Spaceborne GNSS Reflectometry

Yu, Kegen; Han, Songtao; Bu, Jinwei; An, Yuhang; Zhou, Zhewen; Wang, Changyang; Tabibi, Sajad; Cheong, Joon Wayn

doi:10.3390/rs14071605

Cited by 20 publications

(8 citation statements)

References 226 publications

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“…The basic measurement in this project was the use of GNSS. The theory of GNSS is described in many technical articles, as well as the development of accuracy [23][24][25]. In high geographical latitudes, the integration of the GLONASS navigation system proves to be advantageous [26,27].…”

Section: Methodsmentioning

confidence: 99%

Ice Elevation Change Based on GNSS Measurements along the Korth-Traverse in Southern Greenland

2022

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In 1912, a Swiss expedition led by meteorologist Alfred de Quervain crossed the Greenland ice sheet on a route from Disko Bay to Tasiilaq. Based on that, in 2002, a series of geodetic expeditions carried out by W. Korth and later by T. Hitziger began along the same traverse as in 1912, with the last measurements taken in May 2021. The statically collected GPS/GNSS data provide very accurate elevation changes at 36 points along the almost 700 km long crossing over a period of 19 years. According to this, there is a maximum increase of 2.1 m in the central area and a decrease of up to 38.7 m towards the coasts (influence Ilulissat Isbræ). By using kinematic GNSS measurements, there is a very dense profile with a spacing of a few meters. The comparison of those measurements is performed using crossing points or minimum distances and gives equivalent results for both methods. It is shown that local ice topography is preserved, and thus gaps in data sets can be caught. Areas of accumulation and ablation on the ice sheet can be identified, showing the widespread influence of outlet glaciers up to 200 km. The data can be used for direct verification of altimetry data, such as IceSat. Both IceSat elevations and their changes can be compared.

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Section: Methodsmentioning

confidence: 99%

Ice Elevation Change Based on GNSS Measurements along the Korth-Traverse in Southern Greenland

2022

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“…Historically, spaceborne GNSS sensors have been mainly developed for remote sensing purposes [ 24 , 25 , 26 ], e.g., radio occultation and reflectometry. In the last couple of decades, GNSS has been considered to support LEO space missions such as NASA’s Gravity Recovery and Climate Experiment (GRACE) mission and other programs [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

Troglia Gamba,

Polidori,

Minetto

et al. 2024

Sensors

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The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

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“…In addition, the satellite mission is also committed to GNSS radio occultation (GNSS-RO)related applications. It is worth mentioning that although the main objective of the above spaceborne GNSS-R tasks is to monitor ocean wind speed, these satellite tasks can also be used for sea surface height retrieval [8,9], sea ice detection [10,11], sea ice concentration (SIC) retrieval [12,13], sea ice thickness retrieval [14], tsunami detection [15], storm surge monitoring [16,17], wave height retrieval [18][19][20][21][22][23][24], rainfall detection [25,26], rainfall intensity retrieval [27,28], soil moisture retrieval [29] and biomass retrieval [30], etc.…”

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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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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Spaceborne GNSS Reflectometry

Cited by 20 publications

References 226 publications

Ice Elevation Change Based on GNSS Measurements along the Korth-Traverse in Southern Greenland

Ice Elevation Change Based on GNSS Measurements along the Korth-Traverse in Southern Greenland

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

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

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