Bias correction of satellite soil moisture through data assimilation
文献类型：期刊论文
| 作者 | Qin, Jun3; Tian, Jiaxin2,5,6; Yang, Kun1; Lu, Hui; Li, Xin2,5,6; Yao, Ling3; Shi, Jiancheng4 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 610页码:13 |
| ISSN号 | 0022-1694 |
| 关键词 | Soil moisture Bias correction Soil parameters Land data assimilation Land surface model |
| DOI | 10.1016/j.jhydrol.2022.127947 |
| 通讯作者 | Yao, Ling(yaoling@lreis.ac.cn) |
| 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. |
| WOS关键词 | LAND DATA ASSIMILATION ; PARAMETER-ESTIMATION ; SURFACE MODEL ; SMOS ; SMAP ; RETRIEVAL ; PROFILE ; STATE |
| 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] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0301] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000807306600004 |
| 资助机构 | Frontier Science Project of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178947]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yao, Ling |
| 作者单位 | 1.Tsinghua Univ, Dept Earth Syst Sci, Key Lab Earth Syst Modeling, Minist Educ, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 4.Beijing Normal Univ, Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China 5.Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China 6.Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr, Key Lab Tibetan Environm Changes & Land Surfaces P, Beijing, Peoples R China |
| 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]. JOURNAL OF HYDROLOGY,2022,610:13. |
| 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,610,13. |
| MLA | Qin, Jun,et al."Bias correction of satellite soil moisture through data assimilation".JOURNAL OF HYDROLOGY 610(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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