Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data

Zhang, Yun; Yan, Ziyu; Yang, Shuhu; Meng, Wanting; Gu, Siqi; Qin, Jin; Han, Yanling; Hong, Zhonghua

doi:10.3390/rs14051170

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…River flow velocity is fundamental in hydrologic studies, and its detection is valuable for the reason that it links variables such as hydrologic detection, flood control engineering design, hydrologic prediction, ecological environment assessment, and hydrodynamic research (Eric et al, 2021). The potential of the GNSS-R application in river flow measurement was studied using the Chinese BDS constellation (Zhang et al, 2022a(Zhang et al, , 2022b. A river flow velocity retrieval model, based on carrier phase observations, was developed to include both openloop tracing and the carrier phase streamflow retrieval method.…”

Section: River Flowmentioning

confidence: 99%

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Jin,

Camps,

Jia

et al. 2024

Satell Navig

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The Global Navigation Satellite Systems (GNSS), including the US’s GPS, China’s BDS, the European Union’s Galileo, and Russia’s GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth’s surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring.

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Section: River Flowmentioning

confidence: 99%

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Jin,

Camps,

Jia

et al. 2024

Satell Navig

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“…Based on particle tracking velocimetry, Eltner et al [ 4 ] collected and estimated River images. The results show that the deviation of this method is between 4% and 5% [ 4 ]; Zhang et al [ 5 ] used the reflection technology of BeiDou system to inverse the river velocity and then obtained the river velocity with small error [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

“…In view of the above problems, some scholars also proposed to process the collected water flow images. For example, Gulrajani et al [ 6 ] proposed to introduce gradient punishment mechanism into the WGAN algorithm to better improve the WGAN algorithm [ 5 ]. David proposed an image processing technology based on the BEGAN algorithm to solve the contradiction between picture type and generation [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion

Wang

Chen

Wang

2022

Computational Intelligence and Neuroscience

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The necessity of predicting and estimating river velocity motivates the development of a prediction method based on GAN image enhancement and multifeature fusion. In this method, in order to improve the image quality of river velocity, GAN network is used to enhance the image, so as to improve the integrity of image data set. In order to improve the accuracy of prediction, the image is extracted and fused with multiple features, and the extracted multiple features are taken as the input of CNN, so as to improve the prediction accuracy of convolution neural network. The results show that when the velocity is 0.25 m/s, 0.50 m/s, and 0.75 m/s, the accuracy of improved method can reach 85%, 90%, and 92%, which are higher than SVM, VGG-16, and BPNET algorithms. The above results indicate that the improvement has certain positive value and practical application value.

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“…In terms of river flow detection, Warnock et al used CYGNSS IF data to define the correlation width of time delay waveform inverts the flow of the Mississippi River and finds that Associated GNSS-R Width (AGW) is proportional to the flow of the river, demonstrating the feasibility of space-borne GNSS-R detecting the flow of the river [25]. In terms of velocity detection, literature [26][27][28] all found that GNSS signals reflected by flow surface had a small Doppler deviation compared with direct signals, which was related to fluid velocity. All these indicate that GNSS-R technology has certain potential in river detection.…”

Section: ⅰ Introductionmentioning

confidence: 99%

Feasibility Study on Qinghai Lake Boundary Detection Using CYGNSS Raw IF Data

Zhang,

Yan,

Yang

et al. 2024

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

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Qinghai Lake, the largest inland saltwater lake in China, is an important water body that maintains the ecological security of the northeastern Tibetan Plateau. Global navigation satellite system reflectometry technology (GNSS-R) is rarely used in lake detection, especially in lake boundary detection under the circumstances of space-borne. This paper attempts to implement lake boundary detection using CYGNSS raw intermediate frequency (IF) data for the first time. Firstly, the second-order frequency-locked loop is used to assist the third-order phaselocked loop closed-loop tracking to successfully generate higher delay resolution and Doppler resolution (67×51) Delay Doppler Maps (DDM) than CYGNSS compressed DDM (11×17); secondly, based on the principle of scattering characteristics of different reflectors of GNSS-R, and the fusion method of signal power ratio (PR) and effective area delay distance (DLR), the 2018 Qinghai Lake boundary detection inversion was carried out in the lake area to explore the feasibility of achieving higher spatial resolution (0.7 km) and high-precision lake boundary detection with the raw IF data of CYGNSS. The inversion detection results are compared with the topographic distribution results of Google Earth and GLAD, the inversion result error is about 0.5 km, which is consistent with the theoretical spatial resolution. It proves that spaceborne GNSS•R technology and CYGNSS raw IF data have great potential in the field of lake boundary detection.

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Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data

Cited by 6 publications

References 32 publications

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion

Feasibility Study on Qinghai Lake Boundary Detection Using CYGNSS Raw IF Data

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