The First Application of Stare Processing to Retrieve Mean Square Slope Using the SGR-ReSI GNSS-R Experiment on TDS-1

Tye, Jason; Jales, Philip; Unwin, Martin; Underwood, Craig

doi:10.1109/jstars.2016.2542348

Cited by 35 publications

(20 citation statements)

References 11 publications

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“…or snow water equivalent [1]- [3]. The field has experienced many successful ocean-based applications, experiments, and missions from a dedicated research community since the late 1990s [2], [4]- [6]. This success has since inspired land-based remote sensing applications for SM and snow water equivalent [7]- [13].…”

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confidence: 99%

Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Boyd

Gürbüz

Kurum

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Signals of opportunity (SoOp) reflectometry (SoOp-R) is a maturing field for geophysical remote sensing as evidenced by the growing number of airborne and spaceborne experiments. As this approach receives more attention, it is worth analyzing SoOp-R's capabilities to retrieve subsurface soil moisture (SM) by leveraging communication and navigation satellite transmitters. In this research, the CRLB is used to identify the effects of variable SoOp-R parameters on the best achievable estimation error for root-zone soil moisture (RZSM). This study investigates the use of multiple frequency, polarization, and incidence angle measurement configurations on a two-layered dielectric profile. The results also detail the effects of variable SM conditions on the capability of SoOp-R systems to predict subsurface SM. The most prevalent observation is the importance of using at least two frequencies to limit uncertainties from subsurface SM estimates. If at least two frequencies are used, the CRLB of a profile is retrievable within the root-zone depending on the surface SM content as well as the number of independent measurements of the profile. For a depth of 30 cm, it is observed that a CRLB corresponding to 4% RZSM estimation accuracy is achievable with as few as 2 dual-frequency-based SoOp-R measurements. For this depth, increasing number of measurements provided by polarization and incidence angle allow for sensing of increasingly wet SM profile structures. This study, overall, details a methodology by which SoOp-R receiver system can be designed to achieve a desired CRLB using a trade-off study between the available measurements and SM profile.

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confidence: 99%

Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Boyd

Gürbüz

Kurum

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It has been found that 0 has a first-order link to slope distribution as the other terms vary more slowly over the glistening zone [23]. Although more realistic models of the sea-surface exist, the assumption of a zero-mean isotropic Gaussian distribution of surface slope has been widely adopted in previous GNSS-R studies (e.g., [7], [9]- [11], [17] and [26]), i.e. :…”

Section: Definition Of Glistening Zonementioning

confidence: 99%

“…Global Navigation Satellite System Reflectometry (GNSS-R) is by now a well-established remote sensing technique, which has proven to be extremely successful in sensing ocean surface roughness and near-surface wind conditions [1]- [12]. Retrieval strategies employed so far broadly belong to three categories: a) associating an observable extracted from the delay-Doppler map (DDM) to surface roughness or nearsurface wind [1]- [6]; 2) fitting a theoretical model to measured DDMs [7]- [11]; and c) retrieving the surface scattering coefficient through deconvolution of the DDM [12]. However, this last approach is of limited application because of the difficulty associated with DDM deconvolution, along with the ambiguity linked to DD-to-space mapping [8].…”

Section: Introductionmentioning

confidence: 99%

“…Ocean roughness is typically described using statistical properties such as mean square slope (MSS), defined as the variance of surface slope. Although it has been found that GNSS-R signals have a tighter physical link to MSS than to wind speed [11], it has been demonstrated that realistic estimates of near-surface winds can be derived from roughness using (for example) empirical models [25].…”

Section: Introductionmentioning

confidence: 99%

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Quantification of the Relationship Between Sea Surface Roughness and the Size of the Glistening Zone for GNSS-R

Yan

Huang

Foti

2018

IEEE Geosci. Remote Sensing Lett.

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A formulation of the relationship between seasurface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data pre-processing, quality control and GZ area estimation from the DDM. Next, an example of application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in-situ roughness estimates using datasets from the National Data Buoy Center (NDBC), its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy (r=0.73).

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“…Since such distortions affect the DDM shape, they may seriously compromise the multilook approaches aiming at exploiting the ambiguity-free zone of the DDM, the so-called "horse shoe," if such effects are not taken into account in a forward model of the DDM. Among these methods, it is worth to cite the so-called stare processing shown in [22].…”

mentioning

confidence: 99%

Rationale of GNSS Reflected Delay–Doppler Map (DDM) Distortions Induced by Specular Point Inaccuracies

Grieco

Stoffelen

Portabella

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Global navigation satellite system reflectometry (GNSS-R)-derived winds from the cyclone GNSS (CYGNSS) satellite constellation are expected to significantly improve weather forecasts in the tropical region. Delay-Doppler maps (DDMs) acquired by the TechDemosat-1 (TDS-1) GNSS-R satellite mission suffer from distortions that are highly correlated to on-board specular point estimation inaccuracies. Such distortions may affect wind retrievals, especially when multilook approaches aiming at exploiting the ambiguity-free area of the DDM are applied. This article demonstrates: that CYGNSS DDMs are also affected by such distortions; the rationale of DDM shape asymmetries induced by specular point location inaccuracies; and a simple strategy for reducing such induced distortions. Two different datasets have been used, consisting of both regular and raw intermediate frequency CYGNSS measurements. The results show that, similar to TDS-1, the CYGNSS DDM distortions are correlated to specular point location inaccuracies. Furthermore, such inaccuracies are significantly reduced if more accurate specular point related parameters are used to recompress the raw GNSS-R echo, highlighting some sampling issues that are common to both TDS-1 and CYGNSS missions. These results suggest that multilook wind retrieval approaches aiming at exploiting also the peripheral parts of the DDM may be seriously compromised by such distortions. The latter may be substantially reduced by oversampling the outcoming DDM and by a posteriori choosing the proper DDM subsample. For future upcoming GNSS-R missions, it is strongly recommended to store the raw data for eventual reprocessing in case of miscalibration or processing issues such as those shown in this article.Index Terms-Delay-Doppler map (DDM) distortions, global navigation satellite system (GNSS), reflectometry.

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The First Application of Stare Processing to Retrieve Mean Square Slope Using the SGR-ReSI GNSS-R Experiment on TDS-1

Cited by 35 publications

References 11 publications

Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Quantification of the Relationship Between Sea Surface Roughness and the Size of the Glistening Zone for GNSS-R

Rationale of GNSS Reflected Delay–Doppler Map (DDM) Distortions Induced by Specular Point Inaccuracies

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