Correction to: Land surface characterization using BeiDou signal-to-noise ratio observations

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dc.contributor.authorYang, Ting
dc.contributor.authorWan, Wei
dc.contributor.authorChen, Xiuwan
dc.contributor.authorChu, Tianxing
dc.contributor.authorQiao, Zhen
dc.contributor.authorLiang, Hong
dc.contributor.authorWei, Jiahua
dc.contributor.authorWang, Guangqian
dc.contributor.authorHong, Yang
dc.creator.orcidhttp://orcid.org/0000-0001-7467-5658en_US
dc.creator.orcidhttp://orcid.org/0000-0001-9478-7323en_US
dc.creator.orcidhttp://orcid.org/0000-0003-0047-732Xen_US
dc.creator.orcidhttps://orcid.org/0000-0003-1387-6442en_US
dc.creator.orcidhttp://orcid.org/0000-0001-7467-5658
dc.creator.orcidhttp://orcid.org/0000-0001-9478-7323
dc.creator.orcidhttp://orcid.org/0000-0003-0047-732X
dc.creator.orcidhttps://orcid.org/0000-0003-1387-6442
dc.date.accessioned2022-05-11T14:02:06Z
dc.date.available2022-05-11T14:02:06Z
dc.date.issued2019-01-24
dc.description.abstractChina’s BeiDou Navigation Satellite System (BDS) is providing new opportunities for GNSS reflectometry-related applications. We give the first and comprehensive description of the feasibility and potential of using BDS signal-to-noise ratio (SNR) data to characterize land surface in terms of the volumetric soil moisture (VSM), vegetation water content (VWC) and snow depth. BDS SNR-derived interferogram metrics (phase φ, amplitude A, and effective reflector height h) are investigated, and their correlations to the corresponding land surface parameters are established. Data collected from a geodetic-quality BDS/GPS compatible receiver for approximately 300-day period were used to validate the VSM retrieval. Results show that both BDS B1 and B2 frequencies can perform well to reflect the fluctuations of the VSM. Specifically, the B2-derived phase φ exhibits a slightly higher correlation with in situ VSM than that of B1 (R = 0.83 vs. R = 0.80), and the B2-derived amplitude A also exhibits a higher correlation with MODIS NDVI than that of B1 (R = 0.49 vs. R = 0.53); whilst for snow, the B1 and B2 results indicate qualitative agreement with concurrent in situ snow depth measurements. Furthermore, similar estimation performance can be obtained by comparing the results of BDS B1 and B2 against GPS L2C and L5. Therefore, BDS could be a new and powerful data source with comparable potential as GPS for effectively characterizing high-temporal resolution land surface.en_US
dc.description.abstractChina’s BeiDou Navigation Satellite System (BDS) is providing new opportunities for GNSS reflectometry-related applications. We give the first and comprehensive description of the feasibility and potential of using BDS signal-to-noise ratio (SNR) data to characterize land surface in terms of the volumetric soil moisture (VSM), vegetation water content (VWC) and snow depth. BDS SNR-derived interferogram metrics (phase φ, amplitude A, and effective reflector height h) are investigated, and their correlations to the corresponding land surface parameters are established. Data collected from a geodetic-quality BDS/GPS compatible receiver for approximately 300-day period were used to validate the VSM retrieval. Results show that both BDS B1 and B2 frequencies can perform well to reflect the fluctuations of the VSM. Specifically, the B2-derived phase φ exhibits a slightly higher correlation with in situ VSM than that of B1 (R = 0.83 vs. R = 0.80), and the B2-derived amplitude A also exhibits a higher correlation with MODIS NDVI than that of B1 (R = 0.49 vs. R = 0.53); whilst for snow, the B1 and B2 results indicate qualitative agreement with concurrent in situ snow depth measurements. Furthermore, similar estimation performance can be obtained by comparing the results of BDS B1 and B2 against GPS L2C and L5. Therefore, BDS could be a new and powerful data source with comparable potential as GPS for effectively characterizing high-temporal resolution land surface.
dc.description.sponsorshipThis study was jointly supported by the National Natural Science Foundation of China (NSFC) projects (Grant Nos. 41501360, 91437214, and 41401377), the Open Research Fund of Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Chinese Academy of Sciences (Grant No. TEL201503), and the Open Research Fund of State Key Laboratory of Hydroscience and Engineering, Tsinghua University (sklhse-2017-A-02).en_US
dc.description.sponsorshipThis study was jointly supported by the National Natural Science Foundation of China (NSFC) projects (Grant Nos. 41501360, 91437214, and 41401377), the Open Research Fund of Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Chinese Academy of Sciences (Grant No. TEL201503), and the Open Research Fund of State Key Laboratory of Hydroscience and Engineering, Tsinghua University (sklhse-2017-A-02).
dc.identifier.citationYang, T., Wan, W., Chen, X. et al. Correction to: Land surface characterization using BeiDou signal-to-noise ratio observations. GPS Solut 24, 1 (2020). https://doi.org/10.1007/s10291-019-0913-4en_US
dc.identifier.citationYang, T., Wan, W., Chen, X. et al. Correction to: Land surface characterization using BeiDou signal-to-noise ratio observations. GPS Solut 24, 1 (2020). https://doi.org/10.1007/s10291-019-0913-4
dc.identifier.doihttps://doi.org/10.1007/s10291-019-0913-4
dc.identifier.urihttps://hdl.handle.net/1969.6/90578
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherSpringeren_US
dc.publisherSpringer
dc.subjectbeidou navigation satellite systemen_US
dc.subjectbdsen_US
dc.subjectsignal-to-noise ratioen_US
dc.subjectsnren_US
dc.subjectvolumetric soil moistureen_US
dc.subjectvsmen_US
dc.subjectvegetation water contenten_US
dc.subjectsnow depthen_US
dc.subjectbeidou navigation satellite system
dc.subjectbds
dc.subjectsignal-to-noise ratio
dc.subjectsnr
dc.subjectvolumetric soil moisture
dc.subjectvsm
dc.subjectvegetation water content
dc.subjectsnow depth
dc.titleCorrection to: Land surface characterization using BeiDou signal-to-noise ratio observationsen_US
dc.titleCorrection to: Land surface characterization using BeiDou signal-to-noise ratio observations
dc.typeArticleen_US
dc.typeArticle

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