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	Gap-Filling of MODIS Fractional Snow Cover Products via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques
	Hou, Jinliang1,2; Huang, Chunlin1,2; Zhang, Ying1,2; Guo, Jifu1,2; Gu, Juan3
刊名	REMOTE SENSING
	2019
卷号	11期号:1页码:24
关键词	MODIS SCF cloud cover gap-filling NSTF
ISSN号	2072-4292
DOI	10.3390/rs11010090
通讯作者	Huang, Chunlin(huangcl@lzb.ac.cn)
英文摘要	Cloud obscuration leaves significant gaps in MODIS snow cover products. In this study, an innovative gap-filling method based on the concept of non-local spatio-temporal filtering (NSTF) is proposed to reconstruct the cloud gaps in MODIS fractional snow cover (SCF) products. The ground information of a gap pixel was estimated by using the appropriate similar pixels in the remaining known part of an image via an automatic machine learning technique. We take the MODIS SCF product cloud gap filling data from 2001 to 2016 in Northern Xinjiang, China as an example. The results demonstrate that the methodology can generate almost continuous spatio-temporal, daily MODIS SCF images, and it leaves only 0.52% of cloud gaps long-term, on average. The validation results based on cloud assumption exhibit high accuracy, with a higher R2 exceeding 0.8, a lower RMSE of 0.1, an overestimated error of 1.13%, an underestimated error of 1.4%, and a spatial efficiency (SPAEF) of 0.78. The validation based on 50 in situ snow depth observations demonstrates the superiority of the methodology in terms of accuracy and consistency. The overall accuracy is 93.72%. The average omission and commission error have increased approximately 1.16 and 0.53% compared with the original MODIS SCF products under a clear sky term.
收录类别	SCI
WOS关键词	CLOUD REMOVAL ; NORTHERN XINJIANG ; RIVER-BASIN ; AMSR-E ; VALIDATION ; IMAGES ; VARIABILITY ; LANDSAT ; MAPS ; AREA
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000457935600090
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2558171
专题	寒区旱区环境与工程研究所
通讯作者	Huang, Chunlin
作者单位	1.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Gansu, Peoples R China 2.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Gansu, Peoples R China 3.Lanzhou Univ, Key Lab Western Chinas Environm Syst, Minist Educ, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Hou, Jinliang,Huang, Chunlin,Zhang, Ying,et al. Gap-Filling of MODIS Fractional Snow Cover Products via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques[J]. REMOTE SENSING,2019,11(1):24.
APA	Hou, Jinliang,Huang, Chunlin,Zhang, Ying,Guo, Jifu,&Gu, Juan.(2019).Gap-Filling of MODIS Fractional Snow Cover Products via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques.REMOTE SENSING,11(1),24.
MLA	Hou, Jinliang,et al."Gap-Filling of MODIS Fractional Snow Cover Products via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques".REMOTE SENSING 11.1(2019):24.

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