Paper Info
Title
Improving the SSH Retrieval Precision of Spaceborne GNSS-R Based on a New Grid Search Multihidden Layer Neural Network Feature Optimization Method
Abstract
The altimetry precision of conventional spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) is limited, and the error models are complicated. To compensate for the shortcomings of conventional methods, we present a new grid search multihidden layer neural network feature optimization method (GSMHLFO) for sea surface height (SSH) retrieval. Firstly, the GSMHLFO is constructed by combining the multihidden layer neural network, feature engineering, and a grid search algorithm. Moreover, the retrieval performance of the GSMHLFO and its sensitivity to various features are analyzed. By analyzing 14 feature sets with different information details, we concluded that the elevation, signal-to-noise ratio (SNR), atmospheric delay, and ocean wind speed can provide essential contributions to the SSH retrieval based on GSMHLFO. Secondly, the Technical University of Denmark 18 mean sea surface (DTU18 MSS), which is corrected by the TPXO8 global tide model, was used to verify the GSMHLFO. The number of hidden layers and neurons was optimized using the grid search algorithm. The experimental results show that the proposed GSMHLFO with four hidden layers and 200 neurons per layer has a better retrieval performance. Compared with DTU18, the mean absolute difference (MAD), the root mean square error (RMSE), and the Pearson correlation coefficient (PCC) equal 4.23 m, 5.94 m, and 0.98, respectively. The retrieval precision obtained is significantly improved compared to that reported in the literature for the TDS-1 SSH retrieval. Finally, the retrieval performance of the GSMHLFO and the traditional HALF single-point retracking method were compared. The precision of GSMHLFO is higher than that of traditional retracking method according to MAD, RMSE, and PCC, which are increased by 32.86, 25.00, and 8.99%. The GSMHLFO will provide innovative theoretical and methodological support for the high-precision SSH retrieval of GNSS-R altimetry satellites in the future.
Year
DOI
Venue
2022
10.3390/rs14133161
REMOTE SENSING
Keywords
DocType
Volume
GSMHLFO, feature engineering, TDS-1, integral delay waveform, DTU18 MSS
Journal
14
Issue
ISSN
Citations 
13
2072-4292
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Qiang Wang113.05
Wei Zheng203.38
Wu Fan31731192.15
Huizhong Zhu495.34
Aigong Xu5116.44
Yifan Shen601.35
Yelong Zhao700.34