Machine Learning Methods for Spaceborne GNSS-R Sea Surface Height Measurement From TDS-1

Abstract

Sea surface height (SSH) retrieval based on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) usually uses the GNSS-R geometric principle and delay-Doppler map (DDM). The traditional method condenses the DDM information into a single scalar measure and requires error model correction. In this article, the idea of using machine learning methods to retrieve SSH is proposed. Specifically, two widely-used methods, principal component analysis combined with support vector regression (PCA-SVR) and convolution neural network (CNN), are used for verification and comparative analysis based on the observation data provided by Techdemosat-1 (TDS-1). According to the DDM inversion method, ten features from TDS-1 Level 1 data are selected as inputs; The SSH verification model based on the Danmarks Tekniske Universitet (DTU) 15 ocean wide mean SSH model and the DTU global ocean tide model is used as output verification of SSH. For the hyperparameters in the machine learning model, a grid search strategy is used to find the optimal values. By analyzing the TDS-1 data from 31 GPS satellites, the mean absolute error (MAE), root-mean-square error (RMSE) and coefficient of determination (R2) of the PCA-SVR inversion model are 0.61 m, 1.72 m, and 99.56%, respectively; and the MAE, RMSE, and R2 of the CNN inversion model is 0.71 m, 1.27 m, and 99.76%, respectively. In addition, the time required to train the PCA-SVR and CNN inversion models is also analyzed. Overall, the technique proposed in this article can be confidently applied to SSH inversion based on TDS-1 data.

Keywords

• Convolution neural network (CNN)
• Global Navigation Satellite System Reflectometry (GNSS-R)
• principal component analysis combined with support vector regression (PCA-SVR)
• sea surface height (SSH)