NAOC Open IR
Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes
Yan, Qingyun1; Hu, Ting1; Jin, Shuanggen1,2; Huang, Weimin3; Jia, Yan4; Chen, Tiexi5; Wang, Jian6
2021-07-01
Source PublicationREMOTE SENSING
Volume13Issue:14Pages:15
AbstractCyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (G), to facilitate a wide variety of land remote sensing applications. The accuracy of G relies on precise knowledge of the effective instantaneous radiative power (EIRP) of the transmitted GNSS signals in the direction of the specular reflection point, the precise knowledge of zenith antenna patterns which in turn affects estimates of EIRP, the good knowledge of receive antenna patterns etc. However, obtaining accurate estimates on these parameters completely is still a challenge. To solve this problem, in this paper, an effective method is proposed for calibrating the CyGNSS G product in a track-wise manner. Here, two different criteria for selecting data to calibrate and three reference options as targets of the calibrating data are examined. Accordingly, six calibration schemes corresponding to six different combinations are implemented and the resulting G products are assessed by (1) visual inspection and (2) evaluation of their associated soil moisture retrieval results. Both visual inspection and retrieval validation demonstrate the effectiveness of the proposed schemes, which are respectively demonstrated by the immediate removal/fix of track-wisely noisy data and obvious enhancement of retrieval accuracy with the calibrated G. Moreover, the schemes are tested using all the available CyGNSS level 1 version 3.0 data and the good results obtained from such a large volume of data further illustrate their robustness. This work provides an effective and robust way to calibrate the CyGNSS G result, which will further improve relevant remote sensing applications in the future.
KeywordCyGNSS GNSS-R data calibration soil moisture
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST
DOI10.3390/rs13142844
WOS KeywordSOIL-MOISTURE
Language英语
Funding ProjectNational Natural Science Foundation of China[42001375] ; Research Start-up Fund of NUIST[2020R078]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Start-up Fund of NUIST ; Research Start-up Fund of NUIST
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000677065500001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/76237
Collection中国科学院国家天文台
Corresponding AuthorHu, Ting
Affiliation1.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
2.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
3.Mem Univ, Fac Engn & Appl Sci, St John, NF A1B 3X5, Canada
4.Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Peoples R China
5.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China
6.Jiangsu Rongzer Informat Technol Corp, Nanjing 211800, Peoples R China
Recommended Citation
GB/T 7714
Yan, Qingyun,Hu, Ting,Jin, Shuanggen,et al. Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes[J]. REMOTE SENSING,2021,13(14):15.
APA Yan, Qingyun.,Hu, Ting.,Jin, Shuanggen.,Huang, Weimin.,Jia, Yan.,...&Wang, Jian.(2021).Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes.REMOTE SENSING,13(14),15.
MLA Yan, Qingyun,et al."Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes".REMOTE SENSING 13.14(2021):15.
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