Browse > Article
http://dx.doi.org/10.7780/kjrs.2021.37.1.12

Comparison of Land Surface Temperature Algorithm Using Landsat-8 Data for South Korea  

Choi, Sungwon (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Lee, Kyeong-Sang (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Seo, Minji (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Seong, Noh-Hun (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Jin, Donghyun (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Jung, Daeseong (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Sim, Suyoung (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Jung, Im Gook (Climate Prediction Department, Climate Services and Research Division, APEC Climate Center)
Han, Kyung-Soo (Division of Earth Environmental System Science (Major of Spatial Information System Engineering), Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.1, 2021 , pp. 153-160 More about this Journal
Abstract
Land Surface Temperature (LST) is the radiological surface temperature which observed by satellite. It is very important factor to estimate condition of the Earth such as Global warming and Heat island. For these reasons, many countries operate their own satellite to observe the Earth condition. South Korea has many landcovers such as forest, crop land, urban. Therefore, if we want to retrieve accurate LST, we would use high-resolution satellite data. In this study, we made LSTs with 4 LST retrieval algorithms which are used widely with Landsat-8 data which has 30 m spatial resolution. We retrieved LST using equations of Price, Becker et al. Prata, Coll et al. and they showed very similar spatial distribution. We validated 4 LSTs with Moderate resolution Imaging Spectroradiometer (MODIS) LST data to find the most suitable algorithm. As a result, every LST shows 2.160 ~ 3.387 K of RMSE. And LST by Prata algorithm show the lowest RMSE than others. With this validation result, we choose LST by Prata algorithm as the most suitable LST to South Korea.
Keywords
Land surface temperature; LST; Landsat-8;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Benali, A., A.C. Carvalho, J.P. Nunes, N. Carvalhais, and A. Santos, 2012. Estimating air surface temperature in Portugal using MODIS LST data, Remote Sensing of Environment, 124: 108-121.   DOI
2 Coll C., V. Caselles, J.A. Sobrino, and E. Valor, 1994. On the atmospheric dependence of the split-window equation for land surface temperature, Remote Sensing, 15(1): 105-122.   DOI
3 Han, K.S., A.A. Viau, and F. Anctil, 2004.An analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest, International Journal of Remote Sensing, 25(21): 4761-4780   DOI
4 Kustas, W.P. and J.M. Norman, 1996. Use of remote sensing for evapotranspiration monitoring over land surfaces, Hydrological Sciences Journal, 41(4): 495-516.   DOI
5 Moran, M.S. and R.D. Jackson, 1991. Assessing the spatial distribution of evapotranspiration using remotely sensed inputs, Journal of Environmental Quality, 20(4): 725-737.   DOI
6 Prata, A.J., 1993. Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer: 1.Theory, Journal of Geophysical, 98(D9): 16689-16702   DOI
7 Price, J.C., 1984. Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer, Journal of Geophysical Research: Atmospheres, 89(D5): 7231-7237.   DOI
8 Becker, F. and Z.L. Li, 1990. Towards a local split window method over land surfaces, Remote Sensing, 11(3): 369-393.   DOI
9 Becker, F. and Z.L. Li, 1990.Temperature-independent spectral indices in thermal infrared bands, Remote Sensing of Environment, 32(1): 17-33.   DOI
10 Sobrino, J.A., J.C. Jimenez-Munoz, and W. Verhoef, 2005. Canopy directional emissivity: Comparison between models, Remote Sensing of Environment, 99(3): 304-314.   DOI
11 Sobrino, J.A., N. Raissouni, and Z.L. Li, 2001. A comparative study of land surface emissivity retrieval from NOAA data, Remote Sensing of Environment, 75(2): 256-266.   DOI