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Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors

文献类型：期刊论文

作者Tian, Jiaxin1,2; Qin, Jun3,4; Yang, Kun5; Zhao, Long6; Chen, Yingying1,2; Lu, Hui5; Li, Xin1,2; Shi, Jiancheng7
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2022-02-01
卷号269页码:13
关键词Land data assimilation Land surface model Soil moisture Model error Observation error Parameter optimization
ISSN号0034-4257
DOI10.1016/j.rse.2021.112802
通讯作者Qin, Jun(qinjun@igsnrr.ac.cn)
英文摘要Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dualcycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.
WOS关键词ENSEMBLE KALMAN FILTER ; SYSTEM ; COVARIANCES ; PARAMETERS ; SATELLITE ; PRODUCTS ; STATE ; SMOS ; SMAP
资助项目Frontier Science Project of the Chinese Academy of Sciences[QYZDY-SSW-DQC011-03] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20060604] ; National Natural Science Foundation of China[41805133] ; Key Special Project for Introduced Talents Team of Southern Marine 515 Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0301]
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000759691900003
出版者ELSEVIER SCIENCE INC
资助机构Frontier Science Project of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Key Special Project for Introduced Talents Team of Southern Marine 515 Science and Engineering Guangdong Laboratory (Guangzhou)
源URL[http://ir.igsnrr.ac.cn/handle/311030/172263]  
专题中国科学院地理科学与资源研究所
通讯作者Qin, Jun
作者单位1.Chinese Acad Sci, Natl Tibetan Plateau Data Ctr, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surfaces, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
5.Tsinghua Univ, Dept Earth Syst Sci, Minist Educ Key Lab Earth Syst Modeling, Beijing, Peoples R China
6.Southwest Univ, Chongqing, Peoples R China
7.Beijing Normal Univ, Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Jiaxin,Qin, Jun,Yang, Kun,et al. Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors[J]. REMOTE SENSING OF ENVIRONMENT,2022,269:13.
APA Tian, Jiaxin.,Qin, Jun.,Yang, Kun.,Zhao, Long.,Chen, Yingying.,...&Shi, Jiancheng.(2022).Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors.REMOTE SENSING OF ENVIRONMENT,269,13.
MLA Tian, Jiaxin,et al."Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors".REMOTE SENSING OF ENVIRONMENT 269(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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