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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (3) : 72-80     DOI: 10.6046/zrzyyg.2023091
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A method for reconstructing hourly 100-m-resolution all-weather land surface temperature
YAN Jianan1,2(), CHEN Hong3(), ZHANG Yuze4, WU Hua1,5
1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 101408, China
3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
4. National Engineering Research Center for Transportation Safety and Emergency Informatics, China Transport Telecommunications & Information Center, Beijing 100028, China
5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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Abstract  

Land surface temperature (LST) proves to be an important parameter in surface processes on regional and global scales, and its spatiotemporal information can be obtained through thermal infrared remote sensing. However, the constraints of thermal infrared sensors (TIRSs) themselves and the inability of thermal infrared electromagnetic waves to penetrate clouds render it impossible to obtain LST with a high spatiotemporal resolution currently. This study presents a method for reconstructing hourly LST at 100-m resolution in all weathers. This method consists of three main steps: ① cloudy LST at four moments is reconstructed using a moderate resolution imaging spectroradiometer (MODIS) based on the conventional annual temperature cycle (ATC) model; ② the daily variation curve of LST is estimated based on the daily trend in the skin temperature (SKT); ③ with spectral indices as regressors, spatial downscaling is conducted for the hourly LST using Extreme Gradient Boosting (XGBoost). The results show that the proposed reconstruction method can obtain spatiotemporally continuous LST products, improve the spatial resolution of LST, and provide more details. The validation of the hourly 100-m-resolution LST using data from the surface radiation budget network (SURFRAD) developed by the U.S. indicates that the reconstructed hourly LST exhibits roughly the same trend as the measured values of the SURFRAD. The method for reconstructing all-weather hourly LST boasts high accuracy, with R2 of 0.95, a root mean squared error (RMSE) of 3.75 K, and a bias of 0.75 K.
Keywords LST      MODIS      cloudy LST reconstruction      downscaling      XGBoost     
ZTFLH:  TP79  
  P407.6  
Issue Date: 03 September 2024
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Jianan YAN
Hong CHEN
Yuze ZHANG
Hua WU
Cite this article:   
Jianan YAN,Hong CHEN,Yuze ZHANG, et al. A method for reconstructing hourly 100-m-resolution all-weather land surface temperature[J]. Remote Sensing for Natural Resources, 2024, 36(3): 72-80.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023091     OR     https://www.gtzyyg.com/EN/Y2024/V36/I3/72
Fig.1  Landsat8 B5(R), B4(G), B3(B) false color composed images in the study areas
研究区
编号		 站点代号 站点名称 经度/(°) 纬度/(°) 地表
类型
A BND Bondville -88.37 40.05 草地
B DRA Desert Rock -116.02 36.62 灌木丛
C SXF Sioux Falls -96.62 43.73 耕地
D GCM Goodwin Creek -89.87 34.25 草地
E FPK Fort Peck -105.10 48.31 草地
F TBL Table Mountain -105.24 40.13 草地
Tab.1  Detailed information of six SURFRAD sites
研究区
编号		 站点
代号		 中心经
度/(°)		 中心纬
度/(°)		 获取日期 获取UTC
时间
A BND -88.17 39.87 2019-07-12 16: 29
B DRA -116.03 36.58 2019-06-24 18: 21
C SXF -96.43 43.96 2019-05-10 17: 10
D GCM -89.89 34.30 2019-07-03 16: 37
E FPK -105.09 48.30 2019-03-17 17: 46
F TBL -105.07 39.94 2019-09-27 17: 37
Tab.2  Spatiotemporal information of Landsat8 images in the study areas
Fig.2  Flowchart of the downscaling all-weather hourly LST
Fig.3-1  Spatial distribution of hourly LST in area A
Fig.3-2  Spatial distribution of hourly LST in area A
Fig.4  Hourly curves of estimated LSTs and actual LSTs at six stations
Fig.5  Hourly curves of estimated LSTs and actual LSTs at six stations
站点代号 bias/K RMSE/K R2
BND 0.46 1.58 0.93
DRA 3.48 5.21 0.88
SXF -0.86 2.85 0.90
GCM 1.77 2.91 0.34
FPK -1.24 1.71 0.86
TBL 0.92 5.91 0.19
Tab.3  Evaluation indicators of hourly LST at six sites
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