GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition

Hu, Yuan; Yuan, Xintai; Liu, Wei; Wickert, Jens; Zhi, Jiang; Haas, Rüdiger

doi:10.1109/jstars.2021.3118398

Cited by 14 publications

(9 citation statements)

References 43 publications

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“…The requirement of GNSS-IR for sea level measurement is to record both the direct GNSS signals and the GNSS reflected signals that are reflected off from the sea surface. There have been numerous different methods and techniques for using the GNSS-reflected signals for sea level observations and lake level monitoring from ground-based surfaces [1], [3], [5], [6], [7], [8].…”

Section: Identification Of Optimum Parameters For Snr Data Analysismentioning

confidence: 99%

Determination of optimum parameters for measuring sea level using GNSS Interferometric Reflectometry: A Case study of Port Klang, Malaysia

Idris,

Sulaiman,

Tahar

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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This paper presents a new approach of measuring sea level height using Global Navigation Satellite Systems GNSS Interferometric Reflectometry (GNSS-IR) by method of Signal to Noise Ratio (SNR) data analysis from a single geodetic receiver. The study location was selected at Port Klang Tide Gauge (PKTG) station, Malaysia. The purpose of this study was to identify the optimum criteria for the parameters involved to get the best accuracy of reflected heights. The accuracy of reflected height derived from SNR information is strongly dependent on the value of parameters used for quality control. The parameters involved shall achieve the best possible frequency correlated with the sea surface. There are 5 parameters were identified such as estimation of the satellite's azimuth and elevation angle, identify noise frequency range, selection of frequency amplitude power, and calculation of the peak frequency to noise ratio using the Lomb-Scargle periodogram (LSP). The optimum value of each parameter is important to get accurately selected reflector heights and then produce a quality local sea level time series.

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Section: Identification Of Optimum Parameters For Snr Data Analysismentioning

confidence: 99%

Determination of optimum parameters for measuring sea level using GNSS Interferometric Reflectometry: A Case study of Port Klang, Malaysia

Idris,

Sulaiman,

Tahar

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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show abstract

“…This method can obtain instantaneous RHs with high temporal resolution. Hu et al proposed a variational mode decomposition method and improved the Burg algorithm for SNR data spectrum analysis, and about a 5 cm precision was reached by adopting GTGU station data [20,21]. Although there are many methods available to extract the oscillation frequency of SNR data, the LSP is the most widely applied to GNSS-IR altimetry due to better spectrum analysis of non-uniform sampling signals.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of GNSS-IR sea-level retrieval methods

Hou,

Wang,

et al. 2024

Meas. Sci. Technol.

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The Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique has been confirmed to retrieve the sea level using signal-to-noise ratio (SNR) data. To investigate the suitability of different GNSS-IR sea level retrieval methods, several strategies were tested during the data process, including: the whole arc Lomb-Scargle periodogram (whole arc LSP) method and window LSP (WinLSP) method when spectral analysis on SNR data; the tidal harmonic analysis (THA) and dynamic SNR method when using dynamic correction on retrievals; and the moving window smoothing method proposed to apply on processing of retrievals. Furthermore, the THA method is improved by segmented SNR data, and the moving window smoothing with robust local weighted regression (RLOWESS) and Savitzky-Golay (SG) filter was adopt for better serviceability. One-month data from the SC02 station were used to test all the strategies by comparing with local tide gauge records. HKQT station further verified the usability of moving window smoothing. The results confirmed that the WinLSP method can obtain more retrievals implying higher temporal resolution, but whole arc LSP method reaches better precision. The former is easily susceptible to sampling rate of SNR. Correction results of dynamic SNR method are better than that of segmented THA method. When the former is applied to retrievals of WinLSP method, the retrievals of all windows from multiple SNR arcs should be adjusted simultaneously. The moving window smoothing based RLOWESS and SG filter is more applicable to the retrievals corrected by segmented THA method, and the smoothing effect of two smoothing methods has little difference. In addition, wind speed more than 20m/s seriously affects the effect of GNSS-IR sea level retrieval. GNSS-IR technique has the potential of centimeter-level retrieved sea level.

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“…proposed a new GNSS‐IR sea level estimation model combined with Variational Mode Decomposition (VMD). The experimental results showed that RMSE and R of the new model are 4.86 cm and 0.97, respectively (Y. Hu et al., 2021). In 2021, Liu et al.…”

Section: Introductionmentioning

confidence: 99%

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

Zheng

Chai

et al. 2023

Earth and Space Science

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Multisatellite systems and multi‐signal‐to‐noise ratio types provide a more prosperous data basis for the inversion of sea level by GNSS‐IR technology. However, there are few studies on data reconstruction of the retrieved sea level height. This study takes the SPBY station as an example and introduces the least squares learning cross‐checking method with the Gaussian kernel model (GKM) L2 constraint. Furthermore, research on hourly sea level reconstruction based on GNSS‐IR is carried out. Compared with the measured sea level, the sea level height in 2021 obtained after the fusion of multisource data has an R of 0.905 and an RMSE of 0.144 m. And the result obtained after data reconstruction has an R of 0.958 and an RMSE of 0.090 m. Compared with the multisource data fusion results before reconstruction, R is increased by 5.9%, and RMSE is decreased by 37.5%. Finally, using the reconstructed sea level data based on Long Short‐Term Memory (LSTM) artificial neural network to carry out the research on sea level prediction, which verifies the conclusion that more reliable forecast values can be obtained based on 5 months of training data. Among them, the R from 1 to 24 hr is 0.905, and the RMSE is 0.145 m. Compared with the inversion accuracy of GNSS‐IR, the R is increased by 2.0%, and the RMSE is decreased by 21.4%. This study demonstrates the feasibility of GNSS‐IR technology and L2‐constrained least squares learning cross‐checking method based on the GKM to reconstruct sea level data with high temporal resolution and high accuracy. The reliability of the sea level prediction based on GKM reconstruction and LSTM is verified in the sea level forecast of the next 24 epochs, which has essential applications in sea level data recovery and forecasting.

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GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition

Cited by 14 publications

References 43 publications

Determination of optimum parameters for measuring sea level using GNSS Interferometric Reflectometry: A Case study of Port Klang, Malaysia

Determination of optimum parameters for measuring sea level using GNSS Interferometric Reflectometry: A Case study of Port Klang, Malaysia

Comparative analysis of GNSS-IR sea-level retrieval methods

Hourly Sea Level Prediction‐Based GNSS‐IR Inversions by Combining the Least Squares Learning Cross‐Checking Method With the Gaussian Kernel Model L2 Constraint and LSTM

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