Comparison of Single-channel and Split-window Methods for Estimating Land Surface Temperature from Landsat 8 Data

Abstract

Abstract: Landsat 8 is the eighth satellite in the Landsat program, which provides images at 11 spectral channels, including 2 thermal infrared bands at a spatial resolution of 100 m (band 10 (10,30÷11,30 µm) and band 11 (11,50÷12,50 µm)). Until now, most studies have used only band 10 of Landsat 8 image to calculate land surface temperature. In this paper, we compare the results of determining a land surface temperature from Landsat 8 thermal infrared data when using a single band (single-channel method) and using both thermal infrared bands (split-window method). 02 Landsat 8 scenes in the dry season 2015 - 2016 in Loc Ninh district (Binh Phuoc province) and Lam Ha district (Lam Dong province) were used to calculate the land surface temperature according to the SC and SW methods. The results obtained in both experiments showed that the land surface temperature, determined from band 10 of Landsat 8 images was significantly higher than using band 11. Meanwhile, the method using both thermal infrared bands of Landsat 8 data (SW method) to calculate land surface temperature has higher accuracy when compared with the method using band 10 or band 11 only (SC method).

Keywords: Landsat 8, thermal infrared, land surface temperature, split-window algorithm, single-channel algorithm.

References:

[1] T. Alipour, M.R. Sarajian, A. Esmaseily, Land surface temperature estimation from thermal band of LANDSAT sensor, case study: Alashtar city. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (4)/C7 (2004) 1-6.
[2] G. Cueto, J.E. Ostos, D. Toudert, T.A. Martinez, Detection of the urban heat island in Mexicali and its relationship with land use, Atmosfera 20 (2) (2007) 111-131.
[3] J. Mallick, Y. Kant, B.D. Bharath, Estimation of land surface temperature over Delhi using LANDSAT 7 ETM+, Geophysics Union 3 (2008) 131-140.
[4] M.Y. Grishchenko, ETM+ thermal infrared imagery application for Moscow urban heat island study, Current Problems in Remote Sensing of the Earth from Space 9 (4) (2012) 95-101.
[5] K.S. Kumar, P.U. Bhaskar, K. Padmakumari, Estimation of land surface temperature to study urban heat island effect using LANDSAT ETM+ image, International journal of Engineering Science and technology 4 (2) (2012) 771-778.
[6] Trần Thị Vân, Hoàng Thái Lan, Lê Văn Trung, Phương pháp viễn thám nhiệt trong nghiên cứu phân bố nhiệt độ bề mặt đô thị, Tạp chí Các khoa học về Trái đất 31 (2) (2009) 168-177.
[7] Trịnh Lê Hùng, Nghiên cứu sự phân bố nhiệt độ bề mặt bằng dữ liệu ảnh vệ tinh đa phổ LANDSAT, Tạp chí Các khoa học về Trái đất 36 (1) (2014) 82-89.
[8] Bùi Quang Thành, Urban heat island analysis in Ha Noi: examining the relatioship between land surface temperature and impervious surface, Hội thảo Ứng dụng GIS toàn quốc (2015) 674-677.
[9] Nguyễn Đức Thuận, Phạm Văn Vân, Ứng dụng công nghệ viễn thám và hệ thống thông tin địa lý nghiên cứu thay đổi nhiệt độ bề mặt 12 quận nội thành, thành phố Hà Nội giai đoạn 2005 – 2015, Tạp chí Khoa học Nông nghiệp Việt Nam 14 (8) (2016) 1219-1230.
[10] Trịnh Lê Hùng, Kết hợp ảnh vệ tinh Landsat 8 và Sentinel 2 trong nâng cao độ phân giải nhiệt độ bề mặt, Tạp chí Khoa học Đại học Quốc gia Hà Nội, chuyên san Các khoa học và Môi trường 34 (4) (2018) 54-63, https://doi.org/10.25073/2588-1094/ vnuees.4294.
[11] M.S. Boori, V. Vozenilek, H. Balter, K. Choudhary, Land surface temperature with land cover classes in Aster and Landsat data, Journal of Remote Sensing & GIS. 4 (138) (2015) 1-4. https:// doi.org/ 10.4172/2169-0049.1000138.
[12] S. Guha, H. Govil, A. Dey, N. Gill, Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy, European Journal of Remote Sensing 51 (1) (2018) 667-678. https://doi.org/10.1080/22797254.2018.1474494
[13] S. Pal, S. Ziaul, Detection of land use and land cover change and land surface temperature in English Bazar urban centre, The Egyptian Journal of Remote Sensing and Space Science 20 (1) (2017) 125-145.
[14] United States Geological Survey. http://glovis. usgs.gov, 2017 (accessed 20 October 2017).
[15] J.M. Galve, C. Coll, V. Caselles, E. Valor, M. Mira, Comparison of split-window and single-chanel methods for land surface temperature retrieval from MODIS and ASTER data, International Geoscience Remote Sensing Symposium 3 (2008) 294-297.https://doi.org/10.1109/IGARSS.2008. 47 79341.
[16] C. Du, H. Ren, Q. Qin, J. Meng, J. Li, Split-window algorithm for estimating land surface temperature from Landsat 8 TIRS data, International Geosciences Remote Sensing Symposium (2014) 3578–3581. https://doi.org/10.1109/IGARSS. 2014. 6947256.
[17] O. Rozenstein, Z. Qin, Y. Derimian, A. Karnieli, Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm, Sensors 14 (2014) 5768–5780. https://doi.org/10.3390/s1404 05768.
[18] S. Li, G. Jiang, Land surface temperature retrieval from Landsat-8 data with the ggeneralized split-window aalgorithm, IEEE Access 6 (2018) 18149-18162. https://doi.org/10.1109/ACCESS.2018.28 18741.
[19] G. Rongali, A.K.. Keshari, A.K. Gosain, R. Khosa, Split-window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data, Journal of Geovisualization and Spatial Analysis 2 (2) (2018) 1-19.
[20] https://landsat.usgs.gov/landsat-8-data-users-handbook, 2018 (accessed 07 Septamber 2018).
[21] J.W. Rouse, H.R. Haas, A.J. Schell, W.D. Deering, Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351. 1 (1974) 309-317.
[22] L. Vlassova, F. Perez-Cabello, H. Nieto, P. Martin, D. Riaflo, J. de la Riva, Assessment of methods for land surface temperature retrieval from Landsat 5 TM images applicable to multiscale tree-grass ecosystem modeling, Remote Sensing 6 (2014) 4345-4368. https://doi.org/10.3390/rs6054345.
[23] E. Valor, V. Caselles, Mapping land surface emissivity from NDVI. Application to European African and South American areas, Remote sensing of Environment 57 (1996) 167-184.
[24] A.A. Van de Griend, M. Owen, On the relationship between thermal emissivity and the normalized difference vegetation index for natural surface, International Journal of Remote Sensing 14 (1993) 1119-1131.
[25] R. Huazhong, C. Du, Q. Qin, R. Liu, Atmospheric water vapor retrieval from Landsat 8 and its validation, IEEE International Geoscience and Remote Sensing Symposium (2014) 3045-3048. https://doi.org/10.1109/IGARSS.2014.6947119.
[26] J.A. Sobrino, J.C. Jimenez-Munoz, P.J. Zarco-Tejada, G. Sepulcre-Canto, E. de Miguel, Land surface temperature derived from airborne hyperspectral scanner thermal infrared data, Remote Sensing of Environment 102 (2006) 99-115.
[27] D. Skokovic, J.A. Sobrino, J.C. Jiménez Muñoz, . Julien, C. Mattar, J. Cristóbal, Calibration and validation of land surface temperature for Landsat8-TIRS sensor TIRS Landsat-8 characteristics. http:// earth.esa.int/documents/700255/2126408/ESA__Lpve_Sobrino_2014a.pdf, 2018 (accessed 20 December 2018).
[28] X. Yu, X. Guo, X. Wu, Land surface temperature retrieval from Landsat 8 TIRS – Comparison between radiative transfer equation based method, split window algorithm and single channel method, Remote Sensing 6 (2014) 9829-9852. https:// doi.org/10.3390 /rs6109829.
[29] P.S. Chavez, Image-based atmospheric correction s–revisited and improved, Photogrammetric Engineering and Remote Sensing 62 (9) (1996) 1025-1036.