- Volume 36 Issue 3
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Retrieval of Land Surface Temperature Using Landsat 8 Images with Deep Neural Networks
Landsat 8 영상을 이용한 심층신경망 기반의 지표면온도 산출
- Kim, Seoyeon (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ;
- Lee, Soo-Jin (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ;
- Lee, Yang-Won (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
- 김서연 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
- 이수진 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
- 이양원 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공)
- Received : 2020.06.17
- Accepted : 2020.06.22
- Published : 2020.06.30
As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T0 calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.
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