Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Bu, Jinwei; Yu, Kegen; Park, Hyuk; Huang, Weimin; Han, Songtao; Yan, Qingyun; Qian, Nijia; Lin, Yiruo

doi:10.3390/rs14184634

Cited by 14 publications

(10 citation statements)

References 53 publications

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“…First, the CYGNSS DDM data are down sampled and spatially matched with the ERA5 wind speed, ERA5 wave direction, ERA5 swell SWH and IMERG rainfall data to produce the matched data set. To ensure the quality of the data set, the sampling data need to be quality controlled and filtered according to the standards listed in [24,28,42], so as to obtain the filtered high-quality data set. Then, the data set is randomly divided into training set, verification set and test set.…”

Section: Methods Descriptionmentioning

confidence: 99%

“…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%

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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: Methods Descriptionmentioning

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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“…Furthermore, in recent years, national navigation satellite systems have been developed rapidly, the number of global navigation satellites has become more abundant, and remote sensing technology using GNSS signals has become increasingly advanced. At present, this technology has realized engineering applications in the fields of sea surface altitude measurement [ 8 , 9 ], effective wave height measurement at sea level [ 10 , 11 ], the remote sensing of wind fields at sea level [ 12 , 13 , 14 , 15 ], the remote sensing of seawater salinity [ 16 , 17 , 18 ], and tidal detection [ 19 , 20 , 21 ]. In land surface remote sensing, numerous breakthroughs have also been made for measuring quantities such as soil moisture [ 22 , 23 , 24 ], snow thickness [ 25 ], and vegetation cover [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Flying Target Detection Technology Based on GNSS Multipath Signals

Zhu,

Dong

et al. 2024

Sensors

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In this study, a passive radar system that detects flying targets is developed in order to solve the problems associated with traditional flying target detection systems (i.e., their large size, high power consumption, complex systems, and poor battlefield survivability). On the basis of target detection, the system uses the multipath signal (which is usually eliminated as an error term in navigation and positioning), enhances it by supporting information, and utilizes the multi-source characteristics of ordinary omnidirectional global navigation satellite system (GNSS) signals. The results of a validation experiment showed that the system is able to locate a passenger airplane and obtain its flight trajectory using only one GNSS receiving antenna. The system is characterized by its light weight (less than 5 kg), low power consumption, simple system, good portability, low cost, and 24/7 and all-weather work. It can be installed in large quantities and has good prospects for development.

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“…Global Navigation Satellite System reflectometry (GNSS-R) is a new microwave remote sensing technology that is based on satellite navigation signals [8,9]. It can use remote geophysical parameters by receiving and processing GNSS signals reflected from the Earth's surface [10].…”

Section: Introductionmentioning

confidence: 99%

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

Wei

et al. 2023

Remote Sensing

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Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon resulted in severe losses to local people and property. During flood events, there is an urgent need for timely and detailed flood inundation mapping for areas that have been severely affected. However, current satellite missions cannot provide sufficient information at a high enough spatio-temporal resolution for flooding applications. In contrast, spaceborne Global Navigation Satellite System reflectometry technology can be used to observe the Earth’s surface at a high spatio-temporal resolution without being affected by clouds or surface vegetation, providing a feasible scheme for flood disaster research. In this study, Cyclone Global Navigation Satellite System (CYGNSS) L1 science data were processed to obtain the change in the delay-Doppler map and surface reflectivity (SR) during the flood event. Then, a flood inundation map of the extreme precipitation was drawn using the threshold method based on the CYGNSS SR. Additionally, the flooded areas that were calculated based on the soil moisture from the Soil Moisture Active Passive (SMAP) data were used as a reference. Furthermore, the daily Dry Wet Abrupt Alternation Index (DWAAI) was used to identify the occurrence of the flood events. The results showed good agreement between the flood inundation that was derived from the CYGNSS SR and SMAP soil moisture. Moreover, compared with the SMAP results, the CYGNSS SR can provide the daily flood inundation with higher accuracy due to its high spatio-temporal resolution. Furthermore, the DWAAI can identify the transformation from droughts to floods in a relatively short period. Consequently, the distributions of and variations in flood inundation under extreme weather conditions can be identified on a daily scale with good accuracy using the CYGNSS data.

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Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Cited by 14 publications

References 53 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

Flying Target Detection Technology Based on GNSS Multipath Signals

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

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