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Remote Sensing Technology and Application  2022, Vol. 37 Issue (2): 474-487    DOI: 10.11873/j.issn.1004-0323.2022.2.0474
    
Downscaling Land Surface Temperature through AMSR-2 Observations by Using Machine Learning Algorithms
Yongkang Li1,2(),Xinjun Wang1,2(),Yanfei Ma3,Bei Chen1,2,Linan Yan1,2,Guanhong Zhang1,2
1.College of Grassland and Environment Sciences,Xinjiang Agricultural University,Urumqi 830052,China
2.Xinjiang Key Laboratory of Soil and Plant Ecological Processes,Urumqi 830052,China
3.Department of Geography,Handan University,Handan 056005,China
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Abstract  

MODIS dailyLand Surface Temperature (LST) products are seriously contaminated by weather effects and the effective pixel information missed. It is sincerely important in areas where data is sparse. An approach to downscaling LSTs from AMSR-2 vertical polarizations multi-brightness temperature and vegetation index observations was preliminarily investigated in the Gurbantunggut Desert, and then the downscaled LSTs were used to fill the gaps due to clouds in the MODIS of 2018.(1) In this study, four machine learning methods(Cubist、DBN、SVM、RF), two training spatial resolution(5 km、10 km ), ten band combinations, were applied to train the model. The 10-fold cross-validation results show that the RF model and C09 band combination have the best simulation effect. (2) Two robust downscaling methods of land surface temperature using Random Forest algorithm (5 km|RF|09/10 km|RF|09) were developed to retrieve a 1km-resolution land surface temperature product from Advanced Microwave Scanning Radiometer 2 (AMSR2) data. The validation results with MODIS and station LSTs show that 5 km|RF|09 downscaled LSTs has a better performance than 10 km| RF|09. Comparisons of the retrieval results with MODIS LSTs and ground measurement data from Fukang stations yielded that R2 respectively is 0.971、0.761, RMSE is 3.380 K、7.614 K and MAE is 2.509 K、6.695 K, which indicated that the accuracy of the 5 km|RF|09 LST retrieval model was high. (3) The downscaling results fill the gaps due to clouds in the MODIS, which can be applied to long-term LST sequence analysis in Gurbantunggut Desert. The method of LSTs downscaled provided scientific reference for data acquisition in data sparse area.

Key words:  Land Surface Temperature      AMSR-2      Machine learning algorithm      Downscale      Gurbantunggut Desert     
Received:  31 August 2020      Published:  17 June 2022
ZTFLH:  P423  
Corresponding Authors:  Xinjun Wang     E-mail:  yongkang_xau@163.com;wxj8112@163.com
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Yongkang Li
Xinjun Wang
Yanfei Ma
Bei Chen
Linan Yan
Guanhong Zhang

Cite this article: 

Yongkang Li,Xinjun Wang,Yanfei Ma,Bei Chen,Linan Yan,Guanhong Zhang. Downscaling Land Surface Temperature through AMSR-2 Observations by Using Machine Learning Algorithms. Remote Sensing Technology and Application, 2022, 37(2): 474-487.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.2.0474     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I2/474

Fig.1  Overview of study area
AMSR-2被动微波亮温数据MODIS数据
中心频率/GHz极化方式空间分辨率/km数据类型空间分辨率/km数据集
23.8V10

MYD

11A1

1LST_Day_1km QC_Day
36.5V10

MYD

13A2

1

1 km_16_days_EVI

1 km_16_days_NDVI

89V5
Table 1  The information of AMSR-2 microwave brightness temperature and MODIS data
Fig.2  Flow chart of the method for AMSR-2 land surface temperature downscaling

组合

序号

AMSR-2 被动微波亮温MYD13A2MYD11A1
36.5 Pol.V23.8 Pol.V36.5 Pol.V~23.8 Pol.V89 Pol.VNDVIEVILST
C01----
C02----
C03---
C04---
C05---
C06---
C07---
C08--
C09--
C10--
Table 2  The details of feature vector
Fig.3  The 10-fold cross-validation result of 5 km scale with 10 band combinations
Fig.4  The 10-fold cross-validation result of 10 km scale with 10 band combinations
Fig.5  The spatial distribution of pearson correlation
Fig.6  The scatter plots for 1km LST derived from 5 km、10 km RF model with modis LST
Fig.7  Temporal variability of LST derived from 5 km’s and 10 km’s RF model,The scatter plots for LST derived from 5 km、10 km RF model with station LST
Fig.8  The a represents the day of 2018.08.18, the b represents the day of 2018.08.19
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