Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach
Global 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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/22/3679 |
| _version_ | 1827702547506266112 |
|---|---|
| author | Yan Jia Shuanggen Jin Patrizia Savi Qingyun Yan Wenmei Li |
| author_facet | Yan Jia Shuanggen Jin Patrizia Savi Qingyun Yan Wenmei Li |
| author_sort | Yan Jia |
| collection | DOAJ |
| description | Global 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. |
| first_indexed | 2024-03-10T14:57:49Z |
| format | Article |
| id | doaj.art-626b0b16ddd049708cd2444dbfdba463 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:57:49Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-626b0b16ddd049708cd2444dbfdba4632023-11-20T20:23:50ZengMDPI AGRemote Sensing2072-42922020-11-011222367910.3390/rs12223679Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning ApproachYan Jia0Shuanggen Jin1Patrizia Savi2Qingyun Yan3Wenmei Li4Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210046, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaPolitecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalySchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaDepartment of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210046, ChinaGlobal 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.https://www.mdpi.com/2072-4292/12/22/3679Global Navigation Satellite System-Reflectometry (GNSS-R)soil moisture retrievalsignal-to-noise ratio (SNR)random forest (RF)support vector machine (SVM) |
| spellingShingle | Yan Jia Shuanggen Jin Patrizia Savi Qingyun Yan Wenmei Li Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach Remote Sensing Global Navigation Satellite System-Reflectometry (GNSS-R) soil moisture retrieval signal-to-noise ratio (SNR) random forest (RF) support vector machine (SVM) |
| title | Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach |
| title_full | Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach |
| title_fullStr | Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach |
| title_full_unstemmed | Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach |
| title_short | Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach |
| title_sort | modeling and theoretical analysis of gnss r soil moisture retrieval based on the random forest and support vector machine learning approach |
| topic | Global Navigation Satellite System-Reflectometry (GNSS-R) soil moisture retrieval signal-to-noise ratio (SNR) random forest (RF) support vector machine (SVM) |
| url | https://www.mdpi.com/2072-4292/12/22/3679 |
| work_keys_str_mv | AT yanjia modelingandtheoreticalanalysisofgnssrsoilmoistureretrievalbasedontherandomforestandsupportvectormachinelearningapproach AT shuanggenjin modelingandtheoreticalanalysisofgnssrsoilmoistureretrievalbasedontherandomforestandsupportvectormachinelearningapproach AT patriziasavi modelingandtheoreticalanalysisofgnssrsoilmoistureretrievalbasedontherandomforestandsupportvectormachinelearningapproach AT qingyunyan modelingandtheoreticalanalysisofgnssrsoilmoistureretrievalbasedontherandomforestandsupportvectormachinelearningapproach AT wenmeili modelingandtheoreticalanalysisofgnssrsoilmoistureretrievalbasedontherandomforestandsupportvectormachinelearningapproach |