First Assessment of Geophysical Sensitivities from Spaceborne Galileo and BeiDou GNSS-Reflectometry Data Collected by the UK TechDemoSat-1 Mission

Hammond, Matthew L.; Foti, Giuseppe; Rawlinson, Jonathan; Gommenginger, Christine; Srokosz, Meric; King, Lucinda; Unwin, Martin; Rosello, Josep

doi:10.3390/rs12182927

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…These variables are calibrated in the CYGNSS Level 1B product, which is usually used for wind speed retrieval [33]. The signal to noise ratio (SNR) is also strongly correlated with sea surface roughness [34,35]. NBRCS and LES are the two most important observables in CYGNSS products, which are very effective for wind speed retrieval.…”

Section: Cygnss Observations and Other Auxiliary Observationsmentioning

confidence: 99%

“…The experimental results show that the retrieval accuracy of the PSO method is superior to the classical MVE method. Hammond et al used the spaceborne Galileo and BDS GNSS-R data collected by the UK TechDemoSat-1 mission to assess the geophysical sensitivity for the first time [35]. Unfortunately, due to the limited amount of reflective BDS data currently available, it is impossible to conduct a comprehensive analysis of the geophysical sensitivity of BDS signals.…”

Section: Introductionmentioning

confidence: 99%

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GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Zuo

et al. 2023

Remote Sensing

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Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

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Section: Cygnss Observations and Other Auxiliary Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Zuo

et al. 2023

Remote Sensing

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“…The complex waveform products from the other GNSS constellations can be used to assess the performance of these new GNSS signals in different applications (e.g., in [27,71,72]). Such assessments are of great importance for the definition and optimization of mission and instrument specifications for future spaceborne GNSS systems, e.g., in establishing the link budget for reflected signals from different GNSS constellations.…”

Section: Spaceborne Gnss-r From Different Gnss Constellation and Freq...mentioning

confidence: 99%

Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications

Cardellach

Ribó

et al. 2022

Remote Sensing

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Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.

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“…In [41], an assessment of the sensitivity of different GNSS signals is presented, concluding that even though GALILEO signals achieve lower SNR than GPS ones, they provide an improved surface resolution. This can be directly translated to target detection of smaller objects than with GPS signals.…”

Section: Target Detectionmentioning

confidence: 99%

Airborne GNSS-R: A Key Enabling Technology for Environmental Monitoring

Perez-Portero

Muñoz-Martín

Park

et al. 2021

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

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Weather forecast using GNSS-R relies, to a large extent, on data acquired by airborne and spaceborne platforms due to the extended coverage that can be achieved. GEO satellites can retrieve measurements over large areas, but with spatial resolutions on the order of thousands of meters per pixel. LEO satellites with polar orbits can provide measurements over the entire world, but they can not provide good spatial nor high temporal resolutions by themselves. LEO constellations of small satellites have the same coverage with improved spatial resolution and revisit times by using data fusion techniques. High-Altitude Pseudo-Satellites (HAPS) offer an excellent temporal and spatial resolution, but only over limited areas. In this work, the potential of using hosted GNSS-R payloads on commercial aircraft is explored as an alternative and cost-effective means to achieve a very high resolution and a very low revisit time for regional environmental applications. Finally, a case study of soil moisture monitoring over Europe is presented, including an analysis of the expected performance.

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First Assessment of Geophysical Sensitivities from Spaceborne Galileo and BeiDou GNSS-Reflectometry Data Collected by the UK TechDemoSat-1 Mission

Cited by 13 publications

References 20 publications

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications

Airborne GNSS-R: A Key Enabling Technology for Environmental Monitoring

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