Analisis Land Surface Emissivity menggunakan Data NDVI Landsat 8 dan Pengaruhnya terhadap Formasi Land Surface Temperature di Wilayah Kota Kendari

  • Nurgiantoro Nurgiantoro
  • Armayanti Aris
Keywords: Land surface emissivity; LST; NDVI; Landsat 8


Land surface emissivity (LSE) is an important part of urban environment studies, because these parameters are closely related to the material composition of an urban area, and also main role in analysis of land surface temperature (LST). The remote sensing approach using sensor OLI and TIRS onboard Landsat 8 will make it easier to find out the results. The goal of this study is to analyze the variation of LSE and its effect on LST formation in Kendari region within the last 5 years from 2014 and 2019 based on NDVI thresholds method (NDVITHM). The results show LSE values in the study area an average of ε > 0.96 in the emissivity unit. Whereas the NDVI has an average of 0.64 and 0.74 for 2014 and 2019, respectively. Formation of LST an average of 31.74 °C in 2014, and 23.47 °C in 2019 or approximately 7 °C difference from five years ago.


Z. L. Li et al., “Land surface emissivity retrieval from satellite data,” Int. J. Remote Sens., vol. 34, no. 9–10, pp. 3084–3127, 2013.

J. A. Sobrino et al., “Land surface emissivity retrieval from different VNIR and TIR sensors,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 2, pp. 316–327, 2008.

J. Mallick, C. K. Singh, S. Shashtri, A. Rahman, and S. Mukherjee, “Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city,” Int. J. Appl. Earth Obs. Geoinf., vol. 19, no. 1, pp. 348–358, 2012.

Heshun Wang, Qing Xiao, Hua Li, Yongming Du, and Qinhuo Liu, “Investigating the Impact of Soil Moisture on Thermal Infrared Emissivity Using ASTER Data,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 294–298, 2014.

M. Jin and S. Liang, “An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations,” J. Clim., vol. 19, no. 12, pp. 2867–2881, 2006.

T. Hu et al., “Directional variation in surface emissivity inferred from the MYD21 product and its influence on estimated surface upwelling longwave radiation,” Remote Sens. Environ., vol. 228, no. April, pp. 45–60, 2019.

R. G. Vaughan, W. M. Calvin, and J. V. Taranik, “SEBASS hyperspectral thermal infrared data: Surface emissivity measurement and mineral mapping,” Remote Sens. Environ., vol. 85, no. 1, pp. 48–63, 2003.

W. C. Snyder, Z. Wan, Y. Zhang, and Y. Z. Feng, “Classification-based emissivity for land surface temperature measurement from space,” Int. J. Remote Sens., vol. 19, no. 14, pp. 2753–2774, 1998.

A. A. Van de Griend and M. Owe, “On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces,” vol. 14, no. 16, pp. 1119–1131, 1993.

E. Valor and V. Caselles, “Mapping Land Surface Emissivity from NDVI: Application to European, African, and South American Areas,” Environ. Earth Sci., vol. 57, pp. 167–184, 1996.

J. A. Sobrino and N. Raissouni, “Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco,” Int. J. Remote Sens., vol. 21, no. 2, pp. 353–366, 2000.

J. A. Sobrino, J. C. Jiménez-Muñoz, and L. Paolini, “Land surface temperature retrieval from LANDSAT TM 5,” Remote Sens. Environ., vol. 90, no. 4, pp. 434–440, 2004.

D. Dissanayake, T. Morimoto, Y. Murayama, M. Ranagalage, and H. H. Handayani, “Impact of urban surface characteristics and socio-economic variables on the spatial variation of land surface temperature in Lagos City, Nigeria,” Sustain., vol. 11, no. 1, pp. 1–23, 2018.

B. L. Markham and J. L. Barker, “Spectral characterization of the LANDSAT Thematic Mapper sensors,” Int. J. Remote Sens., vol. 6, pp. 697–716, 1985.

Q. Weng, D. Lu, and J. Schubring, “Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies,” Remote Sens. Environ., vol. 89, no. 4, pp. 467–483, 2004.

Q. Weng and D. Lu, “A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States,” Int. J. Appl. Earth Obs. Geoinf., vol. 10, no. 1, pp. 68–83, 2008.

R. C. Estoque, Y. Murayama, and S. W. Myint, “Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia,” Sci. Total Environ., vol. 577, pp. 349–359, 2017.

F. Petitcolin, F. Nerry, and M. P. Stoll, “Mapping temperature independent spectral indice of emissivity and directional emissivity in AVHRR channels 4 and 5,” Int. J. Remote Sens., vol. 23, no. 17, pp. 3473–3491, 2002.

P. Dash, F. M. Göttsche, F. S. Olesen, and H. Fischer, “Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends,” Int. J. Remote Sens., vol. 23, no. 13, pp. 2563–2594, 2002.

V. Caselles, C. Coll, E. Valor, and E. Rubio, “Mapping land surface emissivity using AVHRR data application to La Mancha, Spain,” Remote Sens. Rev., vol. 12, no. 3–4, pp. 311–333, 1995.

A. J. Prata, “Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer: 1. Theory,” J. Geophys. Res., vol. 98, no. D9, p. 16689, 1993.

J. A. Voogt and T. R. Oke, “Thermal remote sensing of urban climates,” Remote Sens. Environ., vol. 86, no. 3, pp. 370–384, 2003.

J. A. Voogt and T. R. Oke, “Effects of urban surface geometry on remotely-sensed surface temperature,” Int. J. Remote Sens., vol. 19, no. 5, pp. 895–920, 1998.

BPS Kota Kendari, “Statistik Daerah Kota Kendari 2018,” Badan Pusat Statistik Kota Kendari, Kendari, 2018.

How to Cite
Nurgiantoro, N., & Aris, A. (2019). Analisis Land Surface Emissivity menggunakan Data NDVI Landsat 8 dan Pengaruhnya terhadap Formasi Land Surface Temperature di Wilayah Kota Kendari. Jurnal Penginderaan Jauh Indonesia, 1(2), 39-44. Retrieved from