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	Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models
	Shao, Donghang1; Xu, Wenbo1; Li, Hongyi2,3; Wang, Jian4,5; Hao, Xiaohua4
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2018-12-01
卷号	56期号:12页码:6969-6985
关键词	Forward model long-time-series reconstruction retrieval model snow albedo
ISSN号	0196-2892
DOI	10.1109/TGRS.2018.2846681
通讯作者	Xu, Wenbo(xuwenbo@uestc.edu.cn) ; Li, Hongyi(lihongyi@lzb.ac.cn)
英文摘要	Snow albedo plays an important role in the global climate system. There are notable missing data and error uncertainties in the current remote sensing snow albedo products that are attributed to the limits of remote-sensing technology. Due to the uncertainties of meteorological factors and the differences in various forward model simulation methods, snow albedo forward simulations also have considerable uncertainties. This paper suggests a long-time-series reconstruction of snow albedo utilizing a forward radiation-transferring model and a remote-sensing retrieval model together with multisource remotely sensed data and meteorological data. The key to this paper is to estimate snow information for areas lacking data utilizing a forward model for snow albedo with clear physical mechanisms. The estimated snow information can be used as reliable data for snow albedo reconstructions. The results indicate that the long time series of snow albedo data obtained by coupling the snow albedo retrieval model and forward simulation model is highly accurate. The mean absolute error, root mean square error, Pearson's correlation coefficient (R), and Nash-Sutcliffe efficiency coefficient of the observed and reconstructed snow albedos are 0.11, 0.14, 0.79, and 0.69, respectively. The reconstructed snow albedo data are underestimated by only 11% relative to the in situ snow surface albedo measurements. In the alpine mountain regions, the proposed method has a simulation accuracy that is 6% greater than that of the MOD10A1 SAD. This paper provides an effective reconstruction solution that improves the accuracy of estimations of snow albedo and fills gaps in the data.
收录类别	SCI
WOS关键词	LAND-SURFACE ALBEDO ; BROAD-BAND ALBEDO ; GRAIN-SIZE ; SPECTRAL ALBEDO ; MODIS ; ALGORITHM ; VALIDATION ; GREENLAND ; REFLECTANCE ; PRODUCTS
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000451621000008
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2558246
专题	寒区旱区环境与工程研究所
通讯作者	Xu, Wenbo; Li, Hongyi
作者单位	1.Univ Elect Sci & Technol China, Sch Resources & Environm, Ctr Informat Geosci, Chengdu 611731, Sichuan, Peoples R China 2.Chinese Acad Sci, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China 3.Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China 4.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lab Remote Sensing & Geospatial Sci, Lanzhou 730000, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Shao, Donghang,Xu, Wenbo,Li, Hongyi,et al. Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(12):6969-6985.
APA	Shao, Donghang,Xu, Wenbo,Li, Hongyi,Wang, Jian,&Hao, Xiaohua.(2018).Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(12),6969-6985.
MLA	Shao, Donghang,et al."Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.12(2018):6969-6985.

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