Arid
DOI10.1016/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
ISSN0034-4257
EISSN1879-0704
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, ...
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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).
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