Determination of land surface temperature using Landsat 8 images: Comparative study of algorithms on the city of Granada

David Hidalgo-García

Abstract

The use of satellite images has become, in recent decades, one of the most common ways to determine the Land Surface Temperature (LST). One of them is through the use of Landsat 8 images that requires the use of single-channel (MC) and two-channel (BC) algorithms. In this study, the LST of a medium-sized city, Granada (Spain) has been determined over a year by using five Landsat 8 algorithms that are subsequently compared with ambient temperatures. Few studies compare the data source with the seasonal variations of the same metropolis, which together with its geographical location, high pollution and the significant thermal variations it experiences make it a suitable place for the development of this research. As a result of the statistical analysis process, the regression coefficients R2, mean square error (RMSE), mean error bias (MBE) and standard deviation (SD) were obtained. The average results obtained reveal that the LST derived from the BC algorithms (1.0 °C) are the closest to the ambient temperatures in contrast to the MC (-5.6 °C), although important variations have been verified between the different zones of the city according to its coverage and seasonal periods. Therefore, it is concluded that the BC algorithms are the most suitable for recovering the LST of the city under study.


Keywords

Landsat 8; land surface temperature; thermal infrared data; remote sensing; algorithms

Full Text:

PDF

References

Avdan, U., Jovanovska, G. 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, 2016, 1480307. https://doi.org/10.1155/2016/1480307

Barbieri, T., Despini, F., Teggi, S. 2018. A multi-temporal analyses of Land Surface Temperature using Landsat-8 data and open source software: The case study of Modena, Italy. Sustainability (Switzerland), 10(5), 1678. https://doi.org/10.3390/ su10051678

Becker, F., Li, Z. 1995. Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote sensing reviews, 12(3-4), 225-253. https://doi.org/10.1080/02757259509532286

Carlson, T.N., Ripley, D.A. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1

Chavez, P.S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3), 459-479. https://doi.org/10.1016/0034-4257(88)90019-3

Coll, C., Caselles, V., Galve, J.M., Valor, E., Niclòs, R., Sánchez, J.M., Rivas, R. 2005. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sensing of Environment, 97(3), 288-300. https://doi.org/10.1016/j.rse.2005.05.007

Coll, C., Valor, E., Galve, J.M., Mira, M., Bisquert, M., García-Santos, V., Caselles, E., Caselles, V. 2012. Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer. Remote Sensing of Environment, 116, 211-225. https://doi.org/10.1016/j.rse.2010.01.027

Congedo, L. 2016. Semi-Automatic Classification Plugin Documentation Release 4.8.0.1. Release, 4(0.1), 29. https://doi.org/10.13140/RG.2.2.29474.02242/1

De Castro, M., Gallardo, C., Jylha, K., Tuomenvirta, H. 2007. The use of a climate-type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models. Climatic Change, 81, 329-341. https://doi.org/10.1007/s10584-006-9224-1

Du, C., Ren, H., Qin, Q., Meng, J., Zhao, S. 2015. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sensing, 7(1), 647-665. https://doi.org/10.3390/rs70100647

Du, J., Xiang, X., Zhao, B., y Zhou, H. 2020. Impact of urban expansion on land surface temperature in Fuzhou, China using Landsat imagery. Sustainable Cities and Society, 61(June), 102346. https://doi.org/10.1016/j.scs.2020.102346

Gallo, K., Hale, R., Tarpley, D., Yu, Y. 2011. Evaluation of the relationship between air and land surface temperature under clear- and cloudy-sky conditions. Journal of Applied Meteorology and Climatology, 50(3), 767-775. https://doi.org/10.1175/2010JAMC2460.1

García-Santos, V., Cuxart, J., Martínez-Villagrasa, D., Jiménez, M.A., Simó, G. 2018. Comparison of three methods for estimating land surface temperature from Landsat 8-TIRS Sensor data. Remote Sensing, 10(9), 1-13. https://doi.org/10.3390/rs10091450

Gerace, A., Montanaro, M. 2017. Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8. Remote Sensing of Environment, 191, 246-257. https://doi.org/10.1016/j.rse.2017.01.029

Jiménez-Muñoz, J.C., Sobrino, J.A., Skoković, D., Mattar, C., Cristóbal, J. 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840-1843. https://doi.org/10.1109/LGRS.2014.2312032

Jin, M., Li, J., Wang, C., Shang, R. 2015. A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote Sensing, 7(4), 4371-4390. https://doi.org/10.3390/rs70404371

Kafer, P.S., Rolim, S.B.A., Iglesias, M.L., Da Rocha, N.S., Diaz, L.R. 2019. Land surface temperature retrieval by Landsat 8 thermal band: Applications of laboratory and field measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2332-2341. https://doi.org/10.1109/JSTARS.2019.2913822

Keeratikasikorn, C., Bonafoni, S. 2018. Urban heat island analysis over the land use zoning plan of Bangkok by means of Landsat 8 imagery. Remote Sensing, 10(3), 440. https://doi.org/10.3390/ rs10030440

Keramitsoglou, I., Kiranoudis, C.T., Ceriola, G., Weng, Q., Rajasekar, U. 2011. Identification and analysis of urban surface temperature patterns in Greater Athens, Greece, using MODIS imagery. Remote Sensing of Environment, 115(12), 3080-3090. https://doi.org/10.1016/j.rse.2011.06.014

Khalaf, A. 2018. Utilization of thermal bands of Landsat 8 data and geographic information system for analysis of urban heat island in Baghdad governorate 2016. MATEC Web of Conferences, 162, 1-5. https://doi.org/10.1051/matecconf/201816203026

Lemus-Canovas, M., Martin-Vide, J., Moreno-Garcia, M.C., Lopez-Bustins, J.A. 2020. Estimating Barcelona’s metropolitan daytime hot and cold poles using Landsat-8 Land Surface Temperature. Science of the Total Environment, 699, 134307. https://doi.org/10.1016/j.scitotenv.2019.134307

Li, T., Meng, Q. 2018. A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data. Landscape and Urban Planning, 179(July), 63-71. https://doi.org/10.1016/j.landurbplan.2018.07.010

Li, Z.L., Tang, B.H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I.F., Sobrino, J.A. 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14-37. https://doi.org/10.1016/j.rse.2012.12.008

Lin, W., Yu, T., Chang, X., Wu, W., Zhang, Y. 2015. Calculating cooling extents of green parks using remote sensing: Method and test. Landscape and Urban Planning, 134, 66-75. https://doi. org/10.1016/j.landurbplan.2014.10.012

Liu, L., Zhang, Y. 2011. Urban heat island analysis using the landsat TM data and ASTER Data: A case study in Hong Kong. Remote Sensing, 3(7), 1535-1552. https://doi.org/10.3390/rs3071535

Macarof, P., Statescu, F. 2017. Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. Present Environment and Sustainable Development, 11(2), 141-150. https://doi.org/10.1515/pesd-2017-0032

Mao, K., Qin, Z., Shi, J., Gong, P. 2005. A practical split-window algorithm for retrieving land-surface temperature from MODIS data. International Journal of Remote Sensing, 26(15), 3181-3204. https://doi.org/10.1080/01431160500044713

Meng, X., Cheng, J., Zhao, S., Liu, S., y Yao, Y. 2019. Estimating land surface temperature from Landsat-8 data using the NOAA JPSS enterprise algorithm. Remote Sensing, 11(2), 155. https://doi.org/10.3390/rs11020155

Mukherjee, F., Singh, D. 2020. Assessing Land Use–Land Cover Change and Its Impact on Land Surface Temperature Using LANDSAT Data: A Comparison of Two Urban Areas in India. Earth Systems and Environment, 4(2), 385-407. https://doi.org/10.1007/s41748-020-00155-9

Prata, A., Caselles, V., Coll, C., Sobrino, J.A., Ottlé, C. 1995. Thermal remote sensing of land surface temperature from satellites: current status and future prospects. Remote sensing reviews, 12(3-4), 175-224. https://doi.org/10.1080/02757259509532285

Peres, L.F., Sobrino, J.A., Libonati, R., Jiménez Muñoz, J.C., Dacamara, C.C., Romaguera, M. 2008. Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX field campaign. International Journal of Remote Sensing, 29(24), 7251-7268. https://doi.org/10.1080/01431160802036532

Qin, Z., Karnieli, A., Berliner, P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719-3746. https://doi.org/10.1080/01431160010006971

Reuter, D.C., Richardson, C.M., Pellerano, F.A., Irons, J.R., Allen, R.G., Anderson, M., Jhabvala, M.D., Lunsford, A.W., Montanaro, M., Smith, R.L., Tesfaye, Z., Thome, K.J. 2015. The thermal infrared sensor (tirs) on Landsat 8: Design overview and pre-launch characterization. Remote Sensing, 7(1), 1135-1153. https://doi.org/10.3390/rs70101135

Rongali, G., Keshari, A.K., Gosain, A.K., Khosa, R. 2018. A mono-window algorithm for land surface temperature estimation from Landsat 8 thermal infrared sensor data: A case study of the beas river basin, India. Pertanika Journal of Science and Technology, 26(2), 829-840. https://doi.org/10.1007/s41651-018-0021-y

Rozenstein, O., Qin, Z., Derimian, Y., Karnieli, A. 2014. Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm. Sensors (Switzerland), 14(4), 5768-5780. https://doi.org/10.3390/s140405768

Saaroni, H., Amorim, J.H., Hiemstra, J.A., Pearlmutter, D. 2018. Urban Green Infrastructure as a tool for urban heat mitigation: Survey of research methodologies and findings across different climatic regions. Urban Climate, 24(October 2017), 94-110. https://doi.org/10.1016/j.uclim.2018.02.001

Sabol, D.E., Gillespie, A.R., Abbott, E., Yamada, G. 2009. Field validation of the ASTER Temperature Emissivity Separation algorithm. Remote Sensing of Environment, 113(11), 2328-2344. https://doi. org/10.1016/j.rse.2009.06.008

Sekertekin, A. 2019. Validation of Physical Radiative Transfer Equation-Based Land Surface Temperature Using Landsat 8 Satellite Imagery and SURFRAD in-situ Measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 196(July), 105161. https://doi.org/10.1016/j.jastp.2019.105161

Sekertekin, A., Bonafoni, S. 2020. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2), 294. https://doi.org/10.3390/rs12020294

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/10.1080/014311600210876

Sobrino, J.A., Jiménez-Muñoz, J.C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., Plaza, A., Martínez, P. 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316-327. https://doi.org/10.1109/TGRS.2007.904834

Srivanit, M., Hokao, K., Phonekeo, V. 2012. Assessing the Impact of Urbanization on Urban Thermal Environment: A Case Study of Bangkok Metropolitan. International Journal of Applied Science and Technology, 2(7), 243-256. Recuperado de http://www.ijastnet.com/journals/Vol_2_No_7_ August_2012/26.pdf (Último acceso octubre 2020).

Srivastava, P.K., Majumdar, T.J., Bhattacharya, A.K. 2009. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research, 43(10), 1563-1574. https://doi.org/10.1016/j.asr.2009.01.023

Stisen, S., Sandholt, I., Nørgaard, A., Fensholt, R., Eklundh, L. 2007. Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sensing of Environment, 110(2), 262-274. https://doi.org/10.1016/j.rse.2007.02.025

Tan, K., Liao, Z., Du, P., Wu, L. 2017. Land surface temperature retrieval from Landsat 8 data and validation with geosensor network. Frontiers of Earth Science, 11(1), 20-34. https://doi.org/10.1007/s11707-016-0570-7

Trigo, I.F., Monteiro, I.T., Olesen, F., Kabsch, E. 2008. An assessment of remotely sensed land surface temperature. Journal of Geophysical Research Atmospheres, 113(17), 1-12. https://doi. org/10.1029/2008JD010035

USGS. 2017. Landsat 8 surface reflectance derived spectral indices. Versión 3.6. in: sioux falls, SD.

Wan, Z., Dozier. J. 1996. A generalized split window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 892-905. https://doi.org/10.1109/36.508406

Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., Zhao, S. 2015a. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing, 7(4), 4268-4289. https://doi.org/10.3390/rs70404268

Wang, L., Lu, Y., Yao, Y. 2019. Comparison of three algorithms for the retrieval of land surface temperature from landsat 8 images. Sensors (Switzerland), 19(22), 5049. https://doi.org/10.3390/s19225049

Wang, S., He, L., Hu, W. 2015b. A temperature and emissivity separation algorithm for landsat-8 thermal infrared sensor data. Remote Sensing, 7(8), 9904-9927. https://doi.org/10.3390/rs70809904

Wu, C., Li, J., Wang, C., Song, C., Chen, Y., Finka, M., La Rosa, D. 2019. Understanding the relationship between urban blue infrastructure and land surface temperature. Science of the Total Environment, 694, 133742. https://doi.org/10.1016/j.scitotenv.2019.133742

Yang, C., Yan, F., Zhang, S. 2020. Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city. Journal of Environmental Management, 265(March), 110563. https://doi.org/10.1016/j.jenvman.2020.110563

Yu, X., Guo, X., Wu, Z. 2014. 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(10), 9829-9852. https://doi.org/10.3390/rs6109829

Yu, Y., Liu, Y., Yu, P., Liu, Y., Yu, P. 2017. Land surface temperature product development for JPSS and GOES-R missions. Comprehensive Remote Sensing, 1-9, 284-303. https://doi.org/10.1016/B978-0-12- 409548-9.10522-6

Zhan, W., Chen, Y., Zhou, J., Wang, J., Liu, W., Voogt, J., Zhu, X., Quan, J., Li, J. 2013. Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats. Remote Sensing of Environment, 131(19), 119-139. https://doi.org/10.1016/j.rse.2012.12.014

Zhang, Y., Chen, L., Wang, Y., Chen, L., Yao, F., Wu, P., Wang, B., Li, Y., Zhou, T., Zhang, T. 2015. Research on the contribution of urban land surface moisture to the alleviation effect of urban land surface heat based on Landsat 8 data. Remote Sensing, 7(8), 10737-10762. https://doi.org/10.3390/rs70810737

Abstract Views

322
Metrics Loading ...

Metrics powered by PLOS ALM




 

This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

e-ISSN: 1988-8740    ISSN: 1133-0953           https://doi.org/10.4995/raet