Combined Use of Landsat 8 and Sentinel 2A Images for Enhanced Spatial Resolution of Land Surface Temperature

Underlying surface temperature is an important parameter of underlying surface thermal radiation and can be used in monitoring forest fires, coal fires, urban thermal radiation and developing climate models. Ground-based observations provide temperature information for small areas around weather stations and in fact cannot provide a high density of surface temperature data. Remote sensing technologies are promising in this respect. However, due to the low spatial resolution in the infrared channel, the surface temperature calculated from Landsat and Aster images does not always have the required detail needed when studying small areas. The results of images from Sentinel 2A and Landsat 8 satellites combination (joint digital processing) made in order to increase spatial resolution of underlying surface temperature are presented. Comparison of surface temperature extreme values shows that in spite of small difference in extreme values of temperature, the spatial field of temperature in case of combined images was more detailed and variable. This is evidenced by a significant increase in the variability of the temperature standard deviation. Direct visual observations of image fragments also confirm that combining Sentinel 2A and Landsat 8 images increases the spatial resolution of the surface temperature when compared to the Landsat 8 image

References

[1] Bondur V.G., Tsidilina M.N., Kladov V.L., et al. Irregular variability of spatiotemporal distributions of wildfires and emissions of harmful trace gases in Europe based on satellite monitoring data. Dokl. Earth Sc., 2019, vol. 485, no. 2, pp. 461--464.DOI: https://doi.org/10.1134/S1028334X19040202

[2] Bondur V.G., Ginzburg A.S. Emission of carbon-bearing gases and aerosols from natural fires on the territory of Russia based on space monitoring. Dokl. Earth Sc., 2016, vol. 466, no. 2, pp. 148--152. DOI: https://doi.org/10.1134/S1028334X16020045

[3] Bondur V.G. Satellite monitoring of small gaseous component and aerosol emissionat wildfires in Russia. Issledovaniya Zemli iz kosmosa, 2015, no. 6, pp. 21--35 (in Russ.). DOI: https://doi.org/10.7868/S0205961415060032

[4] Wan Z., Li Z.-L. MODIS land surface temperature and emissivity. In: Ramachandran B., Justice C., Abrams M. (eds). Land Remote Sensing and Global Environmental Change. Remote Sensing and Digital Image Processing, 2010, vol. 11, New York, NY, Springer. DOI: https://doi.org/10.1007/978-1-4419-6749-7_25

[5] Chaka D.S., Oda T.K. Understanding land surface temperature on rift areas to examine the spatial variation of urban heat island: the case of Hawassa, southern Ethiopia. GeoJournal, 2021, vol. 86, no. 2, pp. 993--1014. DOI: https://doi.org/10.1007/s10708-019-10110-5

[6] Hereher M.E. Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman. Environ. Monit. Assess., 2019, vol. 191, no. 9, art. 592. DOI: https://doi.org/10.1007/s10661-019-7771-y

[7] Huang F., Ma W., Wang B., et al. Air temperature estimation with MODIS data over the Northern Tibetan Plateau. Adv. Atmos. Sci., 2017, vol. 34, no. 5, pp. 650--662. DOI: https://doi.org/10.1007/s00376-016-6152-5

[8] Balling R.C., Brazel S.W. High resolution surface temperature patterns in a complex urban Terrain. Photogramm. Eng. Remote Sens., 1988, vol. 54, no. 9, pp. 1289--1293.

[9] Kumar S., Bhaskar P., Padmakumari K. Estimation of land surface temperature to study urban heat island effect using Landsat ETM + image. IJEST, 2012, vol. 4, no. 2, pp. 771--778.

[10] Maltick J., Kant Y., Bharath D. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 2008, vol. 12, no. 3, pp. 131--140.

[11] Trinh L.H. Studies of land surface temperature using multispectral image Landsat. Vietnam J. Earth Sci., 2014, vol. 36, no. 1, pp. 82--89.

[12] Yuan F., Bauer M. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Rem. Sens. Env., 2007, vol. 106, iss. 3, pp. 375--386. DOI: https://doi.org/10.1016/j.rse.2006.09.003

[13] Anandababu D., Purushothaman B.M., Suresh B.S. Estimation of land surface temperature using Landsat 8 data. IJARIIT, 2018, vol. 4, no. 2, pp. 177--186.

[14] Boori M.S., Vozenilek V., Balzter H., et al. Land surface temperature with land cover classes in ASTER and Landsat data. J. Geophys. Remote Sensing, 2015, vol. 4, no. 1, art. 1000138. DOI: https://doi.org/10.4172/2169-0049.1000138

[15] Guha S., Govil H., Dey A., et al. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur. J. Remote. Sens., 2018, vol. 51, iss. 1, pp. 667--678. DOI: https://doi.org/10.1080/22797254.2018.1474494

[16] Bui Q.T. Urban heat island analysis in Ha Noi: examining the relationship between land surface temperature and impervious surface. Hoi thao ung dung GIS toan quốc, 2015, pp. 674--677.

[17] Nguyen N.D., Pham V.V. Application of remote sensing and GIS to study surface temperature changes of the urban districts in Hanoi during 2005--2015 periods. VJAS, 2016, vol. 14, no. 8, pp. 1219--1230 (in Vietnamese).

[18] Van T.T., Lan H.T., Trung L.V. Thermal remote sensing method in study on urban surface temperature distribution. Vietnam J. Earth Sci., 2009, vol. 31, no. 2, pp. 168--177 (in Vietnamese).

[19] Prakash A., Gupta R.P. Surface fires in Jharia coalfield, India --- their distribution and estimation of area and temperature from TM data. Int. J. Rem. Sens., 1999, vol. 20, iss. 10, pp. 1935--1946. DOI: https://doi.org/10.1080/014311699212281

[20] Mishra R., Roy P., Pandey J., et al. Study of coal fire dynamics of Jharia coalfield using satellite data. IJGAGS, 2014, vol. 4, no. 3, pp. 477--484.

[21] Trinh L.H., Zablotskii V.R. The application of Landsat multi-temporal thermal infrared data to identify coal fire in the Khanh Hoa coal mine, Thai Nguyen province, Vietnam. Izv. Atmos. Ocean. Phys., 2017, vol. 53, no. 9, pp. 1081--1087. DOI: https://doi.org/10.1134/S0001433817090183

[22] Sandholt I., Rasmussen K., Anderson J. A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status. Rem. Sens. Env., 2002, vol. 79, iss. 2-3, pp. 213--224. DOI: https://doi.org/10.1016/S0034-4257(01)00274-7

[23] Bao Y., Gama G., Gang B., et al. Monitoring of drought disaster in Xilin Guole grassland using TVDI model. CRC Press, 2013.

[24] Leblon B. Monitoring forest fire danger with remote sensing. Nat. Hazards, 2005, vol. 35, no. 3, pp. 343--359. DOI: https://doi.org/10.1007/s11069-004-1796-3

[25] Xiao-rui T., Mcrae D.J., Li-fu S., et al. Satellite remote sensing technologies used in forest fire management. J. For. Res., 2005, vol. 16, no. 1, pp. 73--78. DOI: https://doi.org/10.1007/BF02856861

[26] Landsat 8 data users handbook. Available at: https://www.usgs.gov/core-science-systems/nli/landsat/landsat-8-data-users-handbook (accessed: 16.09.2021).

[27] Valor E., Caselles V. Mapping land surface emissivity from NDVI. Application to European African and South American areas. Rem. Sens. Env., 1996, vol. 57, iss. 3, pp. 167--184. DOI: https://doi.org/10.1016/0034-4257(96)00039-9

[28] Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Rem. Sens. Env., 1997, vol. 62, iss. 3, pp. 241--252. DOI: https://doi.org/10.1016/S0034-4257(97)00104-1

[29] Dang N.D., Dao N.L., Trinh L.H. Study on the change of land surface temperature in Thanh Hoa city in the period of 2000--2017 using Landsat thermal infrared data. J. Geodesy and Cartography, 2017, vol. 6, pp. 26--32 (in Vietnamese).

[30] Van de Griend A.A., Owen M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surface. Int. J. Rem. Sens., 1993, vol. 14, iss. 6, pp. 1119--1131. DOI: https://doi.org/10.1080/01431169308904400