Estimating extreme heat event over New Delhi Region, India using Satellite Data
Article Main
Abstract
On May 26, 2024, Delhi experienced an extreme heat event, with temperatures soaring to record-breaking levels, exceeding 52°C, which was later revised to 46°C (114.8°F) by the India Meteorological Department.Amidst the controversy surrounding sensor failures, this study examines land surface temperature (LST) in Delhi using Landsat 9 OLI and MODIS Aqua data, focusing on the extreme heat event of May 26, 2024. This date was selected due to anticipated extreme heat and the availability of data. Satellite observations revealed temperatures as high as 56°C, with distinct spatial variations across Delhi. The western region recorded the highest temperatures, while the eastern region, influenced by the Yamuna River, exhibited cooler conditions. Emissivity values from Landsat (0.970–0.984) and MODIS (0.973–0.987) were analyzed, showing a strong correlation with surface temperatures: lower emissivity values corresponded to greener areas and lower temperatures, whereas higher values were linked to elevated temperatures. The study highlights the impact of surface characteristics on thermal behavior and underscores the role of urban heat islands (UHIs), particularly in northwestern Delhi. These UHIs, driven by industrial activity, dense settlements, and low-albedo materials, resulted in 2–4°C temperature differences between urban and rural areas, posing health risks to vulnerable populations. Mitigation strategies such as expanding green spaces and relocating high-emission industries are recommended to alleviate these risks. Despite the absence of field data, global studies validating Landsat and MODIS-derived LST support the accuracy of this study’s findings. Thus, the spatial pattern of LST remains reliable even with minor errors ranging from 1 to 2 °C. The study will help for strategic planning and mitigation measures to address extreme heat events in urban areas.
Article Details
Article Details
Keywords
Extreme heat event, Land Surface Temperature, Landsat, Modis, Delhi
References
Abrams, J. F., & McGregor, G. R. (2015). Urban heat islands and climate change: A review of the literature. Urban Climate, 14, 1–18. https://doi.org/10.1016/j.uclim.2015.06.001
Ailian, C.X., Angela, Y., Ranhao, S., Liding, C. (2014). Effect of urban green patterns on surface urban cool islands and its seasonal variations, urban forestry & urban greening, 13(4), 646-654. https://doi.org/10.1016/j.ufug.2014.07.006.
Anand, N. (2024). India Sizzles at 50 degrees Celsius: A look at world record temperatures. Business Standard (Published June 2, 2024). Retrieved online from https://www.business-standard.com/india-news/india-sizzles-above-50-degree-celsius-a-look-at-world-temperatures-trends-124052900572_1.html
Armson, D., Stringer, P., Ennos, A.R. (2012). The Effect of tree shade and grass on surface and globe temperatures in an Urban area, Urban Forestry & urban greening, 11(3), 245-255. https://doi.org/10.1016/j.ufug.2012.05.002.
Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using Landsat 8 satellite data. Journal of Sensors, 2016, 1-9. https://doi.org/10.1155/2016/1480307
Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L., and Radocinski, R.G. (2014). Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration. Remote Sensing, 6, 11607–11626.
Berger, C., Rosentreter, J., Voltersen, M., Baumgart, C., Schmullius, C., Hese, S. (2017). Spatio-temporal analysis of the relationship between 2d/3d urban site characteristics and land surface temperature, remote sensing of environment, Volume 193, 225-243. https://doi.org/10.1016/j.rse.2017.02.020.
Berra, E.F., Gaulton, R., Barr, S. (2019). Assessing Spring Phenology of a Temperate Woodland: A Multiscale Comparison of Ground, Unmanned Aerial Vehicle and Landsat Satellite Observations. Remote Sensing of Environment, 223, 229–242.
Chen, Y., & Lu, R. (2014). Spatiotemporal changes in frequency and intensity of high-temperature events in China during 1961–2014. Journal of Geographical Sciences, 27(8), 1027–1043. https://doi.org/10.1007/s11442-017-1419-z
Cook, M., Schott, J.R., Mandel, J. and Raqueno, N. (2014). Development of an Operational Calibration Methodology for the Landsat Thermal Data Archive and Initial Testing of the Atmospheric Compensation Component of a Land Surface Temperature (LST) Product from the Archive. Remote Sensing, 6(11), 11244–11266.
Dayal, S. (2024). Why have temperatures reached record highs in India? Reuters (May 31, 2024). Retrieved online from https://www.reuters.com/world/india/why-have-temperatures-reached-record-highs-india-2024-05-31/
Duan, S.B., Li, Z.L., Zhao, W., Wu, P., Huang, C., Han, X.J., Shang, G. (2020). Validation of Landsat Land Surface Temperature Product in the Conterminous United States Using In Situ Measurements from SURFRAD, ARM, and NDBC Sites. International Journal of Digital Earth, 14(5), 640–660. https://doi.org/10.1080/17538947.2020.1862319
Government of Delhi. (2023). Economic Survey of Delhi 2023–24. https://static.investindia.gov.in/s3fs-public/2024-03/Economic%20Survey%20of%20Delhi%202023-24.pdf
Grover, A., Singh, R.B. (2015). Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai. Environments, 2, 125-138. https://doi.org/10.3390/environments2020125
Guillevic, P., Göttsche, F., Nickeson, J., Hulley, G., Ghent, D., Yu, Y., Trigo, I., Hook, S., Sobrino, J. A., Remedios, J., Román, M., & Camacho, F. (2017). Land surface temperature product validation best practice protocol (Version 1.0, p. 60). In P. Guillevic, F. Göttsche, J. Nickeson, & M. Román (Eds.), Best practice for satellite-derived land product validation. Land Product Validation Subgroup (WGCV/CEOS).
Gunawardena, K.R., Wells, M.J. & Kershaw, T. (2017). Utilizing Green and Blue Space to Mitigate Urban Heat Island Intensity, Science of The Total Environment, 584, 1040-1055. https://doi.org/10.1016/j.scitotenv.201 7.01.158.
Jimenez-Munoz, J.C. and Sobrino, J. (2004). Error Sources on the Land Surface Temperature Retrieved from Thermal Infrared Single Channel Remote Sensing Data. Int. J. Remote Sens., 27(5), 999–1014.
Jiménez-Muñoz, J.C., Cristóbal, J., Sobrino, J.A., Sòria, Ninyerola, G.M. and Pons., X. (2009). Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval from Landsat Thermal-Infrared Data. IEEE Trans. Geosci. Remote Sens., 47, 339–349.
Jimenez-Munoz, J.C., Sobrino, J. Skokovic, D., Mattar, C. & Cristobal, J. (2014). Land Surface Temperature Retrieval Methods from Landsat-8 Thermal Infrared Sensor Data. IEEE Geosci. Remote Sens. Lett., 11(10) 1840–1843.
Laraby, KG. (2017). Landsat Surface Temperature Product: Global Validation and Uncertainty Estimation. Thesis. Rochester Institute of Technology. https://www.cis.rit.edu/~cnspci/references/theses/phd/laraby2017.pdf.
Mallick, J., Rahman, A., & Singh, C. K. (2013). Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India. Advances in Space Research, 52(4), 639–649. https://doi.org/10.1016/j.asr.2013.04.025
Michael, A., Hiroji, T., Glynn, H., Koki, I., David, P., Tom, C., & Jeffrey, K. (2015). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) after fifteen years: Review of Global Products, International Journal of Applied Earth Observation and Geoinformation, 38, 292-301. https://doi.org/10.1016/j.jag.2015.01.013.
.
Nancy, Y., Nugroho, S. T., &Wonorahardjo, S. (2022). Effect of high-rise buildings on the surrounding thermal environment. Building and Environment, 207(Part A), 108393. https://doi.org/10.1016/j.buildenv.2021.108393
Nda, M., Adnan, M. S., Ahmad, K. A., Usman, N., Mohammad Razi, M. A., & Daud, Z. (2018). A review on the causes, effects and mitigation of climate changes on the environmental aspects. International Journal of Integrated Engineering, 10(4), 66–72.
Niclòs, R., Miguez-Macho, G., & Guijarro, J. A. (2023). The use of satellite remote sensing for monitoring urban heat islands: A global review. Urban Climate, 42, 100758. https://doi.org/10.1016/j.uclim.2022.100758
Sayler, K., & Glynn, T. (2022). Landsat 9 – Data user handbook (Version 1.0). U.S. Department of the Interior, United States Geological Survey.
Shahfahad, M., Talukdar, S., Rihan, M., & Rahman, A. (2022). Modeling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environmental Development and Sustainability. https://doi.org/10.1007/s10668-022-02221-0
Sobrino, J. A., &Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International Journal of Remote Sensing, 21(2), 353–366. https://doi.org/10.1080/01431 1600210876.
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.
Suhail, M., Khan, M. S., & Faridi, R. A. (2019). Assessment of urban heat islands effect and land surface temperature of Noida, India by using Landsat satellite data. MAPAN, 34, 431–441. https://doi.org/10.1007/s12647-019-00309-9.
Tan, K., Liao, Z., & Du, P. (2017). Land surface temperature retrieval from Landsat 8 data and validation with geosensor network. Frontiers in Earth Science, 11(1), 1–12. https://doi.org/10.1007/s11707-017-0624-7.
Twumasi, Y. A., Fosu, M. A., & Osei, E. O. (2021). Estimation of land surface temperature from Landsat data in urban areas. Remote Sensing, 13(10), 1–18. https://doi.org/10.3390/rs13101999
U.S. Geological Survey (USGS) (2024). Using the USGS Landsat Level-1 data product. U.S. Geological Survey. Retrieved May 26, 2024, from https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product.
Weng, Q., Dengshang, L., & Jacquelyn, S. (2004). Estimation of land surface temperature and 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.
White-Newsome, J. L., McCormick, S., Sampson, N., Buxton, M. A., O'Neill, M. S., Gronlund, C. J., Catalano, L., Conlon, K. C., & Parker, E. A. (2014). Strategies to reduce the harmful effects of extreme heat events: A four-city study. International Journal of Environmental Research and Public Health, 11(2), 1960–1988. https://doi.org/10.3390/ijerph110201960
Yang, Y., Gatto, E., Gao, Z., Buccolieri, R., Morakinyo, T. E., & Lan, H. (2019). The "plant evaluation model" for the assessment of the impact of vegetation on outdoor microclimate in the urban environment. Building and Environment, 159, 106–151. https://doi.org/10.1016/j.buildenv.2019.05.029
Ailian, C.X., Angela, Y., Ranhao, S., Liding, C. (2014). Effect of urban green patterns on surface urban cool islands and its seasonal variations, urban forestry & urban greening, 13(4), 646-654. https://doi.org/10.1016/j.ufug.2014.07.006.
Anand, N. (2024). India Sizzles at 50 degrees Celsius: A look at world record temperatures. Business Standard (Published June 2, 2024). Retrieved online from https://www.business-standard.com/india-news/india-sizzles-above-50-degree-celsius-a-look-at-world-temperatures-trends-124052900572_1.html
Armson, D., Stringer, P., Ennos, A.R. (2012). The Effect of tree shade and grass on surface and globe temperatures in an Urban area, Urban Forestry & urban greening, 11(3), 245-255. https://doi.org/10.1016/j.ufug.2012.05.002.
Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using Landsat 8 satellite data. Journal of Sensors, 2016, 1-9. https://doi.org/10.1155/2016/1480307
Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L., and Radocinski, R.G. (2014). Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration. Remote Sensing, 6, 11607–11626.
Berger, C., Rosentreter, J., Voltersen, M., Baumgart, C., Schmullius, C., Hese, S. (2017). Spatio-temporal analysis of the relationship between 2d/3d urban site characteristics and land surface temperature, remote sensing of environment, Volume 193, 225-243. https://doi.org/10.1016/j.rse.2017.02.020.
Berra, E.F., Gaulton, R., Barr, S. (2019). Assessing Spring Phenology of a Temperate Woodland: A Multiscale Comparison of Ground, Unmanned Aerial Vehicle and Landsat Satellite Observations. Remote Sensing of Environment, 223, 229–242.
Chen, Y., & Lu, R. (2014). Spatiotemporal changes in frequency and intensity of high-temperature events in China during 1961–2014. Journal of Geographical Sciences, 27(8), 1027–1043. https://doi.org/10.1007/s11442-017-1419-z
Cook, M., Schott, J.R., Mandel, J. and Raqueno, N. (2014). Development of an Operational Calibration Methodology for the Landsat Thermal Data Archive and Initial Testing of the Atmospheric Compensation Component of a Land Surface Temperature (LST) Product from the Archive. Remote Sensing, 6(11), 11244–11266.
Dayal, S. (2024). Why have temperatures reached record highs in India? Reuters (May 31, 2024). Retrieved online from https://www.reuters.com/world/india/why-have-temperatures-reached-record-highs-india-2024-05-31/
Duan, S.B., Li, Z.L., Zhao, W., Wu, P., Huang, C., Han, X.J., Shang, G. (2020). Validation of Landsat Land Surface Temperature Product in the Conterminous United States Using In Situ Measurements from SURFRAD, ARM, and NDBC Sites. International Journal of Digital Earth, 14(5), 640–660. https://doi.org/10.1080/17538947.2020.1862319
Government of Delhi. (2023). Economic Survey of Delhi 2023–24. https://static.investindia.gov.in/s3fs-public/2024-03/Economic%20Survey%20of%20Delhi%202023-24.pdf
Grover, A., Singh, R.B. (2015). Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai. Environments, 2, 125-138. https://doi.org/10.3390/environments2020125
Guillevic, P., Göttsche, F., Nickeson, J., Hulley, G., Ghent, D., Yu, Y., Trigo, I., Hook, S., Sobrino, J. A., Remedios, J., Román, M., & Camacho, F. (2017). Land surface temperature product validation best practice protocol (Version 1.0, p. 60). In P. Guillevic, F. Göttsche, J. Nickeson, & M. Román (Eds.), Best practice for satellite-derived land product validation. Land Product Validation Subgroup (WGCV/CEOS).
Gunawardena, K.R., Wells, M.J. & Kershaw, T. (2017). Utilizing Green and Blue Space to Mitigate Urban Heat Island Intensity, Science of The Total Environment, 584, 1040-1055. https://doi.org/10.1016/j.scitotenv.201 7.01.158.
Jimenez-Munoz, J.C. and Sobrino, J. (2004). Error Sources on the Land Surface Temperature Retrieved from Thermal Infrared Single Channel Remote Sensing Data. Int. J. Remote Sens., 27(5), 999–1014.
Jiménez-Muñoz, J.C., Cristóbal, J., Sobrino, J.A., Sòria, Ninyerola, G.M. and Pons., X. (2009). Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval from Landsat Thermal-Infrared Data. IEEE Trans. Geosci. Remote Sens., 47, 339–349.
Jimenez-Munoz, J.C., Sobrino, J. Skokovic, D., Mattar, C. & Cristobal, J. (2014). Land Surface Temperature Retrieval Methods from Landsat-8 Thermal Infrared Sensor Data. IEEE Geosci. Remote Sens. Lett., 11(10) 1840–1843.
Laraby, KG. (2017). Landsat Surface Temperature Product: Global Validation and Uncertainty Estimation. Thesis. Rochester Institute of Technology. https://www.cis.rit.edu/~cnspci/references/theses/phd/laraby2017.pdf.
Mallick, J., Rahman, A., & Singh, C. K. (2013). Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India. Advances in Space Research, 52(4), 639–649. https://doi.org/10.1016/j.asr.2013.04.025
Michael, A., Hiroji, T., Glynn, H., Koki, I., David, P., Tom, C., & Jeffrey, K. (2015). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) after fifteen years: Review of Global Products, International Journal of Applied Earth Observation and Geoinformation, 38, 292-301. https://doi.org/10.1016/j.jag.2015.01.013.
.
Nancy, Y., Nugroho, S. T., &Wonorahardjo, S. (2022). Effect of high-rise buildings on the surrounding thermal environment. Building and Environment, 207(Part A), 108393. https://doi.org/10.1016/j.buildenv.2021.108393
Nda, M., Adnan, M. S., Ahmad, K. A., Usman, N., Mohammad Razi, M. A., & Daud, Z. (2018). A review on the causes, effects and mitigation of climate changes on the environmental aspects. International Journal of Integrated Engineering, 10(4), 66–72.
Niclòs, R., Miguez-Macho, G., & Guijarro, J. A. (2023). The use of satellite remote sensing for monitoring urban heat islands: A global review. Urban Climate, 42, 100758. https://doi.org/10.1016/j.uclim.2022.100758
Sayler, K., & Glynn, T. (2022). Landsat 9 – Data user handbook (Version 1.0). U.S. Department of the Interior, United States Geological Survey.
Shahfahad, M., Talukdar, S., Rihan, M., & Rahman, A. (2022). Modeling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environmental Development and Sustainability. https://doi.org/10.1007/s10668-022-02221-0
Sobrino, J. A., &Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International Journal of Remote Sensing, 21(2), 353–366. https://doi.org/10.1080/01431 1600210876.
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.
Suhail, M., Khan, M. S., & Faridi, R. A. (2019). Assessment of urban heat islands effect and land surface temperature of Noida, India by using Landsat satellite data. MAPAN, 34, 431–441. https://doi.org/10.1007/s12647-019-00309-9.
Tan, K., Liao, Z., & Du, P. (2017). Land surface temperature retrieval from Landsat 8 data and validation with geosensor network. Frontiers in Earth Science, 11(1), 1–12. https://doi.org/10.1007/s11707-017-0624-7.
Twumasi, Y. A., Fosu, M. A., & Osei, E. O. (2021). Estimation of land surface temperature from Landsat data in urban areas. Remote Sensing, 13(10), 1–18. https://doi.org/10.3390/rs13101999
U.S. Geological Survey (USGS) (2024). Using the USGS Landsat Level-1 data product. U.S. Geological Survey. Retrieved May 26, 2024, from https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product.
Weng, Q., Dengshang, L., & Jacquelyn, S. (2004). Estimation of land surface temperature and 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.
White-Newsome, J. L., McCormick, S., Sampson, N., Buxton, M. A., O'Neill, M. S., Gronlund, C. J., Catalano, L., Conlon, K. C., & Parker, E. A. (2014). Strategies to reduce the harmful effects of extreme heat events: A four-city study. International Journal of Environmental Research and Public Health, 11(2), 1960–1988. https://doi.org/10.3390/ijerph110201960
Yang, Y., Gatto, E., Gao, Z., Buccolieri, R., Morakinyo, T. E., & Lan, H. (2019). The "plant evaluation model" for the assessment of the impact of vegetation on outdoor microclimate in the urban environment. Building and Environment, 159, 106–151. https://doi.org/10.1016/j.buildenv.2019.05.029
Issue
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)
How to Cite
Estimating extreme heat event over New Delhi Region, India using Satellite Data. (2025). Journal of Applied and Natural Science, 17(2), 802-809. https://doi.org/10.31018/jans.v17i2.6477
