Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

Li, Weiqiang; Cardellach, Estel; Fabra, Fran; Ribó, S.; Rius, A.

doi:10.1029/2018gl080976

Cited by 84 publications

(58 citation statements)

References 49 publications

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“…However, in Marine GNSS reflection measurement, the power waveform and peak are essentially determined by the roughness. Therefore, this method can be used in Global Navigation Satellite System-Reflectometry (GNSS-R) altimetry if the surface is smooth enough [21].…”

Section: Tracking Point Delay Estimation Methodsmentioning

confidence: 99%

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Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

Qiu

Jin

2020

Remote Sensing

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Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error ( RMSE ) of 3.56 m and, a correlation coefficient ( R ) of 0.95.

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Section: Tracking Point Delay Estimation Methodsmentioning

confidence: 99%

“…After matching, a series of corresponding lon 0 , lat 0 , H measured , and H model can be evaluated. In this paper, the bias average absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are calculated by using Formulas (20)(21)(22)(23).…”

Section: Mssh From Cygnssmentioning

confidence: 99%

Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

Qiu

Jin

2020

Remote Sensing

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“…The GNSS-R technique can be utilized to retrieve geophysical parameters such as sea surface height [1,2], ocean surface wind [3,4], vegetation [5,6], and soil moisture [7,8], mainly based on delay-Doppler maps (DDMs). It was noted that several studies have been conducted on GNSS-R with phase observation (i.e., carrier phase measurement) [9][10][11][12], which is not included in DDMs and is not discussed in this paper. To generate a DDM, the GNSS-R receiver performs a series of correlation processing procedures at different code phase delays and Doppler frequency bins.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Ocean Wind Speed Using Super-Resolution Delay-Doppler Maps

Wang

Juang

2020

Remote Sensing

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The use of reflected Global Navigation Satellite System (GNSS) signals has shown to be effective for some remote sensing applications. In a GNSS Reflectometry (GNSS-R) system, a set of delay-Doppler maps (DDMs) related to scattered GNSS signals is formed and serves as a measurement of ocean wind speed and roughness. The design of the DDM receiver involves a trade-off between computation/communication complexity and the effectiveness of data retrieval. A fine-resolution DDM reveals more information in data retrieval while consuming more resources in terms of onboard processing and downlinking. As a result, existing missions typically use a compressed or low-resolution DDM as a data product, and a high-resolution DDM is processed for special purposes such as calibration. In this paper, a deep learning, super resolution algorithm is developed to construct a high-resolution DDM based on a low-resolution DDM. This may potentially enhance the data retrieval results with no impact on the instrument design. The proposed method is applied to process the DDM products disseminated by the Cyclone GNSS (CYGNSS) and the effectiveness of wind speed retrieval is demonstrated.

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“…CYGNSS is a constellation consisting of eight small satellites carrying GNSS receivers specially designed to capture surface-reflected GNSS signals over the tropics to retrieve ocean surface wind speeds. To explore additional capabilities, previous studies have shown initial applications to monitor land surface hydrological components such as soil moisture [7,8], lake ice thickness/water levels [9,10], and wetlands [11]. The first attempt, described in [6], used CYGNSS data to map flood inundation during the 2017 Atlantic hurricane season.…”

Section: Introductionmentioning

confidence: 99%

Using CYGNSS Data to Monitor China’s Flood Inundation during Typhoon and Extreme Precipitation Events in 2017

et al. 2019

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NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, is a small satellite constellation designed to measure the ocean surface wind speed in hurricanes and tropical cyclones. To explore its additional capabilities for applications on the land surface, this study investigated the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017. The results showed that despite the lack of quantitative evaluation, the CYGNSS-derived surface reflectivity (SR) and flood inundation area was qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and Soil Moisture Active Passive (SMAP)/Soil Moisture and Ocean Salinity (SMOS)-derived total brightness temperature at circular polarization ( T b C ). The results provide supporting evidence for further designation of Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) constellations to monitor land surface hydrology.

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Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

Cited by 84 publications

References 49 publications

Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

Retrieval of Ocean Wind Speed Using Super-Resolution Delay-Doppler Maps

Using CYGNSS Data to Monitor China’s Flood Inundation during Typhoon and Extreme Precipitation Events in 2017

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