干旱区生态环境知识资源中心(Arid): Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China

Arid

DOI	10.1016/j.rse.2022.112891
	Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China
	Zheng, Jingyao; Zhao, Tianjie; Lu, Haishen; Shi, Jiancheng; Cosh, Michael H.; Ji, Dabin; Jiang, Lingmei; Cui, Qian; Lu, Hui; Yang, Kun; Wigneron, Jean-Pierre; Li, Xiaojun; Zhu, Yonghua; Hu, Lu; Peng, Zhiqing; Zeng, Yelong; Wang, Xiaoyi; Kang, Chuen Siang
通讯作者	Zhao, TJ (corresponding author)，Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
来源期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
EISSN	1879-0704
出版年	2022
卷号	271
英文摘要	A new soil moisture and soil temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located in a semi-arid area of northern China. In this study, in situ measurements of the SMN-SDR were used to evaluate 24 different soil moisture datasets grouped according to three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged products, and (3) model-based products. Triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results. Impacts of different factors on the accuracy of soil moisture products were also investigated, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD). The results reveal that the latest Climate Change Initiative (CCI)-combined product (v06.1, merging extra low-frequency passive microwave data) had the best agreement with in situ measurements from the SMN-SDR, with the lowest ubRMSE ( 0.04 m(3)/m(3)) and highest R (> 0.6). Among all single-sensor retrieved soil moisture products, the Soil Moisture Active Passive (SMAP) products performed best in terms of R (> 0.6) and ubRMSE (close to 0.04 m(3)/m(3)), with the SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than the baseline SCA-V (Single Channel Algorithm-Vertical polarization). Importantly, the newly developed SMAP-IB product, which does not use auxiliary data, delivered the best bias statistics and higher VOD values compared with the drier SMAP retrievals, suggesting that the low VOD values (underestimated vegetation effects) may be the major factor causing the dry bias of SMAP products in this study area. It was also found that TCA may systematically overestimate the correlation and underestimate the ubRMSE of soil moisture products as compared with ground-based metrics. TCA-based metrics may vary considerably when using different triplets, due to the TCA assumptions being violated even with the most conservative triplets (in this case an active product, a passive product, and a model-based product). Redundant TCA-based metrics from multiple inde-pendent triplets could be averaged to increase the accuracy of final TCA estimates. This study is the first to use in situ measurements from the SMN-SDR to conduct a comprehensive evaluation of commonly used, multi-source soil moisture products. These results are expected to further promote the improvement of satellite-and model-based soil moisture products.
英文关键词	Validation In situ network Satellite-based soil moisture Model -based soil moisture Triple collocation Climate change initiative Copernicus climate change service
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000759733700001
WOS关键词	BRIGHTNESS TEMPERATURE ; AMSR-E ; VALIDATION ; PRODUCTS ; SMAP ; SMOS ; RETRIEVAL ; SATELLITE ; ASCAT ; ALGORITHM
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376215
作者单位	[Zheng, Jingyao; Lu, Haishen; Zhu, Yonghua; Wang, Xiaoyi] Hohai Univ, Coll Hydrol & Water Resources, Natl Cooperat Innovat Ctr Water Safety & Hydrosci, State Key Lab Hydrol Water Resources & Hydraul En, Hohai, Peoples R China; [Zhao, Tianjie; Ji, Dabin; Peng, Zhiqing; Zeng, Yelong] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Shi, Jiancheng] Chinese Acad Sci, Natl Space Sci Ctr, Beijing, Peoples R China; [Cosh, Michael H.] USDA ARS, Beltsville Agr Res Ctr, Hydrol & Remote Sensing Lab, 10300 Baltimore Ave, Beltsville, MD 20705 USA; [Jiang, Lingmei] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China; [Cui, Qian] Minist Water Resources China, Informat Ctr, Beijing, Peoples R China; [Lu, Hui; Yang, Kun] Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China; [Wigneron, Jean-Pierre; Li, Xiaojun] INRAE, UMR1391 ISPA, F-33140 Villenave Dornon, France; [Hu, Lu] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Peoples R China; [Kang, ...
推荐引用方式 GB/T 7714	Zheng, Jingyao,Zhao, Tianjie,Lu, Haishen,et al. Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China[J],2022,271.
APA	Zheng, Jingyao.,Zhao, Tianjie.,Lu, Haishen.,Shi, Jiancheng.,Cosh, Michael H..,...&Kang, Chuen Siang.(2022).Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China.REMOTE SENSING OF ENVIRONMENT,271.
MLA	Zheng, Jingyao,et al."Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China".REMOTE SENSING OF ENVIRONMENT 271(2022).