Study on the Exploration of Spaceborne GNSS-R Raw Data Focusing on Altimetry

This article discusses the sea level determination using raw intermediate frequency data transmitted from the Global Navigation Satellite System (GNSS) and received by spaceborne GNSS-Reflectometry satellites, TechDemoSat-1. The reflected signals scattered from a sea ice surface and a rough sea surf...

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Bibliographic Details
Published in:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors: Minfeng Song, Xiufeng He, Xiaolei Wang, Dongzhen Jia, Ruya Xiao, Hongkai Shi, Yihao Wu
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2020
Subjects:
Online Access:https://doi.org/10.1109/JSTARS.2020.3028693
https://doaj.org/article/e0d98e1ade67487b9f7b2c976ff653f8
Description
Summary:This article discusses the sea level determination using raw intermediate frequency data transmitted from the Global Navigation Satellite System (GNSS) and received by spaceborne GNSS-Reflectometry satellites, TechDemoSat-1. The reflected signals scattered from a sea ice surface and a rough sea surface are investigated. The altimetry method based on the bistatic group delay (code phase) from GNSS signals for sea level estimation are introduced. The two raw IF datasets recorded on January 18 and 27, 2015 for a duration of 40 s are analyzed to drain more information than Level 1 data. The results show a good consistence with mean sea surface (MSS) model. The orbit error of the GNSS-R satellite is corrected by a proposed method that combines the MSS and the least squares solution, which help evaluate the actual altimetry precision. The defect of fixed temporal resolution and fixed four onboard processing channels of Level 1 data can be improved by postprocessing using software define receiver to mine more information, so as to explore the potential. At the end, fake high signal-to-noise ratio Doppler delay maps from the raw data are analyzed, which provides a reference for the altimetry of GNSS-R technique using raw data.