Mapping Freezing and Thawing Surface State Periods With the CYGNSS Based F/T Seasonal Threshold Algorithm

Carreño-Luengo, Hugo; Ruf, Christopher S.

doi:10.1109/jstars.2022.3216463

Cited by 10 publications

(7 citation statements)

References 55 publications

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“…It is the first GNSS-R pathfinder mission consisting of eight Low Earth Orbiting (LEO) micro-satellites. CYGNSS was launched in December 2016 with the main objective of near-surface ocean wind and currents monitoring (e.g., [34], [35], [36]); however, it can also be used as tool for the remote sensing of other environmental features, including but not limited to, inland water monitoring (e.g., [37], [38], [39]), soil moisture, wetlands and biomass observation ( [40], [41], [42]), freeze/thaw state retrieval ( [43], [44]), and mesoscale ocean eddies observations [45]. Mayer and Ruf [46] proposed the potential of CYGNSS for conducting altimetry over lake ice and using the transparency of ice to GNSS L-band signals to infer ice thickness.…”

Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

Ghiasi,

Duguay,

Murfitt

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Good agreement was achieved between ice phenology records derived from CYGNSS data and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The CYGNSS timings for freeze-up, i.e., the period starting with the first appearance of ice on the lake (freeze-up start; FUS) until the lake becomes fully ice covered (freeze-up end; FUE), as well as those for breakup, i.e., the period beginning with the first pixel of open water (breakup start; BUS) and ending when the whole lake becomes ice-free (breakup end; BUE), were validated against the phenology dates derived from MODIS images. Mean absolute errors are 7, 5, 10, 4 and 5 days for FUS, FUE, BUS, BUE and ice cover duration, respectively. Observations revealed the sensitivity of GNSS reflected signals to surface melt prior to the appearance of open water conditions as determined from MODIS, which explains the larger difference of 10 days for BUS.

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Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

Ghiasi,

Duguay,

Murfitt

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It is shown that the combined waveform is with a steeper leading edge slope (LES). According to (26), the ranging standard deviation (STD) is partly determined by the ranging sensitivity S r , which is defined as the normalized slope of the waveform. Therefore, the increasing LES of the combined waveform will contribute to increasing S r and reducing the ranging STD, which implies better altimetry performance.…”

Section: Combination Of Reflected Waveformsmentioning

confidence: 99%

“…As calculated in Section III-C2, the combined waveform exhibits an improved SNR. According to (26), both the altimetry sensitivity S r and SNR of the combined waveform increase. Hence, the theoretical range precision σ ρ should be improved.…”

Section: Preliminary Performance Validation In Geophysical Applicationsmentioning

confidence: 99%

“…CYGNSS mission was designed for monitoring high wind speeds near the center of tropical cyclones. Additionally, other geophysical applications have been also demonstrated with the CYGNSS data products, e.g., sea surface height [17]- [19], water mask [20], mean sea slope [21], heat flux [22], soil moisture [23], soil freeze/thaw [24]- [26], and sea surface microplastic concentration [27]. The two most commonly applied techniques are conventional GNSS-R (cGNSS-R) and interferometric GNSS-R (iGNSS-R) [4].…”

Section: Introductionmentioning

confidence: 99%

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Coherent Combination of GPS III L1 C/A and L1C Signals for GNSS Reflectometry

Du,

Nan,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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With the evolution of Global Navigation Satellite System (GNSS), more GNSS satellites and civilian signals are available for GNSS Reflectometry (GNSS-R). Developments of new onboard processing strategies can improve the observation performance of spaceborne GNSS-R. To this end, this article proposes a new processing method by coherently combining reflected Global Positioning System (GPS) III L1 C/A and L1C signals. By exploiting the additional signal component, the signalto-noise ratio of the reflected signal can be significantly improved. Moreover, taking advantage of the narrower auto-correlation function of the combined signal, the spatial resolution and the performance on geophysical applications can be significantly improved. The proposed method has been validated by processing cyclone GNSS (CYGNSS) raw intermediate frequency data including the direct and reflected signals from GPS III satellites. The results indicate that the signal-to-noise ratio (SNR) of the combined reflected waveform can be improved by ∼2 dB compared to the L1 C/A waveform. Moreover, the SNR of the combined signal can be improved more efficiently using a longer coherent integration interval compared to the L1 C/A signal. Preliminary altimetric results demonstrate a 35.3%-61.6% improvement in the ranging standard deviation, and a 22.4%-64.4% improvement in the median absolute deviation, compared to L1 C/A measurements. Additionally, the correlation coefficient between combined measurements and wind speed improves by 26.3% on average compared to L1 C/A measurements, and 45.7% for high winds. This article presents a novel GNSS-R onboard signal processing method with improved performance, which can provide a reference for the design of future GNSS-R instruments.

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“…NASA launched the Cyclone Global Navigation Satellite System (CYGNSS) in 2016, which comprises eight microsatellites. The scientific applications of this constellation have expanded beyond the detection of ocean parameters to include monitoring and retrieval of soil moisture, vegetation biomass, flood inundation, wetlands, and near-surface soil freeze/thaw state [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. It is worth noting that the two GNSS-R CubeSats recently launched by Spire also have the capability to provide soil moisture products [22].…”

Section: Introductionmentioning

confidence: 99%

LAGRS-Soil: A Full-Polarization GNSS-Reflectometry Model for Bare Soil Applications in FY-3E GNOS-R Payload

Wu,

Ouyang,

Xia

et al. 2023

Remote Sensing

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The Land Surface GNSS Reflection Simulator (LAGRS)-Soil model represents a significant advancement in soil moisture detection with the aid of Global Navigation Satellite System (GNSS) Occultation Sounder-Reflectometry (GNOS-R) technology, which is one payload of the Fengyun-3E (FY-3E) satellite that was launched on 5 July 2021. To fully exploit the properties of noncoherent scattering, the LAGRS-Soil model has the capability to calculate DDM information for different observational geometries, which relies on the random surface scattering models employed in LAGRS-Soil. This will provide a comprehensive understanding of soil moisture dynamics across diverse terrains and environments. One of the most notable features of LAGRS-Soil is its ability to obtain DDMs for full polarizations, which enhances soil moisture retrievals compared to current methods that only utilize the commonly used LR polarization (left-hand circular polarization received and right-hand circular polarization transmitted). Meanwhile, the model can also capture frozen soil DDMs which holds immense potential for near-surface Freezing/Thawing (F/T) detection, opening up new research and application opportunities in cold climate regions. LAGRS-Soil is built on microwave scattering models, making it a robust and efficient theoretical model for the FY-3E GNOS-R payload. This model can support ongoing soil moisture retrieval efforts by combining physical models with investigations of diffuse scattering and polarization capabilities for soil moisture detection.

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Mapping Freezing and Thawing Surface State Periods With the CYGNSS Based F/T Seasonal Threshold Algorithm

Cited by 10 publications

References 55 publications

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

Potential of GNSS-R for the Monitoring of Lake Ice Phenology

Coherent Combination of GPS III L1 C/A and L1C Signals for GNSS Reflectometry

LAGRS-Soil: A Full-Polarization GNSS-Reflectometry Model for Bare Soil Applications in FY-3E GNOS-R Payload

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