Enhancing Sea Surface Height Retrieval with Triple Features Using Support Vector Regression
Abstract
:1. Introduction
2. GNSS-R Sea Surface Height Retrieval Method
2.1. Principle of GNSS Conventional SNR-Based Altimetry
2.2. Support Vector Regression (SVR) Retrieval Principle
3. Experiments and Analysis
3.1. Data Sources and Processing
3.2. Retrieval Process of SVR Model
3.3. Results and Analysis
3.3.1. Retrieval Experiment of SC02 Station
3.3.2. Retrieval Experiment of BRST Station
3.4. Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | RMSE/cm | MAE/cm | Correlation Coefficient | a | b |
---|---|---|---|---|---|
CM | 19.5 | 15.8 | 0.971 | 0.9535 | 0.0969 |
SVR | 14.5 | 12.0 | 0.981 | 0.9976 | 0.0077 |
Method | RMSE/cm | MAE/cm | Correlation Coefficient | a | b |
---|---|---|---|---|---|
CM | 30.6 | 26.0 | 0.985 | 0.9730 | 0.1010 |
SVR | 25.3 | 21.3 | 0.990 | 0.9996 | −0.0092 |
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Hu, Y.; Tian, A.; Liu, W.; Wickert, J. Enhancing Sea Surface Height Retrieval with Triple Features Using Support Vector Regression. Remote Sens. 2023, 15, 4029. https://doi.org/10.3390/rs15164029
Hu Y, Tian A, Liu W, Wickert J. Enhancing Sea Surface Height Retrieval with Triple Features Using Support Vector Regression. Remote Sensing. 2023; 15(16):4029. https://doi.org/10.3390/rs15164029
Chicago/Turabian StyleHu, Yuan, Aodong Tian, Wei Liu, and Jens Wickert. 2023. "Enhancing Sea Surface Height Retrieval with Triple Features Using Support Vector Regression" Remote Sensing 15, no. 16: 4029. https://doi.org/10.3390/rs15164029
APA StyleHu, Y., Tian, A., Liu, W., & Wickert, J. (2023). Enhancing Sea Surface Height Retrieval with Triple Features Using Support Vector Regression. Remote Sensing, 15(16), 4029. https://doi.org/10.3390/rs15164029