Soil Moisture Estimation by GNSS Multipath Signal

Global navigation satellite system (GNSS) multipath signals received by a geodetic-quality GNSS receiver can be used to estimate the water content of soil around the antenna. The direct signals from satellite to GNSS antenna are the most valuable signals in geodetic measurement, such as positioning, navigation, GNSS control network, deformation monitoring, and so on. However, the GNSS antenna also captures the reflected signals from the ground, which contain information of surrounding environment, so that useful information about the reflection surface can be inferred by analyzing the reflected signal. This technique is termed as GNSS-interferometric reflectometry. The signal-to-noise ratio (SNR) data recorded by a receiver contains SNR component of reflected signals, which is related to the soil moisture of the ground. The changes of soil moisture content will cause the change of soil permittivity and reflectivity which are the key factors that make further change of the SNR of reflected signals. We used the measured data to evaluate the correlation between amplitude of multipath induced SNR time series and real soil moisture. An improved soil moisture estimation algorithm based on multipath induced SNR amplitude data is proposed in this paper. The performance of the proposed soil moisture estimation method is evaluated using the 15-month data recorded by PBO H2O GNSS station and a 14-day experiment in Wuhan, China. The experimental results show that the estimated soil moisture has a strong correlation with the real soil moisture and the estimation accuracy in terms of root-mean-square error (RMSE) is 0.0345 cm3cm−3 and 0.0339 cm3cm−3, respectively. Meanwhile, the application scope of the method is given.

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Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

Remote Sensing ◽

10.3390/rs10081245 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1245 ◽

Cited By ~ 15

Author(s):

Mehrez Zribi ◽

Erwan Motte ◽

Nicolas Baghdadi ◽

Frédéric Baup ◽

Sylvia Dayau ◽

...

Keyword(s):

Soil Moisture ◽

Signal To Noise Ratio ◽

Soil Surface ◽

Satellite System ◽

Area Index ◽

Vegetation Height ◽

Study Sites ◽

Global Navigation Satellite ◽

Vegetation Characteristic ◽

Agricultural Areas

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

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Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter

Remote Sensing ◽

10.3390/rs12213495 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3495

Author(s):

HongCheng Zeng ◽

Jie Chen ◽

PengBo Wang ◽

Wei Liu ◽

XinKai Zhou ◽

...

Keyword(s):

Target Detection ◽

Sequential Monte Carlo ◽

Signal To Noise Ratio ◽

Power Level ◽

Estimation Method ◽

Satellite System ◽

Moving Target ◽

Passive Radar ◽

Moving Target Detection ◽

Global Navigation Satellite

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately.

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Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data

Remote Sensing ◽

10.3390/rs13040570 ◽

2021 ◽

Vol 13 (4) ◽

pp. 570

Author(s):

Zhounan Dong ◽

Shuanggen Jin

Keyword(s):

Soil Moisture ◽

Global Navigation Satellite System ◽

Land Surface ◽

Satellite System ◽

Navigation Satellite ◽

Global Navigation ◽

Incoherent Component ◽

Global Navigation Satellite ◽

The Impact ◽

Moisture Estimation

With the development of spaceborne global navigation satellite system-reflectometry (GNSS-R), it can be used for terrestrial applications as a promising remote sensing tool, such as soil moisture (SM) retrieval. The reflected L-band GNSS signal from the land surface can simultaneously generate coherent and incoherent scattering, depending on surface roughness. However, the contribution of the incoherent component was directly ignored in previous GNSS-R land soil moisture content retrieval due to the hypothesis of its relatively small proportion. In this paper, a detection method is proposed to distinguish the coherence of land GNSS-R delay-Doppler map (DDM) from the cyclone global navigation satellite system (CYGNSS) mission in terms of DDM power-spreading features, which are characterized by different classification estimators. The results show that the trailing edge slope of normalized integrated time-delay waveform presents a better performance to recognize coherent and incoherent dominated observations, indicating that 89.6% of CYGNSS land observations are dominated by the coherent component. Furthermore, the impact of the land GNSS-Reflected DDM coherence on soil moisture retrieval is evaluated from 19-month CYGNSS data. The experiment results show that the influence of incoherent component and incoherent observations is marginal for CYGNSS soil moisture retrieval, and the RMSE of GNSS-R derived soil moisture reaches 0.04 cm3/cm3.

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Estimation of fractional cycle bias for GPS/BDS-2/Galileo based on international GNSS monitoring and assessment system observations using the uncombined PPP model

Satellite Navigation ◽

10.1186/s43020-021-00039-x ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Jin Wang ◽

Qin Zhang ◽

Guanwen Huang

Keyword(s):

Estimation Method ◽

Satellite Orbit ◽

Satellite System ◽

Assessment System ◽

Navigation Satellite ◽

Dual Frequency ◽

Fractional Cycle Bias ◽

The Stability ◽

High Consistency ◽

Global Navigation Satellite

AbstractThe Fractional Cycle Bias (FCB) product is crucial for the Ambiguity Resolution (AR) in Precise Point Positioning (PPP). Different from the traditional method using the ionospheric-free ambiguity which is formed by the Wide Lane (WL) and Narrow Lane (NL) combinations, the uncombined PPP model is flexible and effective to generate the FCB products. This study presents the FCB estimation method based on the multi-Global Navigation Satellite System (GNSS) precise satellite orbit and clock corrections from the international GNSS Monitoring and Assessment System (iGMAS) observations using the uncombined PPP model. The dual-frequency raw ambiguities are combined by the integer coefficients (4,− 3) and (1,− 1) to directly estimate the FCBs. The details of FCB estimation are described with the Global Positioning System (GPS), BeiDou-2 Navigation Satellite System (BDS-2) and Galileo Navigation Satellite System (Galileo). For the estimated FCBs, the Root Mean Squares (RMSs) of the posterior residuals are smaller than 0.1 cycles, which indicates a high consistency for the float ambiguities. The stability of the WL FCBs series is better than 0.02 cycles for the three GNSS systems, while the STandard Deviation (STD) of the NL FCBs for BDS-2 is larger than 0.139 cycles. The combined FCBs have better stability than the raw series. With the multi-GNSS FCB products, the PPP AR for GPS/BDS-2/Galileo is demonstrated using the raw observations. For hourly static positioning results, the performance of the PPP AR with the three-system observations is improved by 42.6%, but only 13.1% for kinematic positioning results. The results indicate that precise and reliable positioning can be achieved with the PPP AR of GPS/BDS-2/Galileo, supported by multi-GNSS satellite orbit, clock, and FCB products based on iGMAS.

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Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites

Remote Sensing ◽

10.3390/rs13112032 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2032

Author(s):

Junchan Lee ◽

Sunil Bisnath ◽

Regina S.K. Lee ◽

Narin Gavili Kilane

Keyword(s):

Dielectric Constant ◽

Soil Moisture ◽

Global Navigation Satellite System ◽

Measurement Data ◽

Satellite System ◽

Computation Method ◽

Navigation Satellite ◽

Local Soil ◽

Global Navigation ◽

Global Navigation Satellite

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.

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Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece

Applied Sciences ◽

10.3390/app10186445 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6445 ◽

Cited By ~ 1

Author(s):

Theodoros Gatsios ◽

Francesca Cigna ◽

Deodato Tapete ◽

Vassilis Sakkas ◽

Kyriaki Pavlou ◽

...

Keyword(s):

Land Surface ◽

Surface Deformation ◽

Ground Deformation ◽

Local Population ◽

Seasonal Fluctuation ◽

Satellite System ◽

Deformation Monitoring ◽

Persistent Scatterers ◽

Geothermal Activity ◽

Global Navigation Satellite

The Methana volcano in Greece belongs to the western part of the Hellenic Volcanic Arc, where the African and Eurasian tectonic plates converge at a rate of approximately 3 cm/year. While volcanic hazard in Methana is considered low, the neotectonic basin constituting the Saronic Gulf area is seismically active and there is evidence of local geothermal activity. Monitoring is therefore crucial to characterize any activity at the volcano that could impact the local population. This study aims to detect surface deformation in the whole Methana peninsula based on a long stack of 99 Sentinel-1 C-band Synthetic Aperture Radar (SAR) images in interferometric wide swath mode acquired in March 2015–August 2019. A Multi-Temporal Interferometric SAR (MT-InSAR) processing approach is exploited using the Interferometric Point Target Analysis (IPTA) method, involving the extraction of a network of targets including both Persistent Scatterers (PS) and Distributed Scatterers (DS) to augment the monitoring capability across the varied land cover of the peninsula. Satellite geodetic data from 2006–2019 Global Positioning System (GPS) benchmark surveying are used to calibrate and validate the MT-InSAR results. Deformation monitoring records from permanent Global Navigation Satellite System (GNSS) stations, two of which were installed within the peninsula in 2004 (METH) and 2019 (MTNA), are also exploited for interpretation of the regional deformation scenario. Geological, topographic, and 2006–2019 seismological data enable better understanding of the ground deformation observed. Line-of-sight displacement velocities of the over 4700 PS and 6200 DS within the peninsula are from −18.1 to +7.5 mm/year. The MT-InSAR data suggest a complex displacement pattern across the volcano edifice, including local-scale land surface processes. In Methana town, ground stability is found on volcanoclasts and limestone for the majority of the urban area footprint while some deformation is observed in the suburban zones. At the Mavri Petra andesitic dome, time series of the exceptionally dense PS/DS network across blocks of agglomerate and cinder reveal seasonal fluctuation (5 mm amplitude) overlapping the long-term stable trend. Given the steepness of the slopes along the eastern flank of the volcano, displacement patterns may indicate mass movements. The GNSS, seismological and MT-InSAR analyses lead to a first account of deformation processes and their temporal evolution over the last years for Methana, thus providing initial information to feed into the volcano baseline hazard assessment and monitoring system.

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High-rate altimetry in SNR-based GNSS-R: Proof-of-concept of a synthetic vertical array

10.36227/techrxiv.14186930 ◽

2021 ◽

Author(s):

Mauricio Kenji Yamawaki ◽

Felipe Geremia-Nievinski ◽

João Francisco Monico

Keyword(s):

Sea Level ◽

Signal To Noise Ratio ◽

Satellite System ◽

High Rate ◽

Proof Of Concept ◽

Radio Waves ◽

Vertical Array ◽

Remote Sensing Technique ◽

Single Antenna ◽

Global Navigation Satellite

Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a promising remote sensing technique for coastal sea level monitoring. The GNSS-R based on signal-to-noise ratio (SNR) observations employs a single antenna and a conventional receiver. It performs best for low elevation satellites, where direct and reflected radio waves are very similar in polarization and direction of arrival. One of the disadvantages of SNR-based GNSS-R for sea level altimetry is its low temporal resolution, which is of the order of one hour for each independent satellite pass. Here we present a proof-of-concept based on a synthetic vertical array. It exploits the mechanical movement of a single antenna at high rate (about 1 Hz). SNR observations can then be fit to a known modulation, of the order of the antenna sweeping rate. We demonstrate that centimetric altimetry precision can be achieved in a 5-minute session. [©2021 IEEE]

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Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

10.5194/hess-2017-597 ◽

2017 ◽

Author(s):

Sibo Zhang ◽

Jean-Christophe Calvet ◽

José Darrozes ◽

Nicolas Roussel ◽

Frédéric Frappart ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Ground Surface ◽

Satellite System ◽

Surface Model ◽

Global Navigation Satellite ◽

Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

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Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

10.36227/techrxiv.16880926.v1 ◽

2021 ◽

Author(s):

Dengqing Tang ◽

Lincheng Shen ◽

Xiaojiao Xiang ◽

Han Zhou ◽

Tianjiang Hu

Keyword(s):

Real Time ◽

Pose Estimation ◽

Stereo Vision ◽

Vision System ◽

Estimation Method ◽

Satellite System ◽

Training Dataset ◽

Estimation Accuracy ◽

The Impact ◽

Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

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