气候变化领域集成服务门户(CCPortal): Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China

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DOI	10.1016/j.rse.2016.06.005
	Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China
	Che, Tao; Dai, Liyun; Zheng, Xingming; Li, Xiaofeng; Zhao, Kai
发表日期	2016
ISSN	0034-4257
EISSN	1879-0704
起始页码	334
结束页码	349
卷号	183
英文摘要	Snow depth is an important factor in water resources management in Northeast China. Forest covers 40% of Northeast China, and the presence of forests influences the accuracy of snow depth retrievals from passive microwave remote sensing data. An optimal iteration method was used to retrieve the forest transmissivities at 18 and 36 GHz based on the snow and forest microwave radiative transfer models and the snow properties measured in field experiments. The transmissivities at 18 and 36 GHz are 0.895 and 0.656 in the horizontal polarization, and 0.821 and 0.615 in the vertical polarization, respectively. Furthermore, the forest transmissivity and snow properties were input into the Microwave Emission Model of Layered Snowpacks (MEMLS) to establish a dynamic look-up table (LUT). Snow depths were retrieved from satellite passive microwave remote sensing data based on the LUT method, and these retrievals were verified by snow depth observations at 103 meteorological stations. The results showed that the bias between the retrieved and measured snow depths is very small, with root mean square errors (RMSEs) of approximately 6 cm in forest regions and 4 cm in non-forest regions. When compared with the existing snow products, the snow depth retrieved in this work presented the highest level of accuracy. The regional snow depth product in China is superior to the GlobSnow and NASA AMSR-E standard SWE products in non-forest regions, whereas the GlobSnow estimate is superior to the regional snow depth product in China and NASA AMSR-E standard SWE product estimates in forest regions. Therefore, we conclude that 1) the influence of forest on snow depth retrieval is important, and the appropriate forest parameters should be considered in the estimation of snow depth from passive microwave brightness temperature data; and 2) the snow depth retrieval algorithm based on the dynamic LUT method proved to be efficient in Northeast China. (C) 2016 Elsevier Inc. All rights reserved.
英文关键词	Snow cover; Snow depth; Forest; Remote sensing; Passive microwave; Brightness temperature
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS关键词	WATER EQUIVALENT ; EMISSION MODEL ; RADIOMETER DATA ; BOREAL ; MASS ; TRANSMISSIVITY ; INFORMATION ; RETRIEVALS ; PARAMETERS ; ALGORITHM
WOS记录号	WOS:000382345400027
来源期刊	REMOTE SENSING OF ENVIRONMENT
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/241023
作者单位	[Che, Tao; Dai, Liyun] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Zheng, Xingming; Li, Xiaofeng; Zhao, Kai] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China; [Che, Tao] Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China; [Dai, Liyun] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Che, Tao,Dai, Liyun,Zheng, Xingming,et al. Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China[J]. 中国科学院西北生态环境资源研究院,2016,183.
APA	Che, Tao,Dai, Liyun,Zheng, Xingming,Li, Xiaofeng,&Zhao, Kai.(2016).Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China.REMOTE SENSING OF ENVIRONMENT,183.
MLA	Che, Tao,et al."Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China".REMOTE SENSING OF ENVIRONMENT 183(2016).