GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With Multi-GNSS Constellations

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

doi:10.1109/tgrs.2022.3182987

Cited by 14 publications

(4 citation statements)

References 37 publications

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Section: Data Collection and Methods Of Modelingmentioning

confidence: 99%

“…The VMD algorithm establishes a constrained optimization problem based on the assumption that all components are narrowband signals concentrated near their respective center frequencies and decomposes the original signal into intrinsic mode functions (IMFs) with a certain number of layers at different frequencies 39 where X (t) is the original signal, u s (t) is the sth IMF component, r n (t) is the residual term and S is the number of decomposition levels. The IMF is amplitude modulation and frequency modulation signal, written as 40 : www.nature.com/scientificreports/ This is notable, although the superposition of all sub-signals u k is the SNR, the phase φ s of the sth sub-signal is not directly related to φ γ in the SNR. The IMF that is highly consistent with the original SNR is selected as the trend term, and the remaining IMFs can be reconstructed to obtain the oscillation term 41 .…”

Section: Data Collection and Methods Of Modelingmentioning

confidence: 99%

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Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Ansari¹,

Seok

Jamjareegulgarn

2022

Sci Rep

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The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.

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Section: Data Collection and Methods Of Modelingmentioning

confidence: 99%

Section: Data Collection and Methods Of Modelingmentioning

confidence: 99%

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Ansari¹,

Seok

Jamjareegulgarn

2022

Sci Rep

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show abstract

“…The problem of false peaks occurs when using spectral analysis, making the inversion results distorted. In order to solve this, some scholars have used empirical modal decomposition and variational modal decomposition (VMD) to reconstruct the SNR residual sequence to eliminate the distortion of inversion results [17]. Conventional spectral analysis leads to distorted inversion results due to the gradual decrease in the interfering signal components as the elevation angle increases.…”

Section: Introductionmentioning

confidence: 99%

Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences

Hu,

Tian,

Yan

et al. 2024

Remote Sensing

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The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique.

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“…These missions have significantly broadened the horizons for GNSS-R applications in space [13]. The availability of signal sources makes GNSS-R technology suitable for a wide range of applications, such as soil moisture [14][15][16], ice [17][18][19][20], flood [21,22], and sea wind [23], in recent years. In sea level altimetry, GNSS-R also proves valuable for its precision and spatial resolution advantages [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Advancing Sea Surface Height Retrieval through Global Navigation Satellite System Reflectometry: A Model Interaction Approach with Cyclone Global Navigation Satellite System and FengYun-3E Measurements

Xing,

Yang,

Zhang

et al. 2024

Remote Sensing

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The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion.

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GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With Multi-GNSS Constellations

Cited by 14 publications

References 37 publications

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology

Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences

Advancing Sea Surface Height Retrieval through Global Navigation Satellite System Reflectometry: A Model Interaction Approach with Cyclone Global Navigation Satellite System and FengYun-3E Measurements

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