NAOC Open IR
Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach
Jia, Yan1; Jin, Shuanggen2,3; Savi, Patrizia4; Yan, Qingyun2; Li, Wenmei1
2020-11-01
Source PublicationREMOTE SENSING
Volume12Issue:22Pages:24
AbstractGlobal Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth's surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data.
KeywordGlobal Navigation Satellite System-Reflectometry (GNSS-R) soil moisture retrieval signal-to-noise ratio (SNR) random forest (RF) support vector machine (SVM)
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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences
DOI10.3390/rs12223679
WOS KeywordGPS SIGNALS ; SEA-ICE ; LAND ; REFLECTOMETRY ; REGRESSION ; SYSTEM ; REFLECTIVITY ; TEMPERATURE ; RECEIVER ; BIOMASS
Language英语
Funding ProjectNational Natural Science Foundation of China[42001375] ; National Natural Science Foundation of China[42001362] ; Natural Science Foundation of Jiangsu Province[BK20180765] ; Natural Science Foundation of Jiangsu Province[BK20191384] ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists[RK032YZZ18003] ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF)[217152] ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF)[219066] ; Shanghai Leading Talent Project[E056061] ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese 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 for Selected Overseas Scientists ; Nanjing Technology Innovation Foundation for Selected Overseas Scientists ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) ; Shanghai Leading Talent Project ; Shanghai Leading Talent Project ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000595054500001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/80565
Collection中国科学院国家天文台
Corresponding AuthorJin, Shuanggen
Affiliation1.Nanjing Univ Posts & Telecommun, Dept Surveying & Geoinformat, Nanjing 210046, 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, Corso Duca Abruzzi 24, I-10129 Turin, Italy
Recommended Citation
GB/T 7714
Jia, Yan,Jin, Shuanggen,Savi, Patrizia,et al. Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach[J]. REMOTE SENSING,2020,12(22):24.
APA Jia, Yan,Jin, Shuanggen,Savi, Patrizia,Yan, Qingyun,&Li, Wenmei.(2020).Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach.REMOTE SENSING,12(22),24.
MLA Jia, Yan,et al."Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach".REMOTE SENSING 12.22(2020):24.
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