Soil Moisture Estimation Based on GNSS-R Using L5 Signals From a Quasi-Zenith Satellite System

Wang, Nazi; Gao, Fan; Kong, Yahui; Xu, Tianhe; Lili, Jing; Yang, Lei; He, Yunqiao; Yang, Wentao; Meng, Xinyue; Ning, Baojiao

doi:10.1109/lgrs.2022.3176463

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…For cGNSS-R, we have designed a GNSS-R SDR based on Matlab for sea surface altimetry and soil moisture monitoring. This SDR can process L1, L2, L5, B1, and B2 band signals, and several ship-borne [51], coastal [52,116,117], and land-borne [118] experiments have verified its satisfactory performance. Because of a lot of cross-correlation in GNSS-R data processing, this SDR can only perform post-processing.…”

Section: Gnss-r Altimetry With Two Antennasmentioning

confidence: 84%

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

Xu,

Wang,

et al. 2024

Remote Sensing

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For the past 20 years, Global Navigation Satellite System reflectometry (GNSS-R) technology has successfully shown its potential for remote sensing of the Earth’s surface, including ocean and land surfaces. It is a multistatic radar that uses the GNSS signals reflected from the Earth’s surface to extract land and ocean characteristics. Because of its numerous advantages such as low cost, multiple signal sources, and all-day/weather and high-spatiotemporal-resolution observations, this new technology has attracted the attention of many researchers. One of its most promising applications is GNSS-R ocean altimetry, which can complement existing techniques such as tide gauging and radar satellite altimetry. Since this technology for ocean altimetry was first proposed in 1993, increasing progress has been made including diverse methods for processing reflected signals (such as GNSS interferometric reflectometry, conventional GNSS-R, and interferometric GNSS-R), different instruments (such as an RHCP antenna with one geodetic receiver, a linearly polarized antenna, and a system of simultaneously used RHCP and LHCP antennas with a dedicated receiver), and different platform applications (such as ground-based, air-borne, or space-borne). The development of multi-mode and multi-frequency GNSS, especially for constructing the Chinese BeiDou Global Navigation Satellite System (BDS-3), has enabled more free signals to be used to further promote GNSS-R applications. The GNSS has evolved from its initial use of GPS L1 and L2 signals to include other GNSS bands and multi-GNSS signals. Using more advanced, multi-frequency, and multi-mode signals will bring new opportunities to develop GNSS-R technology. In this paper, studies of GNSS-R altimetry are reviewed from four perspectives: (1) classifications according to different data processing methods, (2) different platforms, (3) development of different receivers, and (4) our work. We overview the current status of GNSS-R altimetry and describe its fundamental principles, experiments, recent applications to ocean altimetry, and future directions.

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Section: Gnss-r Altimetry With Two Antennasmentioning

confidence: 84%

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

Xu,

Wang,

et al. 2024

Remote Sensing

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“…Soil moisture accuracy can be affected by the crosstalk between direct and reflected signals, surface roughness, and vegetation [36]. Assuming that the hourly change of soil moisture is not evident, hourly averaging of reflectivity data will weaken the influences of some environmental factors.…”

Section: Discussionmentioning

confidence: 99%

In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM

et al. 2023

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Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval model based on LSTM (long–short term memory) is proposed to improve accuracy and robustness as to the impacts of vegetation cover and soil surface roughness. The Oceanpal GNSS-R data obtained from the experimental campaign at the Valencia Anchor Station are used as the main input data, and the TB (brightness temperature) and TR (soil roughness and vegetation integrated attenuation coefficient) outputs of the ELBARA-II radiometer are used as auxiliary input data, while field measurements with a Delta-T ML2x ThetaProbe soil moisture sensor were used for reference and validation. The results show that the LSTM model can be used to retrieve soil moisture, and that it performs better in the data fusion scenario with GNSS-R and radiometer. The STD of the multi-satellite fusion model is 0.013. Among the single-satellite models, PRN13, 20, and 32 gave the best retrieval results with STD = 0.011, 0.012, and 0.007, respectively.

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“…For over two decades, remote sensing of soil moisture has been further enabled by the development of methods that leverage reflected signals from Global Navigation Satellite Systems (GNSS), which are affected by the soil moisture content of the reflecting surface. Signals collected from tower-mounted antenna have been demonstrated to accurately measure the soil moisture content using various interferometeric and powerbased techniques [4][5][6][7]. Measurements have also been made from aircraft, allowing for the testing of novel instrumentation and techniques over wider areas [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R

et al. 2022

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GNSS reflection measurements in the form of delay-Doppler maps (DDM) can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under various conditions. The application of deep-learning-based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for the incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing the groundwork for a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data were aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2017–2019 which is generally comparable to existing global soil moisture products, and shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well. Comparisons with in-situ measurements demonstrate the correlation between the network predictions and ground truth with high temporal resolution.

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Soil Moisture Estimation Based on GNSS-R Using L5 Signals From a Quasi-Zenith Satellite System

Cited by 7 publications

References 28 publications

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM

A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R

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