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

Choi, Sungwon; Lee, Kyeong-Sang; Seo, Minji; Seong, Noh-Hun; Jin, Donghyun; Jung, Daeseong; Sim, Suyoung; Jung, Im Gook; Han, Kyung-Soo

doi:10.7780/kjrs.2021.37.1.12

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

대한원격탐사학회지 = Korean journal of remote sensing, v.37 no.1, 2021년, pp.153 - 160

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)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (7)

표 Table 1. Characteristic of Landsat-8
그림 Fig. 1. Result of LST by split window method algorithms (a) Price (2017.06.23), (b) Becker et al (2017.06.23), (c) Prata (2017.06.23), (d) Coll et al. (2017.06.23), (e) Price et al. (2017.11.14), (f) Becker et al. (2017.11.14), (g) Prata (2017.11.14), (h) Coll et al. (2017.11.14).
그림 Fig. 2. Comparison of LSTs (a) Becker et al. vs. Price, (b) Becker et al. vs. Prata, (c) Becker et al. vs. Coll et al.
그림 Fig. 3. Timeseries of LST. left: Gangnam-gu, right: Bukhan mountain.
그림 Fig. 4. Window-size test of LSTs.
그림 Fig. 5. Validation in NDVI cases.
그림 Fig 6. Validation results of LST retrieval algorithms.

AI 본문요약
AI-Helper

대상 데이터

Study area in this study is Landsat-8 images, 36.42 ~ 38.51°N, 125.25~127.98°E, and it observes this area every 16days

이론/모형

In this study, we used 4 LST retrieval algorithms to make LST using Landsat-8 data. And these algorithms are based on split-window method. These formulae for calculating LST are as follows.
NDVI is calculated by normalizing the difference between the red band (band 3) reflectivity and the Near InfraRed (NIR, band 4) band reflectivity. In this study, NDVI is used to calculate emissivity. The formula for calculating NDVI is as follows.

성능/효과

With 30 m resolution, we can retrieve LST data which reflect various land types. Landsat-8 has 2 sensors, Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS). OLI has 9 bands from 0.
As a result, although there are very small difference, Coll et al.’s LST is the lowest accuracy in 4 LST retrieval algorithms.
6~8 K). As a result, we decided LST of Prata equation is the most suitable LST in this study.
The result shows there are few differences of RMSE and bias, and we can validate directly Landsat-8 LST and MOD11_L2. The result of validation shows LST of Prata equation has the lowest RMSE in whole scene (2.16 K) and each NDVI cases (1.6~8 K). As a result, we decided LST of Prata equation is the most suitable LST in this study.
Because spatial resolution of Landsat-8 and MODIS, we tested several window-size cases. The result shows there are few differences of RMSE and bias, and we can validate directly Landsat-8 LST and MOD11_L2. The result of validation shows LST of Prata equation has the lowest RMSE in whole scene (2.
Generally, because humidity in vegetation effect to temperature, if there are many vegetations, temperature change range is lower than the place with less vegetation. Therefore, there are some temperature difference but LST trend of each algorithms were shown very similar and they reflect change of season well.
which were developed by AVHRR data and is used with Landsat-8 data. We compared each algorithm to check they retrieve LST reasonable and they were showed over 0.98 of R values. We checked them by time series in 2 actual measure points, all LSTs showed they reflected change of seasons.
In this study, we tried to find the most suitable LST algorithm to South Korea. We used Landsat-8 data which has high spatial resolution because South Korea has very complex and has variety land type. We used 4 algorithms for high-resolution satellite data, Price, Becker et al.

참고문헌 (11)

Becker, F. and Z.L. Li, 1990. Towards a local split window method over land surfaces, Remote Sensing, 11(3): 369-393.

상세보기
Becker, F. and Z.L. Li, 1990.Temperature-independent spectral indices in thermal infrared bands, Remote Sensing of Environment, 32(1): 17-33.

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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.

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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.
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

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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.

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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.

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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
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.
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.

상세보기
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.

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