Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature

doi:10.1109/JSTARS.2019.2896923

	Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature
	Li, Wan 1,2; Ni, Li 3; Li, Zhao-Liang 4,5; Duan, Si-Bo 4; Wu, Hua 1,2,6
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2019-07-01
卷号	12 期号:7 页码:2299-2307
关键词	Comparison downscaling land surface temperature (LST) machine learning
ISSN号	1939-1404
DOI	10.1109/JSTARS.2019.2896923
通讯作者	Ni, Li(nili@radi.ac.cn) ; Wu, Hua(wuhua@igsnrr.ac.cn)
英文摘要	Land 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.
资助项目	National 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]
WOS关键词	DISAGGREGATION ; RESOLUTION ; ENERGY
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000480354800029
资助机构	National Key R&D Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/68657]
专题	中国科学院地理科学与资源研究所
通讯作者	Ni, Li; Wu, Hua
作者单位	1.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
推荐引用方式 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.