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Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature
Li, Wan1,2; Ni, Li3; Li, Zhao-Liang4,5; Duan, Si-Bo4; Wu, Hua1,2,6
2019-07-01
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
Volume12Issue:7Pages:2299-2307
Corresponding AuthorNi, Li(nili@radi.ac.cn) ; Wu, Hua(wuhua@igsnrr.ac.cn)
AbstractLand surface temperature (LST) is described as one of the most important environmental parameters of the land surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m, and downscaling results would be validated by the resampled LST product of the advanced spaceborne thermal emission and reflection radiometer. The results are further compared with the classical algorithm-thermal sharpening algorithm (TsHARP), using images derived from two representatives kind of areas of Beijing city. The result shows that: 1) all machine learning algorithms produce higher accuracy than TsHARP; 2) the performance of TsHARP on urban area is unsatisfactory than rural because of the weak indication of impervious surface by normalized difference vegetation index, however, machine learning algorithms get the desired results on both two areas, in which ANN and RF models perform well with high accuracy and fast arithmetic, SVM also gets a good result but there is a smoothing effect on land surface; and 3) additionally, machine learning algorithms are promising to achieve a universal framework which can downscale LST for any area within the training data from long spatiotemporal sequences.
KeywordComparison downscaling land surface temperature (LST) machine learning
DOI10.1109/JSTARS.2019.2896923
WOS KeywordDISAGGREGATION ; RESOLUTION ; ENERGY
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018YFB0504800] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; National Natural Science Foundation of China[41771398] ; National Natural Science Foundation of China[41871267]
Funding OrganizationNational Key R&D Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000480354800029
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68658
Collection中国科学院地理科学与资源研究所
Corresponding AuthorNi, Li; Wu, Hua
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agriinformat, Beijing 100081, Peoples R China
5.CNRS, UdS, ICube, F-67412 Illkirch Graffenstaden, France
6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Li, Wan,Ni, Li,Li, Zhao-Liang,et al. Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2019,12(7):2299-2307.
APA Li, Wan,Ni, Li,Li, Zhao-Liang,Duan, Si-Bo,&Wu, Hua.(2019).Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,12(7),2299-2307.
MLA Li, Wan,et al."Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 12.7(2019):2299-2307.
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