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Gap-Filling of MODIS Fractional Snow Cover Products via Non-Local Spatio-Temporal Filtering Based on Machine Learning Techniques

文献类型：期刊论文

作者Hou, Jinliang1,2; Huang, Chunlin1,2; Zhang, Ying1,2; Guo, Jifu1,2; Gu, Juan3
刊名REMOTE SENSING
出版日期2019
卷号11期号:1页码:24
关键词MODIS SCF cloud cover gap-filling NSTF
ISSN号2072-4292
DOI10.3390/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
WOS记录号WOS:000457935600090
出版者MDPI
URI标识http://www.irgrid.ac.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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来源：寒区旱区环境与工程研究所

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