NAOC Open IR
Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
Jia, Yan1; Jin, Shuanggen2,3; Chen, Haolin1; Yan, Qingyun2,3; Savi, Patrizia4; Jin, Yan1; Yuan, Yuan1
2021
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
Volume14Pages:4879-4893
AbstractGlobal navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm(3)/cm(3) is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm(3)/cm(3) are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against in situ SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the in situ networks is 0.049 cm(3)/cm(3). The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data.
KeywordSpatial resolution Soil moisture Maximum likelihood estimation Spaceborne radar Reflectivity Moisture Vegetation mapping CYGNSS GNSS-Reflectometry preclassifica tion SMAP soil moisture XGBoost
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences
DOI10.1109/JSTARS.2021.3076470
WOS KeywordRETRIEVAL ; GPS ; REFLECTOMETRY ; REFLECTIVITY ; SENSITIVITY ; LAND
Language英语
Funding ProjectNational Natural Science Foundation of China[42001375] ; National Natural Science Foundation of China[42001362] ; National Natural Science Foundation of China[41901356] ; National Natural Science Foundation of China[42001332] ; Natural Science Foundation of Jiangsu Province[BK20180765] ; Nanjing Technology Innovation Foundation[RK032YZZ18003] ; NUPTSF[219066] ; Shanghai Leading Talent Project[E056061] ; Strategic Priority Research Program Project of theChinese Academy of Sciences[XDA23040100]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Natural Science Foundation of Jiangsu Province ; Nanjing Technology Innovation Foundation ; Nanjing Technology Innovation Foundation ; NUPTSF ; NUPTSF ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of theChinese Academy of Sciences ; Strategic Priority Research Program Project of theChinese Academy of Sciences
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000655843100010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/77338
Collection中国科学院国家天文台
Corresponding AuthorJin, Shuanggen
Affiliation1.Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210023, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
3.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
4.Politecn Torino, Dept Elect & Telecommun, I-10129 Turin, Italy
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GB/T 7714
Jia, Yan,Jin, Shuanggen,Chen, Haolin,et al. Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:4879-4893.
APA Jia, Yan.,Jin, Shuanggen.,Chen, Haolin.,Yan, Qingyun.,Savi, Patrizia.,...&Yuan, Yuan.(2021).Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,4879-4893.
MLA Jia, Yan,et al."Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):4879-4893.
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