Sea-Level Monitoring and Ocean Tide Analysis Based on Multipath Reflectometry Using Received Strength Indicator Data From Multi-GNSS Signals

Wang, Nazi; Xu, Tianhe; Gao, Fan; He, Yunqiao; Meng, Xinyue; Lili, Jing; Ning, Baojiao

doi:10.1109/tgrs.2022.3219074

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…The experimental results showed that sea levels determined from GPS SNR and the proposed method agreed well with a correlation coefficient of 0.97-0.98 and RMSE values less than 0.2 m for station SC02 in the US. We also proposed using SSI data in GNSS observation files for estimating sea levels, which is an alternative to existing methods because SSI data always exist [37]. Four multi-GNSS and multi-frequency datasets from three stations (SC02 in the US, station AT01 in Alaska, and station HKQT in Hong Kong) were used to monitor sea level.…”

Section: Gnss-ir Altimetrymentioning

confidence: 99%

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GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

Xu,

Wang,

et al. 2024

Remote Sensing

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For the past 20 years, Global Navigation Satellite System reflectometry (GNSS-R) technology has successfully shown its potential for remote sensing of the Earth’s surface, including ocean and land surfaces. It is a multistatic radar that uses the GNSS signals reflected from the Earth’s surface to extract land and ocean characteristics. Because of its numerous advantages such as low cost, multiple signal sources, and all-day/weather and high-spatiotemporal-resolution observations, this new technology has attracted the attention of many researchers. One of its most promising applications is GNSS-R ocean altimetry, which can complement existing techniques such as tide gauging and radar satellite altimetry. Since this technology for ocean altimetry was first proposed in 1993, increasing progress has been made including diverse methods for processing reflected signals (such as GNSS interferometric reflectometry, conventional GNSS-R, and interferometric GNSS-R), different instruments (such as an RHCP antenna with one geodetic receiver, a linearly polarized antenna, and a system of simultaneously used RHCP and LHCP antennas with a dedicated receiver), and different platform applications (such as ground-based, air-borne, or space-borne). The development of multi-mode and multi-frequency GNSS, especially for constructing the Chinese BeiDou Global Navigation Satellite System (BDS-3), has enabled more free signals to be used to further promote GNSS-R applications. The GNSS has evolved from its initial use of GPS L1 and L2 signals to include other GNSS bands and multi-GNSS signals. Using more advanced, multi-frequency, and multi-mode signals will bring new opportunities to develop GNSS-R technology. In this paper, studies of GNSS-R altimetry are reviewed from four perspectives: (1) classifications according to different data processing methods, (2) different platforms, (3) development of different receivers, and (4) our work. We overview the current status of GNSS-R altimetry and describe its fundamental principles, experiments, recent applications to ocean altimetry, and future directions.

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Section: Gnss-ir Altimetrymentioning

confidence: 99%

“…The results in these references verified the proposed methods, which provide substitutes for GNSS-IR technology and expand its applicability. ), 1), 9 [37] Combinations of code and phase…”

mentioning

confidence: 99%

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

Xu,

Wang,

et al. 2024

Remote Sensing

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“…Two typical applications of GNSS reflectometers are GNSS‐IR technology and cyclone GNSS (CYGNSS) technology (Arai et al., 2022; C. C. Chew & Small, 2018; Huang et al., 2021). With an increasing number of satellites in orbit (Darrozes et al., 2016), GNSS‐IR technology has been applied to sea level monitoring (Anderson, 2000; Löfgren et al., 2011; Wang et al., 2022), snow depth detection (An et al., 2020; Bilich et al., 2008; Larson et al., 2009; Yu et al., 2020), determination of soil moisture (C. Chew et al., 2016; Sanchez‐Martin et al., 2015), and identification of sea ice (Parvizi et al., 2021). This study aims at the increasing number of GNSS satellites and SNR types to research GNSS‐IR technology for monitoring sea levels.…”

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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“…Some studies choose to apply both the bending angle and the path delay corrections [24], [25]. Some studies choose to apply only one of them [26], [27] and some studies choose to apply no tropospheric correction at all [14], [28]- [32].…”

Section: Introductionmentioning

confidence: 99%

A Novel Tropospheric Error Formula for Ground-Based GNSS Interferometric Reflectometry

Feng,

Haas,

Elgered

2023

IEEE Trans. Geosci. Remote Sensing

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We deduce a Novel Interferometric Tropospheric Error (NITE) formula for ground-based GNSS interferometric reflectometry (GNSS-IR). This formula contains two parts: a geometric displacement error that accounts for the reflection point change due to the atmosphere and the earth curvature, and a path delay derived following the definition of the mapping function (with the small curve path effect included). We validate the NITE formula together with two previously used approaches, the bending angle correction and the mapping function path delay (MPF delay) using raytracing and radiosonde data. The raytracing results show that the NITE formula is more accurate than the previous approaches. Numerically, the geometric displacement error is < 5 % of the path delay error for a GNSS antenna located 20 m above sea level. We further evaluate six tropospheric correction strategies for GNSS-IR sea level monitoring through two sets of experiments. With an elevation angle range test, we show that applying no tropospheric correction and applying the bending angle correction plus the MPF delay both introduce large elevation-dependent biases. Analyzing the time series of differences between GNSS-IR and tide gauges sea level, we show that the bending angle correction with the widely used Bennett equation introduces long-term (4 h to several months) trends in the sea-level retrievals. We identify one station where the NITE formula produces better long-term (τ > 4 h) sea-level retrievals. Finally, we show that at low elevation angles, the bending angle correction can be reformulated as an MPF delay.

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Sea-Level Monitoring and Ocean Tide Analysis Based on Multipath Reflectometry Using Received Strength Indicator Data From Multi-GNSS Signals

Cited by 6 publications

References 41 publications

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

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

A Novel Tropospheric Error Formula for Ground-Based GNSS Interferometric Reflectometry

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