Sustainable Cities and Society, 99, Article 104981. https://doi.org/10.1016/j.scs.2023.104981
Zhang, Y., Odeh, I. O. A., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264. https://doi.org/10.1016/j.jag.2009.03.001
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, 583–594. https://doi.org/10.1080/01431160304987
Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., & Sobrino, J. A. (2019). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 11(1), Article 48. https://doi.org/10.3390/rs11010048
Arshad, A., Zhang, W., Zaman, M. A., Dilawar, A., & Sajid, Z. (2019). Monitoring the impacts of spatio-temporal land-use changes on the regional climate of city Faisalabad, Pakistan. Annals of GIS, 25(1), 57–70. https://doi.org/10.1080/19475683.2018.1543205
Artis, D. A., & Carnahan, W. H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12(4), 313–329.
Bonafoni, S. (2015). Spectral index utility for summer urban heating analysis. Journal of Applied Remote Sensing, 9(1), Article 096030. https://doi.org/10.1117/1.JRS.9.096030
Cai, X., Yang, J., Zhang, Y., Xiao, X., & Xia, J. (2023). Cooling island effect in urban parks from the perspective of internal park landscape. Humanities and Social Sciences Communications, 10, Article 674. https://doi.org/10.1057/s41599-023-02209-5
Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62, 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Chen, S., Haase, D., Qureshi, S., & Firozjaei, M. K. (2022). Integrated land use and urban function impacts on land surface temperature: Implications on urban heat mitigation in Berlin with eight-type spaces. Sustainable Cities and Society, 83, Article 103944. https://doi.org/10.1016/j.scs.2022.103944
Chen, S., Yang, J., Yu, W., Ren, J., & Xia, J. C. (2023). Relationship between urban spatial form and seasonal land surface temperature under different grid scales. Sustainable Cities and Society, 89, Article 104374. https://doi.org/10.1016/j.scs.2022.104374
Chen, W., Liu, L., Zhang, C., Wang, J., Wang, J., & Pan, Y. (2004, September 20–24). Monitoring the seasonal bare soil areas in Beijing using multi-temporal TM images. In Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (pp. 3379–3382), Anchorage, AK, USA.
Ezimand, K., Azadbakht, M., & Aghighi, H. (2021). Analyzing the effects of 2D and 3D urban structures on LST changes using remotely sensed data. Sustainable Cities and Society, 74, Article 103216. https://doi.org/10.1016/j.scs.2021.103216
Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205–214. https://doi.org/10.1016/j.rse.2015.12.040
Ghaderizadeh, S., Abbasi-Moghadam, D., Sharifi, A., Tariq, A., & Qin, S. (2022). Multiscale Dual-Branch residual spectral–spatial network with attention for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5455–5467. https://doi.org/10.1109/JSTARS.2022.3188732
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., Briggs, J. M., & Grimm, N. (2008). Global change and the ecology of cities. Science, 319(5864), 756–760. https://doi.org/10.1126/science.1150195
Guha, S., & Govil, H. (2022). Annual assessment on the relationship between land surface temperature and six remote sensing indices using Landsat data from 1988 to 2019. Geocarto International, 37(15), 4292–4311. https://doi.org/10.1080/10106049.2021.1886339
Guha, S., & Govil, H. (2021). A long-term monthly analytical study on the relationship of LST with normalized difference spectral indices. European Journal of Remote Sensing, 54(1), 487–512. https://doi.org/10.1080/22797254.2021.1965496
Guha, S., Govil, H., Gill, N., & Dey, A. (2021). A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quaternary International, 575–576, 249–258. https://doi.org/10.1016/j.quaint.2020.06.041
Guha, S., Govil, H., Taloor, A. K., Gill, N., & Dey, A. (2022). Land surface temperature and spectral indices: A seasonal study of Raipur City. Geodesy and Geodynamics, 13(1), 72–82. https://doi.org/10.1016/j.geog.2021.05.002
Hao, X., Li, W., & Deng, H. (2016). The oasis effect and summer temperature rise in arid regions-case study in Tarim Basin. Scientific Reports, 6, Article 35418. https://doi.org/10.1038/srep35418
Hussain, S., Qin, S., Nasim, W., Bukhari, M. A., Mubeen, M., Fahad, S., Raza, A., Abdo, H. G., Tariq, A., Mousa, B. G, Mumtaz, F., & Aslam, M. (2022). Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020. Atmosphere, 13(10), Article 1609. https://doi.org/10.3390/atmos13101609
Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A., & Qin, S. (2022). Modeling and predicting land use land cover spatiotemporal changes: A case study in Chalus Watershed, Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5496–5513. https://doi.org/10.1109/JSTARS.2022.3189528
Kawamura, M., Jayamana, S., & Tsujiko, Y. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. International Archives of Photogrammetry and Remote Sensing, 31(B7), 321–326.
Lee, J., Lee, S. S., & Chi, K. H. (2010). Development of an urban classification method using a built-up index [Conference presentation]. Sixth WSEAS International Conference on Remote Sensing, Iwate Prefectural University, Japan.
Majeed, M., Tariq, A., Anwar, M. M., Khan, A. M., Arshad, F., Mumtaz, F., Farhan, M., Zhang, L., Zafar, A., Aziz, M., Abbasi, S., Rahman, G., Hussain, S., Waheed, M., Fatima, K., & Shaukat, S. (2021). Monitoring of land use–land cover change and potential causal factors of climate change in Jhelum District, Punjab, Pakistan, through GIS and multi-temporal satellite data. Land, 10(10), Article 1026. https://doi.org/10.3390/land10101026
Nichol, J. E. (2005). Remote sensing of urban heat islands by day and night. Photogrammetric Engineering & Remote Sensing, 71, 613–621. https://doi.org/10.14358/PERS.71.5.613
Office of the Surveyor General of India. (n.d.). http://www.surveyofindia.gov.in/
Oke, T. R. (1988). The urban energy balance. Progress in Physical Geography: Earth Environment, 12(4), 471–508. https://doi.org/10.1177/030913338801200401
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108, 1–24. https://doi.org/10.1002/qj.49710845502
Ogunjobi, K. O., Adamu, Y., Akinsanola, A. A., & Orimoloye, I. R. (2018). Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria. Royal Society Open Science, 5, Article 180661. https://doi.org/10.1098/rsos.180661
Orimoloye, I. R., & Ololade, O. O. (2020). Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology. International Journal of Environmental Science and Technology, 17, 4465–4480. https://doi.org/10.1007/s13762-020-02789-8
Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43, 39–47.
Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators, 122, Article 107230. https://doi.org/10.1016/j.ecolind.2020.107230
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Sobrino, J. A., Raissouni, N., Li, Z. L. (2001). A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment, 75(2), 256–266. https://doi.org/10.1016/S0034-4257(00)00171-1
Tariq, A., & Shu, H. (2020). CA-Markov Chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), Article 3402. https://doi.org/10.3390/rs12203402
Tariq, A., & Mumtaz, F. (2023). Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data. Environmental Science and Pollution Research, 30, 23908–23924. https://doi.org/10.1007/s11356-022-23928-3
Tariq, A., Yan, J., Gagnon, A. S., Khan, M. R., & Mumtaz, F. (2022a). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science, 26(3), 302–320. https://doi.org/10.1080/10095020.2022.2100287
Tariq, A., Yan, J., & Mumtaz, F. (2022b). Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. Physics and Chemistry of the Earth, 128, Article 103286. https://doi.org/10.1016/j.pce.2022.103286
Tariq, A., Mumtaz, F., Zeng, X., Baloch, M. Y. J., & Moazzam, M. F. U. (2022c). Spatio-temporal variation of seasonal heat islands mapping of Pakistan during 2000–2019, using day-time and night-time land surface temperatures MODIS and meteorological stations data. Remote Sensing Applications: Society and Environment, 27, Article 100779. https://doi.org/10.1016/j.rsase.2022.100779
Tariq, A., Riaz, I., Ahmad, Z., Yang, B., Amin, M., Kausar, R., Andleeb, S., Farooqi, M. A., & Rafiq, M. (2020). Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environmental of Earth Sciences, 79, Article 40. https://doi.org/10.1007/s12665-019-8766-2
Tomlinson, C. J., Chapman, L., Trones, J. E., & Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: A review. Meteorological Applications, 18, 296–306. https://doi.org/10.1002/met.287
Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119–132. https://doi.org/10.1016/j.isprsjprs.2017.01.001
U.S. Geological Survey. (n.d.). https://earthexplorer.usgs.gov/
Weng, Q. H., Lu, D. S., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
Willie, Y. A., Pillay, R., Zhou, L., & Orimoloye, I. R. (2019). Monitoring spatial pattern of land surface thermal characteristics and urban growth: A case study of King Williams using remote sensing and GIS. Earth Science Informatics, 12, 447–464. https://doi.org/10.1007/s12145-019-00391-2
Zhang, R., Yang, J., & Xia, J. C. (2023). Optimal allocation of local climate zones based on heat vulnerability perspective. Sustainable Cities and Society, 99, Article 104981. https://doi.org/10.1016/j.scs.2023.104981
Zhang, Y., Odeh, I. O. A., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264. https://doi.org/10.1016/j.jag.2009.03.001
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, 583–594. https://doi.org/10.1080/01431160304987
Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., & Sobrino, J. A. (2019). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 11(1), Article 48. https://doi.org/10.3390/rs11010048