气候变化领域集成服务门户(CCPortal): Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model

CCPortal

DOI	10.1029/2020WR029010
	Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model
	Hou, Jinliang; Huang, Chunlin; Chen, Weijing; Zhang, Ying
通讯作者	Huang, CL (通讯作者)，Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Northwest Inst Ecoenvironm & Resources, Lanzhou, Peoples R China.
发表日期	2021
ISSN	0043-1397
EISSN	1944-7973
卷号	57 期号:7
英文摘要	In this study, an innovative MODIS fractional snow cover (SCF) data assimilation (DA) prototype framework that invokes machine learning (ML) techniques and Common land model (CoLM) is proposed to improve the estimation of the snow depth (SD) and the SCF. To validate our new framework, we analyzed two snow seasons from 2013 to 2015 at 46 stations in Northern Xinjiang in China. We developed 12 SCF DA schemes that represent different DA methods (direct insertion (DI) and Ensemble Kalman Filter (EnKF)), observational data (original data and gap-filled MODIS SCF data), and observation operators (five new snow depletion curves (SDCs) defined using traditional multivariate nonlinear regression and four ML methods). While improving the frequency of the SCF observations in the DI-based DA scheme only resulted in a marginal improvement in the snow estimates, by adding new SDCs fitted by ML techniques (e.g., deep belief network), and the gap-filled MODIS SCF data to the EnKF-based DA scheme, we were able to reduce model structural uncertainties of CoLM and achieve marked improvement in the accuracy of the snow estimates (RMSE = 5.92 cm, mean bias error = -1.94 cm, and average degree of improvement = 32.18% for SD estimates and RMSE = 15. 79%, mean bias error = -1.21%, and average degree of improvement = 47.95% for SCF estimates). Our results demonstrate the feasibility of improving snow estimates by combining the ML techniques with physically based snowpack model in a SCF DA framework.
关键词	WATER EQUIVALENT SURFACE MODEL BRIGHTNESS TEMPERATURE NORTH-AMERICA AREA RETRIEVAL DEPTH SIMULATIONS VALIDATION SYSTEM
英文关键词	data assimilation; snow depth; fractional snow cover; machine learning; Common Land Model
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
WOS类目	Environmental Sciences ; Limnology ; Water Resources
WOS记录号	WOS:000680092200028
来源期刊	WATER RESOURCES RESEARCH
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/253959
作者单位	[Hou, Jinliang; Huang, Chunlin; Zhang, Ying] Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Northwest Inst Ecoenvironm & Resources, Lanzhou, Peoples R China; [Chen, Weijing] Univ Texas Austin, Dept Geol Sci, Jackson Sch Geosci, Austin, TX 78712 USA
推荐引用方式 GB/T 7714	Hou, Jinliang,Huang, Chunlin,Chen, Weijing,et al. Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model[J]. 中国科学院西北生态环境资源研究院,2021,57(7).
APA	Hou, Jinliang,Huang, Chunlin,Chen, Weijing,&Zhang, Ying.(2021).Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model.WATER RESOURCES RESEARCH,57(7).
MLA	Hou, Jinliang,et al."Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model".WATER RESOURCES RESEARCH 57.7(2021).