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Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry
Open accessAbstract
In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. ...
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Written from the paper’s full text.
TL;DR
This paper uses an end-to-end simulator validated against CYGNSS raw IF observations to characterize how spaceborne GNSS-R SNR over river crossings can retrieve river width. Under idealized isolated straight-river scenes, peak SNR predicts widths of 160–192 m with total uncertainty of roughly 2–5 m, but nearby clutter within about 500 m can make retrievals unreliable.
Key findings
- For isolated, straight rivers, the peak SNR measured at the river centerline is predictive of river width to within ±5 m; total retrieval uncertainty ranges from 2.76–5.12 m for perpendicular tracks and 3.40–4.40 m for oblique tracks (Table 8).
- The width model has the form \(w = a \cdot \mathrm{SNR_{peak}}^m + b\), with \(m\) ranging from about 0.5–0.6 and decreasing with increasing incidence angle; regression constants vary by incidence angle and approach angle (Tables 3–4).
- Precision errors are 2.4–5.1 m for perpendicular crossings and 2.9–4.4 m for the reported oblique cases; accuracy errors are 0.48–0.68 m for perpendicular and 0.03–0.04 m for oblique cases (Tables 5–7).
- A 1000 m diameter circular lake causes SNR perturbations that become significant when the lake edge is within about 500 m of the river center; large water bodies more than about 900 m from the river do not significantly impact the peak SNR related to the river crossing.
Methods
- An end-to-end simulator (E2ES) combines coherent Huygens-Kirchhoff and incoherent bistatic radar models, calibrated to CYGNSS raw IF observations and validated over the Río Santiago, Lake Ilopango, and Roosevelt River.
- Simulated CYGNSS data were generated for synthetic straight river subreaches of 160 m, 176 m, and 192 m width using three actual overpass geometries with incidence angles of 14°, 31°, and 42°, for perpendicular and 45° oblique approach angles, plus clutter scenarios with a 1000 m diameter lake at varying distances.
- Analysis regressed peak SNR against river width, propagated SNR noise for precision, computed RMS residual for accuracy, and combined both as root-sum-square total uncertainty.
Limitations
- The paper states that the limited number of simulations and idealized scenes mean the uncertainty cannot be generalized to river conditions far different from those considered; possible breakdowns may occur when \(Z_f\) becomes sufficiently large or small, and higher-order Fresnel zones, clutter, or meandering river shapes may alter the relationships.
- The paper states that the exact clutter distance threshold cannot be determined from these simulations because of nonlinear SNR behavior and the finite number of cases.
- The E2ES assumes perfectly smooth water, a constant land NBRCS of \(\sigma_0 = 1\), and a water Fresnel reflection coefficient of 1; the paper says these affect relative SNR magnitudes but not significantly the width portion used for retrieval. Validation also notes that the Marañón River water mask underrepresents the true river extent at the observation time.
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Claims the paper makes
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- Findingown data
Under idealized conditions, GNSS-R data is able to measure river widths as narrow as 160m with high accuracy.
“The results of this study indicate that under idealized conditions, GNSS-R data is able to measure river widths as narrow as 160m with high accuracy.”
Quote in the paperSubstantial claim - Findingown data
For the perpendicular SP track crossing, clutter becomes problematic at ~850m for the low incidence angles (Track 1).
“The results in Figure 7 indicate that clutter becomes problematic for the perpendicular SP track crossing at ~850m for the low incidence angles (Track 1).”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingown data
The distance at which the impact of the lake on the SNR magnitude over the river becomes significant was identified as ~500m from the center of the river to the edge of the lake.
“The distance at which the impact of the lake on the SNR magnitude over the river becomes significant was identified as ~500m from the center of the river to the edge of the lake.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingnone given
The regression constants a, b, and m vary for each incidence angle.
“The regression constants 𝑎, 𝑏, and 𝑚 vary for each incidence angle and are shown in Table 3.”
Quote in the paperNot a substantial claim - Findingreasoning
The total uncertainties range from ~3-5m.
“The total uncertainties range from ~3-5m.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingreasoning
For perpendicular crossing cases, the ellipse encompassing the FFZ along the SP track does not contain water until the SP point is ~600m, ~680m, and ~710m from the river center point for the 14 degree, 31 degree, and 42 degree incidence angles, respectively.
“These behaviors can be explained by considering the 𝑍 𝑓 for the perpendicular vs. 45-degree approach angle cases, where the ellipse encompassing the FFZ along the SP track for the perpendicular crossing cases does not contain water until the SP point is ~600m, ~680m, and ~710m from the river center point for the 14 degree, 31 degree, and 42 degree incidence angles, respectively.”
Quote in the paperSubstantial claim - Findingreasoning
The river width uncertainty ranges between 3 and 5 m for river widths of 160-190 m, or roughly 2-3%.
“The river width uncertainty ranges between 3 and 5 m for river widths of 160-190 m, or roughly 2-3%.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingreasoning
For oblique (45-degree) approach angles, the waveforms corresponding to the same incidence angles and river widths have lower amplitudes compared to perpendicular approach angles.
“Qualitatively, the waveforms corresponding to the same incidence angles and river widths have lower amplitudes as compared to the perpendicular approach angles (top row of Figure 3 vs. Figure 5).”
Quote in the paperNot a substantial claim - Findingown data
Clutter from large (on the order of 1km wide) water bodies located within 500m of a river can introduce perturbations in the peak SNR at river center.
“these results indicate that clutter from large (on the order of 1km wide) water bodies located within 500m of a river can introduce perturbations in the peak SNR at river center”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingown data
The presence of clutter (surrounding water bodies) within ~500m of the river causes perturbations in the SNR measured over the river that can render the river width retrievals unreliable.
“The presence of clutter (surrounding water bodies) within ~500m of the river causes perturbations in the SNR measured over the river that can render the river width retrievals unreliable.”
Quote in the paperSubstantial claim - Findingown data
Large water bodies located > ~900m from the river do not significantly impact the peak SNR related to the river crossing.
“while large water bodies located > ~900m from the river do not significantly impact the peak SNR related to the river crossing.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingown data
The reduction in peak SNR and expansion of the waveform width increases with increasing incidence angle.
“The reduction in peak SNR and expansion of the waveform width increases with increasing incidence angle (Figure 5, top row).”
Quote in the paperSubstantial claim - Findingown data
The SNR peak at the center of the river begins to be perturbed when the center of the lake is ~1.5 km away from the river (~1 km from the center of the river to the edge of the lake).
“It is found that the SNR peak at the center of the river begins to be perturbed when the center of the lake is ~1.5 km away from the river (~1 km from the center of the river to the edge of the lake)”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingown data
The precision is comparable for the perpendicular and oblique approach angle cases.
“A comparison of Tables 5 and 6 shows that the precision is comparable for the perpendicular and oblique approach angle cases”
Quote in the paperNot a substantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingnone given
For the isolated river cases examined, the SNR peak of each SP track coincides with the SP track center point over the river.
“For the isolated river cases examined above, the SNR peak of each SP track coincides with the SP track center point over the river”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingnone given
The Z_f fraction reaches a peak value when the specular point is located at the center of the river.
“The 𝑍 𝑓 fraction reaches a peak value when the specular point is located at the center of the river”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingown data
For the Marañón River, the water mask selected, based on the best available Landsat image, underrepresents the true extent of the river at the time of the CYGNSS raw IF data collection.
“A comparison of the observed and simulated SNR indicates that the mask that was selected, which was based on the best available Landsat image, underrepresents the true extent of the river at the time of the CYGNSS raw IF data collection.”
Quote in the paperNot a substantial claim - Findingown data
For the oblique approach angle cases, the lake becomes problematic when it is centered ~1000m from the river center point for all incidence angles and river widths (~500m from the edge of the clutter water body).
“For the oblique approach angle cases (Figure 8), the lake becomes problematic when it is centered ~1000m from the river center point for all incidence angles and river widths (~500m from the edge of the clutter water body).”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingnone given
For the isolated river cases examined, the SNR peak magnitude increases in proportion to the river width.
“and its magnitude increases in proportion to the river width.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingown data
For simplistic river scenes, the SNR over the river is predictive of the river width to within +/-5m.
“An uncertainty analysis conducted using the predictive relationship indicates that for simplistic river scenes, the SNR over the river is predictive of the river width to within +/-5m.”
Quote in the paperSubstantial claim - Findingown data
For the higher incidence angles (Tracks 2 and 3), the lake can pose a problem when located as far as ~1400m away (~900m from the edge of the clutter water body).
“For the higher incidence angles (Tracks 2 and 3) the lake can pose a problem when located as far as ~1400m away (~900m from the edge of the clutter water body).”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Findingreasoning
The parameter values in the model vary with both incidence angle and approach angle.
“However, the parameter values in the model vary with both of these conditions.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Findingown data
The SNR waveforms for isolated, straight river scenes with the SP track perpendicular to the river have a single peak value that occurs when the SP is at the river centerline.
“These cases provide the simplest scenario, and the SNR waveforms have a single peak value that occurs when the SP is at the river centerline.”
Quote in the paperSubstantial claim - Findingreasoning
The waveforms at the 45-degree approach angle are wider compared to the perpendicular approach angle cases for the same incidence angles and river widths.
“Note that the waveforms at the 45-degree approach angle are wider compared to the perpendicular approach angle cases for the same incidence angles and river widths.”
Quote in the paperNot a substantial claim - Findingreasoning
When the major axis of the FFZ ellipse is aligned at a 45-degree angle to the river centerline, a portion of the river is encompassed by the FFZ earlier in the SP track, reflected in the start of the waveform rising edge at ~800, ~1000m, and ~2000m for the 14 degree, 31 degree, and 42 degree incidence angles, respectively.
“When the major axis of the FFZ ellipse is aligned at a 45-degree angle to the river centerline, a portion of the river is encompassed by the FFZ earlier in the SP track, and this is reflected in the start of the waveform rising edge at ~800, ~1000m, and ~2000m for the 14 degree, 31 degree, and 42 degree incidence angles, respectively.”
Quote in the paperNot a substantial claim - Findingown data
The perturbation becomes significant when the lake is within ~1.0 km of the river (~500m from the center of the river to the edge of the lake).
“and the perturbation becomes significant (i.e., becomes greater than the standard deviation of the measurement noise) when the lake is within ~1.0 km of the river (~500m from the center of the river to the edge of the lake).”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- Premisecitation
Databases such as the Global Width Database for Large Rivers and the North American River Width (NARWidth) data set rely on long-term imagery records from the SRTM and Landsat missions, respectively, to represent river extents at mean flow conditions.
“Databases such as the Global Width Database for Large Rivers [18], and the North American River Width (NARWidth) data set [19] rely on long-term imagery records from the SRTM and Landsat missions, respectively, to represent river extents at mean flow conditions.”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationnone given
When nearby water bodies (clutter) are present, their scattered signals will be combined with the scattering from the river itself.
“When nearby water bodies (clutter) are present, their scattered signals will be combined with the scattering from the river itself.”
Quote in the paperSubstantial claim - Interpretationcitation
The ability of GNSS-R measurements to resolve the width of rivers during their specular point river crossings has been expanded from previous work.
“The ability of GNSS-R measurements to resolve the width of rivers during their specular point river crossings has been expanded from previous work”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Warnock, A., and Ruf, C. (2019). Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator. Remote Sens., 11. source
- Study design and conductcitation
The ability of the E2ES to properly simulate the behavior of CYGNSS observations in mixed coherent+incoherent conditions was demonstrated in [23] by a comparison between observed and simulated measurements of a CYGNSS track that crossed directly over a large lake (Lake Ilopango in El Salvador).
“The ability of the E2ES to properly simulate the behavior of CYGNSS observations in mixed coherent+incoherent conditions was demonstrated in [23] by a comparison between observed and simulated measurements of a CYGNSS track that crossed directly over a large lake (Lake Ilopango in El Salvador).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Carreno-Luengo, H., Warnock, A., and Ruf, C.S. (2022, January 17–22). The Cygnss Coherent End-to-End Simulator: Development and Results. Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia. source
- Study design and conductreasoning
The exact distance of the lake from the river SP crossing where the SNR perturbation becomes significant cannot be determined by these simulations.
“the exact distance of the lake from the river SP crossing where the SNR perturbation becomes significant cannot be determined by these simulations”
Quote in the paperNot a substantial claim - Premisenone given
The theoretical basis for the GNSS-R SNR sensitivity to river width derives from the vastly differing properties of the scattering received by a GNSS-R receiver over smooth (coherent scattering) vs. rough (incoherent scattering) surfaces.
“The theoretical basis for the GNSS-R SNR sensitivity to river width derives from the vastly differing properties of the scattering received by a GNSS-R receiver over smooth (coherent scattering) vs. rough (incoherent scattering) surfaces.”
Quote in the paperSubstantial claim - Interpretationreasoning
The decreased strength of the scattered signal itself, due to the fraction of the first Fresnel zone that contains water being < 1, contains useful information about river width because it scales linearly with the Z_f.
“We find that the decreased strength of the scattered signal itself, due to the fraction of the first Fresnel zone that contains water being < 1, contains useful information about river width because it scales linearly with the 𝑍 𝑓 .”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Interpretationreasoning
The high precision is a result of the very high SNR measurements that typically result from coherent scattering surface such as rivers and lakes.
“The high precision is a result of the very high SNR measurements that typically result from coherent scattering surface such as rivers and lakes.”
Quote in the paperSubstantial claim - Interpretationreasoning
For incidence angle variations, the parameters vary because the size of the first Fresnel zone also varies with incidence angle.
“For the case of incidence angle variations, the parameters vary because the size of the first Fresnel zone also varies with incidence angle.”
Quote in the paperSubstantial claim - Interpretationreasoning
When clutter is close enough to the river, scattering from it will begin to perturb the SNR peak and cause the predictive relationship described by Equation (4) to break down.
“it can be expected that when the clutter is close enough to the river, scattering from it will begin to perturb the SNR peak and cause the predictive relationship described by Equation (4) to break down.”
Quote in the paperSubstantial claim - Premisecitation
The SWOT mission's KaRIn instrument will provide global measurements of rivers that are at least 100 meters wide.
“The Surface Water and Ocean Topography (SWOT) mission, launched by NASA in December 2022, aims to address the river width data scarcity with its innovative remote sensing instrument, the Kaband Radar Interferometer (KaRIn), that will provide global measurements of rivers that are at least 100 meters wide [20].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Biancamaria, 2016, The SWOT Mission and Its Capabilities for Land Hydrology, Surv. Geophys. source
- Study design and conductcitation
The standard deviation of the noise added to simulated SNR is estimated using a SP track over Lake Ilopango that was used for an E2ES validation study in [23].
“The standard deviation of the noise is estimated using a SP track over Lake Ilopango that was used for an E2ES validation study in [23].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Carreno-Luengo, H., Warnock, A., and Ruf, C.S. (2022, January 17–22). The Cygnss Coherent End-to-End Simulator: Development and Results. Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia. source
- Interpretationown data
The clutter result is consistent with the size of the first Fresnel zone.
“it is consistent with the size of the first Fresnel zone”
Quote in the paperSubstantial claim - Study design and conductown data
Because of the noise in the data and the influence from nearby water bodies, the approach of using 10th percentile and 99th percentile/maximum to calculate representative values for observed data is more accurate than taking averages or maxima/minima over the time series.
“Because of the noise in the data and the influence from nearby water bodies, we have found this approach to be more accurate than taking averages or maxima/minima over the time series to calculate the representative values for the observed data.”
Quote in the paperSubstantial claim - Interpretationreasoning
For approach angle variations, the parameters vary because the alignment of the major and minor axes of the first Fresnel zone relative to the river vary, thereby affecting the fraction that is filled by water for a given river width.
“For the case of the approach angle variations, the parameters vary because the alignment of the major and minor axes of the first Fresnel zone relative to the river vary, thereby affecting the fraction that is filled by water for a given river width.”
Quote in the paperSubstantial claim - Premisecitation
Currently, river width data is typically only available for large-scale regions using long-term records of satellite imagery.
“Currently, river width data is typically only available for large-scale regions using long-term records of satellite imagery.”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
The significant increase in signal strength over smooth surfaces vs. rough land is the reason that GNSS-R data is highly sensitive to the presence and size of inland water bodies.
“The significant increase in signal strength over smooth surfaces vs. rough land is the reason that GNSS-R data is highly sensitive to the presence and size of inland water bodies.”
Quote in the paperSubstantial claim- Rests on an assumption: The scattering properties over smooth and rough surfaces are very different, with forward scattering from smooth surfaces typically orders of magnitude stronger than that from rough surfaces.
- Interpretationown data
This finding is consistent with the distance where the lake would begin to enter the FFZ for a SP over the river.
“This finding is consistent with the distance where the lake would begin to enter the FFZ for a SP over the river.”
Quote in the paperNot a substantial claim - Study design and conductreasoning
The uncertainty in river width predicted from GNSS-R measurements calculated in this work cannot be generalized to river conditions far different from those considered here.
“the uncertainty in river width predicted from GNSS-R measurements that is calculated in this work cannot be generalized to river conditions far different from those considered here.”
Quote in the paperSubstantial claim - Interpretationown data
A robust modeling approach has been determined that allows river width to be estimated with ~5 m uncertainty when clutter does not exist within ~500m of the river.
“In summary, a robust modeling approach has been determined that allows river width to be estimated with ~5 m uncertainty when clutter does not exist within ~500m of the river.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
- It may state two separate claims as one. 21
- The quote only partly states this claim. 1
- Probably not a substantial claim. 7
It may state two separate claims as one.
Findingown dataThe accuracy errors for the oblique approach angle cases are an order of magnitude smaller than those for the perpendicular cases and range from 3-68cm.
“The accuracy errors for the oblique approach angle cases are an order of magnitude smaller than those for the perpendicular cases and range from 3-68cm.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
It may state two separate claims as one.
Findingown dataIn the Lake Ilopango case (middle row of Figure 1), the SP track grazes the boundary of the water body rather than passing across its center, leading to a lower magnitude waveform that spans several kilometers.
“In the Lake Ilopango case (middle row of Figure 1), the SP track grazes the boundary of the water body rather that passing across its center, leading to a lower magnitude waveform that spans several kilometers.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
FindingreasoningThe ~850m distance corresponds to ~350m from the SP track over the river and the edge of the lake.
“This corresponds to a distance of ~350m from the SP track over the river and the edge of the lake.”
Quote in the paperNot a substantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
- Rests on an assumption: The circular lake used in the clutter simulations is representative of real nearby water bodies.
It may state two separate claims as one.
Findingnone givenFor the river cases (top and bottom row of Figure 1), the peak and shape of the waveform corresponding to the rivers match closely.
“For the river cases (top and bottom row of Figure 1), the peak and shape of the waveform corresponding to the rivers match closely.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe width was found in all cases to vary proportionally as SNR_peak^m, where the value of m ranges from ~0.5-0.6 and decreases with increasing incidence angle.
“the width was found in all cases to vary proportionally as 𝑆𝑁𝑅 𝑝𝑒𝑎𝑘 𝑚 , where the value of 𝑚 ranges from ~0.5-0.6 and decreases with increasing incidence angle.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
It may state two separate claims as one.
FindingreasoningThe precision of the river width estimates is fairly independent of river width but increases significantly as incidence angle decreases.
“Note that the precision is fairly independent of river width but increases significantly as incidence angle decreases.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
It may state two separate claims as one.
Findingown dataDespite significant discrepancies along the water/land transitions for the Marañón River, and to a lesser degree for the Lake Ilopango and UHE Rondon II overpasses, the signals match well over the plateau section of the SP track where the signal is saturated by coherent scattering (i.e., Zf = 1).
“Despite the significant discrepancies along the water/land transitions for the Marañón River, and to a lesser degree for the Lake Ilopango and UHE Rondon II overpasses, the signals match well over the plateau section of the SP track where the signal is saturated by coherent scattering (i.e., 𝑍 𝑓 = 1 ).”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataA model can be derived that predicts the river width from the SNR peak value, with the form w = a * SNR_peak^m + b, where a, b, and m are constants determined by regression.
“By examining the relationship between the peak SNR that is measured over the river centerline and the river width in each case, a model can be derived that predicts the river width from the SNR peak value (Figure 4).”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataGiven a reasonably accurate water mask, the E2ES captures the main features of the CYGNSS raw IF observations as well as much of the fine structure in the vicinity of the land/water transition region.
“Overall, the results in Figure 1 indicate that given a reasonably accurate water mask, the E2ES captures the main features of the CYGNSS raw IF observations as well as much of the fine structure in the vicinity of the land/water transition region.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
FindingreasoningThe Z_f and SNR peak are both highly correlated with the river width.
“and hence the 𝑍 𝑓 and SNR peak are both highly correlated with the river width.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
It may state two separate claims as one.
Findingown dataThe functional form of the model is robust to variations in measurement geometry (in particular, the incidence angle of the specular reflection) and to the approach angle of the specular point track relative the river.
“The functional form of the model is found to be robust to variations in measurement geometry (in particular, the incidence angle of the specular reflection) and to the approach angle of the specular point track relative the river.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
It may state two separate claims as one.
Findingown dataThe overall shape and magnitude of the simulated SNR series in Figure 1 match the observations well.
“The overall shape and magnitude of the simulated SNR series in Figure 1 match the observations well.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe precision is on the order of 4% or less for the perpendicular approach angle cases, and 3% or less for the oblique approach angle cases.
“and is on the order of 4% or less for the perpendicular approach angle cases, and 3% or less for the oblique approach angle cases.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
It may state two separate claims as one.
Findingown dataTotal uncertainty was found to range from ~2-5m, with uncertainty decreasing with increasing incidence angle.
“Total uncertainty was found to range from ~2-5m, with uncertainty decreasing with increasing incidence angle.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationThe coherent scattering model is based on the Huygens-Kirchhoff principle, a statistical model for coherently reflected signals from an irregular surface.
“The coherent scattering model is based on the Huygens-Kirchhoff principle, which is a statistical model for coherently reflected signals from an irregular surface [23].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Carreno-Luengo, H., Warnock, A., and Ruf, C.S. (2022, January 17–22). The Cygnss Coherent End-to-End Simulator: Development and Results. Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia. source
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationThe incoherent scattering model is derived from a previously developed E2ES used for simulating the CYGNSS incoherent scattering signal over a rough surface such as the ocean, and is based on the bistatic radar equation.
“The incoherent scattering model is derived from a previously developed E2ES which was used for simulating the CYGNSS incoherent scattering signal over a rough surface such as the ocean [21], and is based on the bistatic radar equation [22]:”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [21] Gleason, 2014, The end-to-end simulator for the cyclone GNSS (CYGNSS) mission, IEEE Trans. Geosci. Remote Sens.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [22] Balakhder, 2019, On the Coherency of Ocean and Land Surface Specular Scattering for GNSS-R and Signals of Opportunity Systems, IEEE Trans. Geosci. Remote Sens. source
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationIn a previous work [16], the waveform width during the river overpass was determined to be correlated with observed streamflow.
“The first is the waveform width during the river overpass, which in a previous work [16] was determined to be correlated with observed streamflow.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [16] Warnock, A., and Ruf, C. (2019). Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator. Remote Sens., 11. source
It may state two separate claims as one.
InterpretationreasoningThe shape of the FFZ ellipse elongates with increasing incidence angle, so when the incidence angle is larger the waveform appears more spread out.
“The shape of the FFZ ellipse elongates with increasing incidence angle, so when the incidence angle is larger the waveform appears more spread out.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
InterpretationreasoningData from GNSS-R could provide riverine monitoring with revisit rates on the order of hours to days because of the low cost of GNSS-R receivers and the ability to launch constellations of instruments, and has the potential to supplement and provide independent validation of the SWOT river width data.
“Conversely, data from GNSS-R, which could provide riverine monitoring with revisit rates on the order of hours to days because of the low cost of GNSS-R receivers and the ability to launch constellations of instruments, has the potential to supplement and provide independent validation of the SWOT river width data.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Interpretationnone givenIn cases where the river width is sufficiently narrow that Zf < 1, changes in the SNR value will be correlated with changes in river width and therefore this approach can resolve the width of small rivers.
“In cases where the river width is sufficiently narrow that 𝑍 𝑓 < 1, changes in the SNR value will be correlated with changes in river width and therefore this approach can resolve the width of small rivers.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationBistatic radar data contains signatures that have been used for wide-ranging Earth Science applications, including measuring ocean surface wind speeds, soil moisture, inland water body mapping, river width and surface slope estimation.
“The bistatic radar data contains signatures that have been used for wide-ranging Earth Science applications, including measuring ocean surface wind speeds [4], soil moisture [5][6][7], inland water body mapping [8][9][10][11][12][13][14][15], and river width [16] and surface slope [17] estimation.”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [4] Ruf, 2019, Assessment of CYGNSS Wind Speed Retrieval Uncertainty, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. source
- cites [5] Chew, 2018, Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture, Geophys. Res. Lett. source
- cites [6] Johnson, 2019, Time-Series Retrieval of Soil Moisture Using CYGNSS, IEEE Trans. Geosci. Remote Sens. source
- cites [7] Clarizia, 2019, Analysis of CYGNSS Data for Soil Moisture Retrieval, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. source
- cites [16] Warnock, A., and Ruf, C. (2019). Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator. Remote Sens., 11. source
It may state two separate claims as one.
PremisecitationThe SWOT mission's revisit rate will be on the order of weeks, which is still limiting for rapidly changing, high-risk events.
“The SWOT mission should lead to unprecedented global surveillance of the Earth's rivers; however, its revisit rate will be on the order of weeks, which is still limiting for rapidly changing, high-risk events.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20] Biancamaria, 2016, The SWOT Mission and Its Capabilities for Land Hydrology, Surv. Geophys. source
It may state two separate claims as one.
InterpretationreasoningThe small uncertainty values of 3-5 m are a result of the very high SNR values for coherently scattered signals and the linear dependence of SNR on Z_f, which varies directly with river width for narrow rivers.
“Overall, the small uncertainty values of 3-5 m are a result of the very high SNR values for coherently scattered signals and the linear dependence of SNR on 𝑍 𝑓 , which varies directly with river width for narrow rivers.”
Quote in the paperSubstantial claim- Rests on an assumption: The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
Probably not a substantial claim. The paper may give no reason why it holds.
Interpretationnone givenThe E2ES's assumptions about surface properties (perfectly smooth water, constant NBRCS over land, Fresnel coefficient of 1) will affect the relative magnitudes of the SNR over land and water, but do not significantly impact the width of the water portion of the SNR track that is used to determine river width.
“These assumptions will affect the relative magnitudes of the SNR over land and water, but do not significantly impact the width of the water portion of the SNR track that is used to determine river width.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: The water surface is perfectly smooth (h = 0).
- The authors state this as an assumption: A constant NBRCS value of σ0 = 1 over land.
- The authors state this as an assumption: The water surface has a Fresnel reflection coefficient, ℜ, of 1.
It may state two separate claims as one.
Study design and conductown dataThe E2ES is validated for SP tracks over small inland water bodies, where the first Fresnel zone fraction is < 1 over the entirety of the track, and is shown to reproduce the CYGNSS observations well.
“The E2ES is validated for SP tracks over small inland water bodies, where the first Fresnel zone fraction is < 1 over the entirety of the track, and is shown to reproduce the CYGNSS observations well.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Study design and conductown dataThe E2ES forward model accurately simulates CYGNSS observations of mixed water/land scenes, as demonstrated by comparisons to actual observations of known water body shapes at particular measurement geometries.
“The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes.”
- Also stated in: § 2.1. Model Description · plain
“An end-to-end simulator (E2ES) has been developed that accurately simulates the overland CYGNSS signal by combining two models that calculate the relative contribution of incoherent and coherent scattering at the specular point (SP) of reflection from the distribution of rough and smooth surfaces, respectively.”
- Also stated in: § Table 1. · boosted
“The overall shape and magnitude of the simulated SNR series in Figure 1 match the observations well.”
Quote in the paperSubstantial claim- Also stated in: § 2.1. Model Description · plain
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
Interpretationnone givenThe complexity of the interaction between higher order Fresnel zones in the presence of clutter or a meandering river shape can be expected to alter the relationships determined in the simulated cases considered above.
“Additionally, the complexity of the interaction between higher order Fresnel zones in the presence of clutter or a meandering river shape can be expected to alter the relationships determined in the simulated cases considered above.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
InterpretationcitationThe SWOT mission should lead to unprecedented global surveillance of the Earth's rivers.
“The SWOT mission should lead to unprecedented global surveillance of the Earth's rivers; however, its revisit rate will be on the order of weeks, which is still limiting for rapidly changing, high-risk events.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Biancamaria, 2016, The SWOT Mission and Its Capabilities for Land Hydrology, Surv. Geophys. source
The paper relies on this without support.
Thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed.
“However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research.”
The paper relies on this without support.
River width is a valuable hydrology metric because of its correlation to river discharge and its necessity as an input parameter to hydraulic routing and distributed hydrology models.
“River width is a valuable hydrology metric because of its correlation to river discharge (and therefore of use for flood monitoring, prediction, and water resources management) as well as its necessity as an input parameter to hydraulic routing and distributed hydrology models.”
The paper relies on this without support.
The GNSS signal reflected from inland scenes is comprised of forward scatter from smooth (e.g., inland water bodies) and rough (e.g., land) surfaces.
“The GNSS signal reflected from inland scenes is comprised of forward scatter from smooth (e.g., inland water bodies) and rough (e.g., land) surfaces.”
The paper relies on this without support.
The scattering properties over smooth and rough surfaces are very different, with forward scattering from smooth surfaces typically orders of magnitude stronger than that from rough surfaces.
“The scattering properties over these two surface types are very different, with the forward scattering received from smooth surfaces typically orders of magnitude stronger than that from rough surfaces.”
The authors state this as an assumption.
The water surface is perfectly smooth (h = 0).
“The water surface is assumed to be perfectly smooth (i.e., ℎ = 0 in Equation ( 2))”
The authors state this as an assumption.
A constant NBRCS value of σ0 = 1 over land.
“a constant NBRCS value of 𝜎 0 = 1 is assumed over land.”
The authors state this as an assumption.
The water surface has a Fresnel reflection coefficient, ℜ, of 1.
“Additionally, the water surface is assumed to have a Fresnel reflection coefficient, ℜ, of 1.”
The authors state this as an assumption.
Zf can safely be assumed to be 1 when the SP track is near the center of a very large water body.
“The exact extent of the water bodies at the time of the CYGNSS data collection is not known but, by choosing overpasses of very large water bodies, the 𝑍 𝑓 can safely be assumed to be 1 when the SP track is near the center of the water body.”
The paper relies on this without support.
The E2ES simulations of river crossings accurately represent actual GNSS-R signals over rivers.
“An End-to-End Simulator has been used to accurately simulate these two scattering modalities over scenes that contain both water (smooth surfaces) and land (rough surfaces) and create synthetic data that allows for controlled study of factors impacting the GNSS-R SNR over rivers.”
The paper relies on this without support.
The circular lake used in the clutter simulations is representative of real nearby water bodies.
“While this result is found using a somewhat idealized simulation”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Clutter can be problematic beyond the stated 500 m clearance
MajorThe results say a lake whose edge is about 900 m away can pose a problem at higher incidence angles. The summary nevertheless presents an absence of clutter within about 500 m as sufficient for the stated width uncertainty.
“For the higher incidence angles (Tracks 2 and 3) the lake can pose a problem when located as far as ~1400m away (~900m from the edge of the clutter water body).”
“In summary, a robust modeling approach has been determined that allows river width to be estimated with ~5 m uncertainty when clutter does not exist within ~500m of the river.”
Table 2 labels two different geometry tracks as Track 1
MinorTable 2 labels its third geometry track "Track 1," whereas the paper identifies the third track as Track 3.
“| Track 1 | Track 2 | Track 1 |”
“for Track 1 (left column), Track 2 (middle column), and Track 3 (right column).”
Oblique-case accuracy range includes a perpendicular-case value
MinorTable 7 gives oblique-case accuracy errors of 0.03-0.04 m (3-4 cm). The stated upper end of 68 cm is the perpendicular Track 2 value, not an oblique-case value.
“The accuracy errors for the oblique approach angle cases are an order of magnitude smaller than those for the perpendicular cases and range from 3-68cm.”
“| Perpendicular | 0.48 | 0.68 | 0.57 | | Oblique | 0.04 | 0.04 | 0.03 |”
Citations
Cites
The SWOT Mission and Its Capabilities for Land Hydrology
Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016) · Surveys in Geophysics · doi:10.1007/s10712-015-9346-y · Cited by 458
Chew, C. C., & Small, E. E. (2018) · Geophysical Research Letters · doi:10.1029/2018gl077905 · Cited by 392
Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission
Foti, G., Gommenginger, C. P., Jales, P., Unwin, M. J., Shaw, A. G. P., Robertson, C., & Roselló, J. (2015) · Geophysical Research Letters · doi:10.1002/2015gl064204 · Cited by 355
Analysis of CYGNSS Data for Soil Moisture Retrieval
Clarizia, M. P., Pierdicca, N., Costantini, F., & Floury, N. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2895510 · Cited by 277
CYGNSS: Enabling the Future of Hurricane Prediction [Remote Sensing Satellites]
Ruf, C. S., Unwin, M. J., Dickinson, J., Rose, R., Rose, D., Vincent, M. A., & Lyons, A. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2260911 · Cited by 257
Self-citationTime-Series Retrieval of Soil Moisture Using CYGNSS
Al-Khaldi, M. M., Johnson, J. T., O'Brien, A. J., Balenzano, A., & Mattia, F. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2890646 · Cited by 234
Development of the Global Width Database for Large Rivers
Yamazaki, D., O'Loughlin, F., Trigg, M. A., Miller, Z. F., Pavelsky, T. M., & Bates, P. D. (2014) · Water Resources Research · doi:10.1002/2013wr014664 · Cited by 224
Allen, G. H., & Pavelsky, T. M. (2015) · Geophysical Research Letters · doi:10.1002/2014gl062764 · Cited by 160
Jing, C., Niu, X., Duan, C., Lu, F., Di, G., & Yang, X. (2019) · Remote Sensing · doi:10.3390/rs11243013 · Cited by 155
The SWOT Mission and Its Capabilities for Land Hydrology
Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016) · Space sciences series of ISSI · doi:10.1007/978-3-319-32449-4_6 · Cited by 149
Assessment of CYGNSS Wind Speed Retrieval Uncertainty
Ruf, C. S., Gleason, S., & McKague, D. S. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2825948 · Cited by 128
Self-citationEstimating inundation extent using CYGNSS data: A conceptual modeling study
Chew, C. C., & Small, E. E. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111869 · Cited by 109
A CYGNSS‐Based Algorithm for the Detection of Inland Waterbodies
Gerlein‐Safdi, C., & Ruf, C. S. (2019) · Geophysical Research Letters · doi:10.1029/2019gl085134 · Cited by 107
Self-citationAl-Khaldi, M. M., Johnson, J. T., Gleason, S. T., Loria, E., O’Brien, A. J., & Yi, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3009784 · Cited by 101
Spatial Resolution in GNSS-R Under Coherent Scattering
Camps, A. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2916164 · Cited by 95
Inland Water Body Mapping Using CYGNSS Coherence Detection
Al-Khaldi, M. M., Johnson, J. T., Gleason, S. T., Chew, C. C., Gerlein‐Safdi, C., Shah, R., & Zuffada, C. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3047075 · Cited by 92
Latest Advances in the Global Navigation Satellite System—Reflectometry (GNSS-R) Field
Rodriguez-Alvarez, N., Muñoz-Martín, J. F., & Morris, M. (2023) · Remote Sensing · doi:10.3390/rs15082157 · Cited by 89
Analysis of scattering characteristics from inland bodies of water observed by CYGNSS
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A Machine Learning Method for Inland Water Detection Using CYGNSS Data
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Improving Representation of Tropical Wetland Methane Emissions With CYGNSS Inundation Maps
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Self-citationInland Water Mapping Based on GA-LinkNet From CyGNSS Data
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River Slope Observation From Spaceborne GNSS-R Carrier Phase Measurements: A Case Study
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Detection of inland water bodies under dense biomass by CYGNSS
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Self-citationMapping Surface Water Extents Using High-Rate Coherent Spaceborne GNSS-R Measurements
Zhang, J., Morton, Y. J., Wang, Y., & Roesler, C. J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3218254 · Cited by 33
Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
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Self-citationA new algorithm for mapping large inland water bodies using CYGNSS
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The Cygnss Coherent End-to-End Simulator: Development and Results
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Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Gleason, 2014, The end-to-end simulator for the cyclone GNSS (CYGNSS) mission, IEEE Trans. Geosci. Remote Sens., 3, 306
Cited by
Sorted by citations
Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview
Bu, J., Wang, Q., Wang, Z., Fan, S., Liu, X., & Zuo, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3415754 · Cited by 50
Spaceborne GNSS-R Bistatic Radar Remote Sensing, CYGNSS, and Future Missions
Ruf, C. S., & Gleason, S. T. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3583997 · Cited by 22
Self-citationWang, Y., Morton, Y. J., Minear, J. T., Putnam, A., Conrad, A. V., Axelrad, P., Nerem, R. S., Warnock, A. M., Ruf, C. S., Moreira, D. M., et al. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114597 · Cited by 17
Self-citationXie, J., Bu, J., Li, H., & Wang, Q. (2025) · Remote Sensing · doi:10.3390/rs17071199 · Cited by 8
Surface Water Mapping and Flood Dynamics Monitoring Using CYGNSS and Deep Learning
Pérez, R. A., Setti, P. T., & Tabibi, S. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3685119
Metadata
- Authors
- Warnock, A., Ruf, C. S., & Knoll, A. L.
- Affiliations
- SRI International (US)
- University of Michigan (US)
- SRI International, Ann Arbor, MI 48105, USA
- Corresponding author
- Warnock, April
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2024-04-19
- Volume
- 16
- Issue
- 8
- Pages
- 1446
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W4394954747
- Cited by
- 4
- References
- 28
- Citations by year
- 2025: 3 · 2024: 1
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Aeronautics and Space Administration Earth Science Division (80NSSC21K1119)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › GNSS positioning and interference
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Ocean Waves and Remote Sensing
- Keywords
- GNSS reflectometry, CYGNSS, inland waters, signal-to-noise ratio, measurement uncertainty, clutter
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