Physical Modeling and Compensation for Systematic Negative Errors in GNSS-R Snow Depth Retrieval

Zhang, Zhe; Guo, Fei; Ban, Wei; Li, Zheng; Liu, Hang

doi:10.1109/tgrs.2023.3260808

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…GNSS-IR has been developed and used in estimating snow depths (Larson et al, 2009;Pinat et al, 2021;Yang et al, 2023aYang et al, , 2023bZhang et al, 2023aZhang et al, , 2023bZhang et al, , 2023c) (Fig. 12), e.g., sensing snow depth variation using the GNSS SNR measurements and phase-based multipath (Qian & Jin, 2016).…”

Section: Snow and Cryosphere Sensingmentioning

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: Snow and Cryosphere Sensingmentioning

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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show abstract

“…This technique was originally used for water level inversion, and accurate information on water level can be obtained by interferometry of GNSS signals [4][5][6]. Later, GNSS-IR technology was gradually extended to other fields, such as SM [7], snow depth [8][9][10], vegetation index [11,12], sea ice thickness [13,14] and ground height [15]. The GNSS-IR technology of SM retrieval research has achieved some results.…”

Section: Introductionmentioning

confidence: 99%

Research on GNSS-IR soil moisture retrieval based on random forest algorithm

Zheng,

Chai,

Wang

et al. 2024

Meas. Sci. Technol.

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Soil moisture (SM) retrieval is of great significance in climate, agriculture, ecology, hydrology, and natural disaster monitoring, and it is one of the essential hydrometeorological parameters studied in the world at present. With the continuous development of the GNSS, a technique called GNSS-IR became widely used in ground SM inversion. Therefore, based on the frequency, amplitude and phase of signal-to-noise ratio residuals (δSNR), this study takes P037 and P043 stations set by UNAVCO in the United States as examples and develops the research of SM inversion from Random Forest Regression (RFR) prediction. The experimental results show that the retrieval accuracy of SM under different practical schemes can be in descending order: L1 + L2 dual frequency combination > L2 single frequency > L1 single frequency. It is confirmed that the experimental scheme based on the L1+L2 dual-frequency combination is beneficial to the inversion of SM. In the L1+L2 dual-frequency combination, the prediction set accuracy of the P037 station is as follows: R is 0.796, RMSE is 0.032 cm3cm-3, ME is 0.002 cm3cm-3. The prediction accuracy of the P043 station is as follows: R is 0.858, RMSE is 0.039 cm3cm-3, ME is -0.009 cm3cm-3. Among them, the RMSE of the L1+L2 dual-frequency combination of the two stations has an improvement effect of 13%-37% compared with their single-frequency, which has a noticeable improvement effect. The difference between the SM retrieved by GNSS-IR and the reference value of PBO-H2O is concentrated around 0, further showing the accuracy of SM retrieved by GNSS-IR technology. To sum up, this study considers that SM retrieval based on the RFR model has good reliability and accuracy, which makes GNSS-IR technology an efficient means for SM retrieval. With the continuous improvement of the GNSS system and technology, the application of GNSS-IR technology in SM will become broader.

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GNSS-Based SAR Imaging for Object Detection Based on Iterative Range Compressions

Zheng,

Zhang,

Zhu

et al. 2024

IEEE Sensors J.

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Physical Modeling and Compensation for Systematic Negative Errors in GNSS-R Snow Depth Retrieval

Cited by 3 publications

References 43 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

Research on GNSS-IR soil moisture retrieval based on random forest algorithm

GNSS-Based SAR Imaging for Object Detection Based on Iterative Range Compressions

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