NSSC OpenIR
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000-2019 Using an Ensemble-Based Deep Neural Network
Alternative TitleWOS:000569706800001;20203709178810
Ding, Yifan1,4,5; Cheng, Xiao1,2,4,5; Liu, Jiping3; Hui, Fengming1,2,4,5; Wang, Zhenzhan6; Chen, Shengzhe3
Source PublicationREMOTE SENSING
2020
Volume12Issue:17Pages:2746
DOI10.3390/rs12172746
Language英语
Keywordmelt pond fraction Arctic sea ice deep neural network C-BAND SAR IN-SITU SEASONAL EVOLUTION SURFACE-FEATURES ALBEDO CLASSIFICATION VARIABILITY AERIAL VALIDATION ALGORITHM
AbstractThe accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.
Indexed BySCI ; EI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7712
Collection中国科学院国家空间科学中心
Affiliation1.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
2.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
3.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519000, Peoples R China
4.SUNY Albany, Dept Atmospher & Environm Sci, Albany, NY 12222 USA
5.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, Peoples R China
6.Univ Corp Polar Res, Beijing 100875, Peoples R China
7.Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Microwave Remote Sensing, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Ding, Yifan,Cheng, Xiao,Liu, Jiping,et al. Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000-2019 Using an Ensemble-Based Deep Neural Network[J]. REMOTE SENSING,2020,12(17):2746.
APA Ding, Yifan,Cheng, Xiao,Liu, Jiping,Hui, Fengming,Wang, Zhenzhan,&Chen, Shengzhe.(2020).Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000-2019 Using an Ensemble-Based Deep Neural Network.REMOTE SENSING,12(17),2746.
MLA Ding, Yifan,et al."Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000-2019 Using an Ensemble-Based Deep Neural Network".REMOTE SENSING 12.17(2020):2746.
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