A Physical Model for GPS Multipath Caused by Land Reflections: Toward Bare Soil Moisture Retrievals

Zavorotny, Valery U.; Larson, Kristine M.; Braun, John; Small, E. E.; Gutmann, E. D.; Bilich, Andria

doi:10.1109/jstars.2009.2033608

Cited by 172 publications

(114 citation statements)

References 17 publications

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“…In this section we use the forward model of Nievinski and Larson (2014b), that has been extended from that of Zavorotny et al (2010), to obtain GPS multipath as observed by a particular antenna. The goal is to compare the theoretical performance of L5 versus L2 and L1 in deriving estimates of soil moisture and snow depth under different conditions.…”

Section: Snr Multipath Modelmentioning

confidence: 99%

Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications

Tabibi

Geremia‐Nievinski

Dam

et al. 2015

Advances in Space Research

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Global Positioning System (GPS) signal-to-noise ratio (SNR) measurements can be employed to retrieve environmental variables in multipath reception conditions, whereby direct or line-of-sight transmission is received simultaneously with coherent reflections thereof. Previous GPS SNR multipath studies of soil moisture and snow depth have focused on the legacy GPS L1 and L2 bands. In the present paper, short-delay, near-grazing incidence multipath from the L5-band GPS SNR is assessed for its value in detecting soil moisture and snow depth. The L5 signal will become more important in the future because of compatibility and interoperability among the different global satellite navigation systems. The L5 results are compared with L2C estimates to determine whether the L2C-L5 differences (given their differing power budgets and their modulation properties) are significant. To address these questions, measurements and simulations were employed. A physically-based multipath simulator was enhanced to investigate the differences between parameter retrievals for the L2C and the L5 GPS signals. Parameter retrievals from synthetic observations for different scenarios were compared. Comparisons included varying reflector height, surface material, and surface roughness. Measurements from two GPS stations in Colorado, USA, were used to retrieve soil moisture and snow depth conditions. Over a 153-day period that encompassed the catastrophic 2013 Colorado flooding event, L2-derived volumetric soil moisture had an RMS difference of 0.042 cm 3 /cm 3 while the L5 RMS difference was 0.034 cm 3 / cm 3 with respect to in-situ data (values of volumetric soil moisture range between 0.04 and 0.34 cm 3 /cm 3 ). In a separate 483-day campaign, L5-derived snow depth estimates were compared to L2C-derived values and found strongly correlated, deviating from a one-toone relationship by only 0.00001 ± 0.0064 cm/cm. These results indicate the absence of any detectable biases in L5 as compared to L2C for retrieving soil moisture and snow depth from GPS SNR multipath observations.

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Section: Snr Multipath Modelmentioning

confidence: 99%

Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications

Tabibi

Geremia‐Nievinski

Dam

et al. 2015

Advances in Space Research

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“…The frozen soil characteristics of Fields 1 and 5 are shown in Table 3. A more realistic model of the frozen soil [30] should be implemented for future studies. Figure 5 shows the measurement and theoretical results for the perfect reflector and the two frozen soils.…”

Section: Without a Ground Reflectormentioning

confidence: 99%

Inferring Snow Water Equivalent for a Snow-Covered Ground Reflector Using GPS Multipath Signals

Jacobson

2010

Remote Sensing

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A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals.

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“…where λ is the wavelength, θ is satellite elevation angle and H is antenna effective height [20], which determines the frequency of SNR variation. In general, Equation (4) is a commonly-used SNR model assuming only specular reflection.…”

Section: Receiver Snr Modeling and Estimation Under Noisementioning

confidence: 99%

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

et al. 2018

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Abstract:The Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR) technique on soil moisture remote sensing was studied. A semi-empirical Signal-to-Noise Ratio (SNR) model was proposed as a curve-fitting model for SNR data routinely collected by a GNSS receiver. This model aims at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This is achieved empirically through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al., T. Yang et al.), this model can improve the Quality of Fit (QoF) with little prior knowledge needed and can allow soil permittivity to be estimated from the reconstructed signals. In developing this model, we showed how noise affects the receiver SNR estimation and thus the model performance through simulations under the bare soil assumption. Results showed that the reconstructed signals with a grazing angle of 5 • -15 • were better for soil moisture retrieval. The QoF was improved by around 45%, which resulted in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected. Experimental data collected at Lamasquère, France, were also used to validate the proposed model. The results were compared with the simulation and previous works. It was found that the model could ensure good fitting quality even in the case of irregular SNR variation. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements. A deeper insight into the Larson model and the proposed model was given at this stage, which formed a possible explanation of this fact. Furthermore, frequency and phase information extracted using this model were also studied for their capability to monitor soil moisture variation. Finally, phenomena such as retrieval ambiguity and error sensitivity were stated and discussed.

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A Physical Model for GPS Multipath Caused by Land Reflections: Toward Bare Soil Moisture Retrievals

Cited by 172 publications

References 17 publications

Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications

Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications

Inferring Snow Water Equivalent for a Snow-Covered Ground Reflector Using GPS Multipath Signals

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

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