Remote Sensing (Nov 2022)

Coastal High-Temporal Sea-Surface Altimetry Using the Posterior Error Estimations of Ionosphere-Free PPP and Information Fusion for Multi-GNSS Retrievals

  • Wei Zhou,
  • Shaofeng Bian,
  • Yi Liu,
  • Liangke Huang,
  • Lilong Liu,
  • Cheng Chen,
  • Houpu Li,
  • Guojun Zhai

DOI
https://doi.org/10.3390/rs14215599
Journal volume & issue
Vol. 14, no. 21
p. 5599

Abstract

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Ocean tidal variation is a key parameter for ensuring coastal safety, monitoring marine climate, and maintaining elevation datum. Recently, the ground-based global navigation satellite system reflectometry (GNSS-R) technique has been applied for regional tidal measurements, which is somewhat restricted in terms of temporal and spatial resolutions. A convenient method to improve temporal resolution of measurements is to combine multi-GNSS observations. This paper proposes a new sea-surface altimetry method using the posterior errors (PE) of dual-frequency carrier-phase signals derived from the ionosphere-free Precise Point Positioning (IF-PPP). Considering that the number of initial retrievals is obviously unsuitable for minute-level tidal measurements, both the time sliding window based on the Lomb–Scargle periodogram and a weighted cubic spline smoothing function are significant processing steps for estimating the reflector heights between the sea surface and antenna center. Measurements from two coastal GNSS stations with different tidal amplitudes are used to test the proposed method and compare it with the tide gauge and the signal-to-noise ratio (SNR) methods, respectively. The experimental results show that the multi-GNSS PE combination method can be used to estimate a minute-level sea level time series, and its root-mean-squared errors (RMSE) are about 12.5 cm. In terms of correlation, for all results, the corresponding coefficients exceed 0.97. Moreover, this combined PE method demonstrates a significant advantage in increasing temporal resolution, which is beneficial for application on high-frequency sea-level monitoring.

Keywords