期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 17, 期 8, 页码 1333-1337出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2948566
关键词
Wind speed; Satellites; Training; Sea measurements; Global Positioning System; Spaceborne radar; Geophysical model function (GMF); Global Navigation Satellite System Reflectometry (GNSS-R); machine learning; neural networks; TechDemoSat-1 (TDS-1); wind speed
类别
资金
- Geo.X
- Research Network for Geosciences in Berlin and Potsdam
A machine learning technique is implemented for retrieving space-borne Global Navigation Satellite System Reflectometry (GNSS-R) wind speed. Conventional approaches commonly fit a function in a predefined form to matchup data in a least-squares (LS) sense, mapping GNSS-R observations to wind speed. In this study, a feedforward neural network is trained for TechDemoSat-1 (TDS-1) wind speed inversion. The input variables, along with the derived bistatic radar cross-section sigma(0), are selected after investigating the wind speed dependence and the model performance. When compared to an LS-based approach, the derived model shows a significant improvement of 20% in the root mean square error (RMSE). The proposed neural network demonstrates an ability to model a variety of effects degrading the retrieval accuracy such as the different levels of the effective isotropic radiated power (EIRP) of GPS satellites. For example, the derived Mean Absolute Error (MAE) of the satellite with SVN 34 is decreased by 32% using the machine-learning-based approach.
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