Wind speed retrieval using GNSS-R technique with geographic partitioning

Li, Zheng; Guo, Fei; Chen, Fade; Zhang, Zhe; Zhang, Xiaohong

doi:10.1186/s43020-022-00093-z

Cited by 18 publications

(3 citation statements)

References 32 publications

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“…Wind speed retrieval based on the observables and GMF is the most mature method up to now as it is easy to implement. For airborne cases, there are a large number of observables based on the shape and power of the DDM (Li et al, 2023;Rodriguez-Alvarez et al, 2013). However, for spaceborne missions, limited by the spatial resolution, usually only a small part of the DDM can be used for retrieval, and thus the DDM power magnitude is more sensitive to wind speed rather than the shape.…”

Section: Retrieval Algorithmmentioning

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: Retrieval Algorithmmentioning

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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“…Drawing upon this model, the application of GNSS-R technology has commenced for monitoring environmental characteristics within the realms of both oceans and land [15]. This encompasses the measurement of ocean wind speed [16], [17], SWH [18], [19], and soil moisture [20], [21], showcasing exceptional performance. Sea surface roughness directly affects GNSS-R observations.…”

Section: Introductionmentioning

confidence: 99%

Correction of the SMAP Sea Surface Brightness Temperature and Retrieval of Sea Surface Salinity Incorporating CYGNSS Observables

Li,

Guo,

Zhang

et al. 2024

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

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The correction of sea surface brightness temperature is crucial for improving the accuracy of sea surface salinity (SSS) retrieval by L-band microwave radiometer. However, the traditional method of correcting brightness temperature using only wind speed and significant wave height (SWH) is inadequate, as sea surface roughness is affected by multiple factors. The Global Navigation Satellite System Reflectometer (GNSS-R) observables, which directly respond to sea surface roughness, have been preliminarily validated in groundbased experiments for their potential to correct sea surface brightness temperature. Compared to ground-based GNSS-R, spaceborne GNSS-R has a wider coverage and can better support the brightness temperature correction of spaceborne L-band microwave radiometers. This paper has preliminarily verified the correlation between Cyclone GNSS (CYGNSS) observables and brightness temperature variations, and found that the incidence angle of the observable needs to be taken into account when retrieving SSS jointly with Soil Moisture Active and Passive (SMAP) and CYGNSS. A multilayer perceptron (MLP) model was established to assess the SSS retrieval performance of SMAP combined with different parameters. The results show that the retrieval performance based on the MLP model is better than that based on the geophysical model function (GMF) model. Compared to joint wind speed and SWH, joint CYGNSS observables performs better in retrieving SSS. The root mean square error (RMSE) of retrieval salinity decreased from 0.58 to 0.46 psu, and the correlation coefficient (R) increased from 0.83 to 0.90. This provides reference for future joint retrieval of SSS using Lband microwave radiometers and spaceborne GNSS-R.

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“…It operates under all weather conditions, offering advantages such as low costs and extensive coverage. GNSS-R has found wide applications in the studies related to ocean surface roughness and wind monitoring (Garrison et al, 2002;Li & Huang, 2014;Li et al, 2023;Yan et al, 2017), snow depth estimation (Jin & Najibi, 2014;Jin et al, 2016;Larson et al, 2009), and soil moisture retrieval (Camps et al, 2016;Katzberg et al, 2006;.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of sea ice thickness using FY-3E/GNOS-II data

Xie,

Yan

2024

Satell Navig

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Sea ice, a significant component in polar regions, plays a crucial role in climate change through its varying conditions. In Global Navigation Satellite System-Reflectometry (GNSS-R) studies, the observed surface reflectivity Γ serves as a tool to examine the physical characteristics of sea ice covers. This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model. However, it is important to note that when Sea Ice Thickness (SIT) becomes thicker, the accuracy of SIT retrieval via this two-layer model begins to decline. In this paper, we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E (FY-3E). Soil Moisture Ocean Salinity (SMOS) data are treated as the reference. The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval. The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m. By comparing the retrieved SITs against reference values, the three-layer model achieved a Root Mean Square Error (RMSE) of 0.149 m and Correlation Coefficient (r) of 0.830, while the two-layer model reported the RMSE of 0.162 m and r value of 0.789. A scheme incorporating both models yielded superior results than either individual model, with the RMSE of 0.137 m and r reaching up to 0.852. This study is the first application of FY-3E for GNSS-R SIT retrieval, combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m. This provides a good reference for the future studies on GNSS-R SIT retrieval.

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Wind speed retrieval using GNSS-R technique with geographic partitioning

Cited by 18 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

Correction of the SMAP Sea Surface Brightness Temperature and Retrieval of Sea Surface Salinity Incorporating CYGNSS Observables

Retrieval of sea ice thickness using FY-3E/GNOS-II data

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