Land surface temperature estimation for Buriram town municipality, Thailand
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Abstract
Land Surface Temperature (LST) has long been monitored and studied; however, the most reliable method of estimating the LST has yet to be examined regarding the mixed land-use types over a small city. This research explores an optimum method for Land Surface Temperature (LST) estimation in a city using the data from LANDSAT-8. Four favored LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were used to estimate the LST over Buriram Town Municipality, Thailand. The calculated LST from these four methods was compared with ground-based temperature data of 100 measured sites over the study area on the same date and time as the employed Landsat-8 data. The lowest Normalized Root Means Square Error (NRMSE) was considered to identify the optimum method of the LST estimation. The SW algorithm provides the lowest NRMSE value (0.114), followed by the RTE (0.171), the IMW algorithm (0.181), and the GSC (0.219). As a result, the SW algorithm is the optimum method in LST estimation for Buriram Town Municipality. The SW algorithm mainly eliminates atmospheric effects based on differential absorption in two thermal bands, which have shown the smallest error in the retrieval of LST. The explored optimal method will benefit GIS specialists working for Buriram local government to conduct the best practice to monitor the LST over the city. The other local governments could consider the SW algorithm to monitor the LST over their small cities with similar contents.
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References
Avdan, U., and Jovanovska, G. 2016. Algorithm for automated mapping of the land surface temperature using Landsat-8 satellite data. Journal of Sensors. 2016: 1-8.
Bendib, A., Dridi, H., and Kalla, M. I. 2016. Contribution of Landsat-8 data for the estimation of land surface temperature in Batna City, Eastern Algeria. Geocarto International. https://doi.org/10.1080/10106049.2016.1156167
Buriram Statistic Office. 2018. Monthly temperature and atmospheric pressure data 2013-2018. Retrieved from http://buriram.old.nso.go.th/nso/project/search_option/search_result.jsp
Buriram World. 2016. Retrieved from https://www.buriramworld.com/
Caselles, V., Rubio, E., Coll, C., and Valor, E. 1998. Thermal band selection for the PRISM instrument 3: Optimal band configurations. Journal of Geophysical Research. 103:17057-17067.
Chen, F., Zhao, X., Ye, H., and Hu, H. 2011. Retrieving land surface temperature from Landsat TM using different atmospheric products as ancillary data. Paper presented at the Spatial Data Mining and Geographic Knowledge Services (ICSDM), Fuzhou, China.
Coll, C., Galve, J. M., Sanchez, J. M., and Caselles, V. 2010. Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing. 48(1): 547-555.
Cristobal, J., Jimenez-Munoz, J. C., Sobrino, J. A., Ninyerola, M., and Pons, X. 2009. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. Journal of Geophysical Research. 114. https://doi.org/10.1029/2008JD010616
Du, C., Ren, H., Qin, Q., Meng, J., and Zhao, S. 2015. A practical split-window algorithm for estimating land surface temperature from Landsat-8 data. Remote Sensing. 7: 647-665.
Feng, X., Foody, G., Aplin, P., and Gosling, S. N. 2015. Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas. Sustainable Cities and Society. 19: 341-348.
Holmes, T. R. H., Crow, W. T., Yilmaz, M. T., Jackson, T. J., and Basara, J. B. 2013. Enhancing model-based land surface temperature estimates using multiplatform microwave observations. Journal of Geophysical Research: Atmospheres, 118: 577-591.
Jimenez-Munoz, J. C., Cristobal, J., Sobrino, J. A., Soria, G., Ninyerola, M., and Pons, X. 2009. Revision of the singlechannel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing. 47(1): 339-349.
Jimenez-Munoz, J. C., and Sobrino, J. A. 2003. A Generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research. 108: 1-9.
Jimenez-Munoz, J. C., and Sobrino, J. A. 2008. Split-window coefficients for land surface temperature retrieval from lowresolution thermal infrared sensors. IEEE Geoscience and Remote Sensing Letters. 5(4): 806-809.
Jimenez-Munoz, J. C., Sobrino, J. A., Skokovic, D., Matter, C., and Cristobal, J. 2014. Land surface temperatures retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters. 11(10): 1840-1843.
Jin, M., Li, J., Wang, C., and Shang, R. 2015. A practical splitwindow algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote Sensing. 7: 4371-4390.
Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., and Sobrino, J. A. 2013. Satellite-derived land surface temperature: Current status and perspective. Remote Sensing of Environment. 131: 14-37.
Liu, L., and Zhang, Y. 2011. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing. 3: 1535-1552.
Liu, Y., Hiyama, T., and Yamaguchi, Y. 2006. Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous Terrain Area. Remote Sensing of Environment. 105: 115-128.
Mechri, R., Ottle, C., Pannekoucke, O., and Kallel, A. 2014. Genetic particle filter application to land surface temperature downscaling. Journal of Geophysical Research: Atmospheres. 119: 2131-2146.
Orhan, O., Ekercin, S., and Dadaser-Celik, F. 2014. Use of Landstat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin Area, Turkey. The Scientific World Journal. 2014: 1-11.
Qin, Z., Karnieli, A., and 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.
Rozenstein, O., Qin, Z., Derimian, Y., and Karnieli, A. 2014. Derivation of land surface temperature for Landsat-8 TIRS using a split-window algorithm. Sensors. 14: 5768-5780.
Salakkham, E., and Piyatadsananon, P. 2020. The optimum method for urban land surface temperature estimation. preprints 2020. Seminar Report. Suranaree University of Technology.
Skokovic, D., Sobrino, J. A., Jimenez-Munoz, J. C., Soria, G., Julien, Y., Matter, C., and Cristobal, J. 2014. Calibration and validation of land surface temperature for Landsat-8 TIRS sensor. Retrieved from https://earth.esa.int/documents/700255/2126408/ESA_Lpve_Sobrino_2014a.pdf
Sobrino, J. A., Jimenez-Munoz, J. C., and Paolini, L. 2004. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment. 90: 434-440.
Tourism Authority of Thailand. 2017. Buriram. Retrieved from https://www.tourismthailand.org/About-Thailand/Destination/Buri-Ram
Vazquez, D. P., Reyes, F. J. O., and Arboledas, L. A. 1997. A comparative study of algorithms for estimating land surface temperature from AVHRR data. Remote Sensing of Environment. 62: 215-222.
Vlassova, L., Perez-Cabello, F., Nieto, H., Martin, P., Riano, D., and Riva, J. d. l. 2014. Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing. 6: 4345-4368.
Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., and Zhao, S. 2015. An improved mono-window algorithm for land surface temperature retrieval from Landsat-8 thermal infrared sensor data. Remote Sensing. 7: 4268-4289.
Wenbin, L., Yonghua, S., Dan, M., and Xiaojuan, L. 2013. Analysis of Beijing urban heat island under the influence of extreme heat based on HJ-1B data. Paper presented at the 2013 IEEE International Geoscience and Remote Sensing Symposium.
Weng, Q., and Fu, P. 2014. Modeling annual parameters of cleatsky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR Data. Remote Sensing of Environment. 140: 267-278.
Weng, Q., Fu, P., and Gao, F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and Modis data. Remote Sensing of Environment. 145: 55-67.
Weng, Q., Lu, D., and Schubring, J. 2004. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. 89: 467-483.
Wu, P., Shen, H., Ai, T., and Liu, Y. 2013. Land surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion. International Journal of Digital Earth. 6(1), 113-133.
Wu, P., Shen, H., Zhang, L., and Gottsche, F.M. 2015. Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment. 156, 169-181.
Zhou, J., Dai, F., Zhang, X., Zhao, S., and Li, M. 2015. Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSE-E data over the Chinese landmss. International Journal of Applied Earth Observation and Geoinformation. 34: 35-50.