气候变化领域集成服务门户(CCPortal): Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

CCPortal

DOI	10.3390/rs13071250
	Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere
	Hu, Yanxing; Che, Tao; Dai, Liyun; Xiao, Lin
发表日期	2021
EISSN	2072-4292
卷号	13 期号:7
英文摘要	In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets-Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth-were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R-2 of Sodankyla, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.
英文关键词	snow depth datasets; data fusion; machine learning algorithms; the Northern Hemisphere
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000638801100001
来源期刊	REMOTE SENSING
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/241042
作者单位	[Hu, Yanxing; Che, Tao; Dai, Liyun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China; [Hu, Yanxing; Che, Tao] Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100049, Peoples R China; [Hu, Yanxing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Xiao, Lin] Sichuan Agr Univ, Key Lab Forest Resource Conservat & Ecol Safety U, Sichuan Prov Key Lab Ecol Forestry Engn Upper Rea, Natl Forestry & Grassland Adm,Coll Forestry, Chengdu 611130, Peoples R China
推荐引用方式 GB/T 7714	Hu, Yanxing,Che, Tao,Dai, Liyun,et al. Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere[J]. 中国科学院西北生态环境资源研究院,2021,13(7).
APA	Hu, Yanxing,Che, Tao,Dai, Liyun,&Xiao, Lin.(2021).Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere.REMOTE SENSING,13(7).
MLA	Hu, Yanxing,et al."Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere".REMOTE SENSING 13.7(2021).