Mapping land surface temperature in Nigeria using Modis data

  • Authors

    2024-07-04
    https://doi.org/10.14419/rftz0816
  • Nigeria; Land Surface Temperature; MODIS Data; Climate Change; Sustainability.

  • In developing countries such as Nigeria, rapid land degradation is an unavoidable phenomenon. Nigeria is experiencing significant growth as its population clusters continue to expand due to migration from rural areas. Unfortunately, one of the negative consequences of this de-velopment is the increase in impervious areas, which results in higher land surface temperatures (LST). A number of studies have highlight-ed the utilization of Landsat thermal data and the usefulness of MODIS data in the preparation of LST maps. This current study aimed to produce LST maps for Nigeria using 8-day average MODIS MOD11A2 images for the years 2001, 2011, and 2015, allowing for an as-sessment of the average LST for the country. Analysis of the LST maps reveals that the average temperature in Nigeria is 32°C, with the highest and lowest LSTs recorded as 43°C and 20°C respectively within the selected years. These results indicate that impervious areas contain a greater number of pixels falling into the high to very high-class category. It is crucial to adopt appropriate climate change mitiga-tion measures in light of these findings.

  • References

    1. Chen, L., Ma, Z.G., and Zhao, T.B., (2016). Modeling and analysis of the potential impacts on regional climate due to vegetation deg-radation over arid and semi-arid regions of china. Clim. Change 144, 461–473. https://doi.org/10.1007/s10584-016-1847-2.
    2. Hereher, M.E., (2017). Effect of land use/cover change on land surface temperatures – the nile delta, Egypt. J. Afr. Earth Sc. 126, 75–83. https://doi.org/10.1016/j.jafrearsci.2016.11.027.
    3. Jiang, L., Li, N.N., Fu, Z.T., and Zhang, J.P., (2015). Long-range correlation behaviors for the 0-cm average ground surface tempera-ture and average air temperature over china. Theor. Appl. Climatol. 119, 25–31. https://doi.org/10.1007/s00704-013-1080-0.
    4. NourEldeen, N., Mao, K., Yuan, Z., Shen, X., Xu, T., Qin, Z., 2020. Analysis of the spatiotemporal change in land surface temperature for a long-term sequence in africa (2003–2017). Remote Sens. 12, 488–511. https://doi.org/10.3390/rs12030488.
    5. Li, Z.L., Tang, B.H., Wu, H., Ren, H.Z., Yan, G.J., Wan, Z.M., Trigo, I.F., and Sobrino, J.A., (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ. 131, 14–37 https://doi.org/10.1016/j.rse.2012.12.008.
    6. Mukherjee, S., Joshi, P.K., and Garg, R.D., (2016). Analysis of urban built-up areas and surface urban heat island using downscaled modis derived land surface temperature data. Geocarto Int. 32, 900–918. https://doi.org/10.1080/10106049.2016.1222634.
    7. Ndossi, M., Avdan, U., (2016). Inversion of land surface temperature (lst) using terra aster data: A comparison of three algorithms. Remote Sens. 8, 993–1012. https://doi.org/10.3390/rs8120993.
    8. Peng, J., Ma, J., Liu, Q., Liu, Y., Hu, Y., Li, Y., and Yue, Y., (2018). Spatial-temporal change of land surface temperature across 285 cities in china: An urban-rural contrast perspective. Sci. Total Environ. 635, 487–497. https://doi.org/10.1016/j.scitotenv.2018.04.105.
    9. Phan, T.N., Kappas, M., Nguyen, K.T., Tran, T.P., Tran, Q.V., and Emam, A.R., (2019). Evaluation of modis land surface temperature products for daily air surface temperature estimation in northwest vietnam. Int. J. Remote Sens. 40, 5544–5562. https://doi.org/10.1080/01431161.2019.1580789.
    10. Vancutsem, C., Ceccato, P., Dinku, T., and Connor, S.J., (2010). Evaluation of modis land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ. 114, 449–465. https://doi.org/10.1016/j.rse.2009.10.002.
    11. Wang, J.L., Pan, Z.H., Han, G.L., Cheng, L., Dong, Z.Q., Ting, Zhang Jing, Pan, Y.Y., Huang, L., Zhao, H., Fan, D.L., and Wu, D., (2016). Variation in ground temperature at a depth of 0 cm and the relationship with air temperature in china from 1961 to 2010. Re-sour. Sci. 38, 1733–1741. https://doi.org/10.18402/resci.2016.09.11.
    12. Wei, B.C., Xie, Y.W., Jia, X., Wang, X.Y., He, H.J., and Xue, X.Y., (2018). Land use/land cover change and it’s impacts on diurnal temperature range over the agricultural pastoral ecotone of northern china. Land Degrad. Dev. 29, 3009–3020 https://doi.org/10.1002/ldr.3052.
    13. Wilson, J.S., Clay, M., Martin, E., Stuckey, D., and Vedder-Risch, K., (2003). Evaluating environmental influences of zoning in urban ecosystems with remote sensing. Remote Sens. Environ. 86, 303–321. https://doi.org/10.1016/S0034-4257(03)00084-1.
    14. Xu, L., Zheng, C., and Ma, Y., (2020). Variations in precipitation extremes in the arid and semiarid regions of china. Int. J. Climatol. 1–13.
    15. Xu, Y.M., and Shen, Y., (2013). Reconstruction of the land surface temperature time series using harmonic analysis. Comput. Geosci. 61, 126–132. https://doi.org/10.1016/j.cageo.2013.08.009.
    16. Yang, G., Sun, W., Shen, H., Meng, X., and Li, J., (2019). An integrated method for reconstructing daily MODIS land surface tempera-ture data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 1026–1040. https://doi.org/10.1109/JSTARS.2019.2896455.

  • Downloads

  • How to Cite

    M. Menegbo, E. (2024). Mapping land surface temperature in Nigeria using Modis data. International Journal of Advanced Geosciences, 12(2), 70-74. https://doi.org/10.14419/rftz0816