气候变化领域集成服务门户(CCPortal): Bias correction of satellite soil moisture through data assimilation

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DOI	10.1016/j.jhydrol.2022.127947
	Bias correction of satellite soil moisture through data assimilation
	Qin, Jun; Tian, Jiaxin; Yang, Kun; Lu, Hui; Li, Xin; Yao, Ling; Shi, Jiancheng
通讯作者	Yao, L (通讯作者)
发表日期	2022
ISSN	0022-1694
EISSN	1879-2707
卷号	610
英文摘要	Soil moisture exhibits great spatio-temporal heterogeneity and plays a critical part in land surface energy and water cycles, being identified as a terrestrial essential climate variable. Thus, it is urgently needed in a wide variety of environmental processes such as hydrology, meteorology, agriculture, and ecology. Microwave remote sensing has the potential to provide near real-time soil moisture estimates on large spatial scales according to the distinctive contrast between dielectric properties of water and dry soils. Thus, many space-borne microwave sensors have been launched for retrieving soil moisture. Especially, SMOS and SMAP at L-band frequency (1.4 GHz) supply an unprecedented opportunity for retrieving surface soil moisture due to their deeper penetration than instruments at other bands. However, these satellite soil moisture products need bias correction before application such as data assimilation. Two common correction methods require reliable land surface soil moisture simulations. However, the quality of these simulations relies heavily on model parameters, such as soil porosity and texture, which are almost unavailable in remote regions such as the Tibetan Plateau. In this study, a dual-cycle assimilation algorithm is taken to make on-line bias correction when assimilating SMAP soil moisture products. During the assimilation, a linear bias correction scheme is regarded as the observation operator to link the simulated soil moisture values and the satellite retrievals. In the inner cycle, a sequentially based assimilation algorithm is run with both model parameters and bias correction coefficients, which are provided by the outer cycle. At the same time, both the analyzed soil moisture 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 both model parameters and correction coefficients through an optimization algorithm. A series of numerical experiments are designed and conducted, indicating that the soil moisture estimates by the presented algorithm are superior to those with the existing bias correction schemes.
关键词	LAND DATA ASSIMILATION PARAMETER-ESTIMATION SURFACE MODEL SMOS SMAP RETRIEVAL PROFILE STATE
英文关键词	Soil moisture; Bias correction; Soil parameters; Land data assimilation; Land surface model
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:000807306600004
来源期刊	JOURNAL OF HYDROLOGY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260711
推荐引用方式 GB/T 7714	Qin, Jun,Tian, Jiaxin,Yang, Kun,et al. Bias correction of satellite soil moisture through data assimilation[J]. 中国科学院青藏高原研究所,2022,610.
APA	Qin, Jun.,Tian, Jiaxin.,Yang, Kun.,Lu, Hui.,Li, Xin.,...&Shi, Jiancheng.(2022).Bias correction of satellite soil moisture through data assimilation.JOURNAL OF HYDROLOGY,610.
MLA	Qin, Jun,et al."Bias correction of satellite soil moisture through data assimilation".JOURNAL OF HYDROLOGY 610(2022).