IEEE Geoscience and Remote Sensing Magazine - December 2014 - 36

that the "distance"-related observables are more sensitive to
wind speed and less sensitive to variations of the elevation
angle [176]. A new descriptor based on the DDM was proposed for wind-direction retrieval in [177]. This descriptor,
designated as the skewness angle, measures the asymmetry
in the DDM power distribution along the Doppler frequency axis, and it was modeled as a function of wind direction
by means of a simulation study. Then that model was validated using real GNSS-R data from an airborne experiment.
After validation, the DDM skewness model was successfully
used for wind-direction retrieval with a resulting RMS error
on the order of 20°.
(c) Scatterometric-Delay
As seen in Section VII.A.1, the arrival time of the waveform's peak does not correspond to the arrival time of the
specular reflection. We can define the range between both
arrivals as the scatterometric delay: t scatt = t peak - t spec . For
a given geometry, this delay is nearly linear with MSS [28].
This fact is applied to airborne-acquired data to retrieve the
surface's slope variances, MSS [142], [178]. This technique
can be applied to airborne and high-altitude ground-based
experiments, but it is likely that the scatterometric delay
would quickly saturate in spaceborne scenarios.
(D) linear or Deconvolution approacheS
A set of approaches attempts to untangle geophysical information from the radar equation by its reorganization
in sets of linear equations or deconvolution. In general,
these approaches are suited for airborne and spaceborne
scenarios. For example, if the GNSS bistatic radar equation is re-organized in a series of terms, each depending
on the surface slope, the system is linear with respect to the
Probability Density Function (PDF) of the slopes. Discrete
values of the PDF(s) are therefore obtained. This retrieval
does not require an analytical model for the PDF (no particular statistics assumed), thus enabling one to obtain
non-Gaussian features of the PDF (such as up/down-wind
separation [108]).
A similar approach permits 2-D mapping of the normalized radar cross section (NRCS) over the glistening zone,
directly inverted from the DDMs [179]. Accumulation of information from either combining several overlapping glistening zones or from time series of a single satellite DDM to
linearly invert the sea roughness over a fixed 2-D grid was
also suggested in [180] (where it was misnamed tomographic approach). The potential outcome of this latter technique
to the CYGNSS mission is being investigated (N. RodriguezAlvarez and J.L. Garrison, personal communication).
(e) coherence-time
When the specular component of the scattering is significant (very low-altitude observations, very slant geometries,
or relatively calm waters), the coherence time of the interferometric complex field depends on the sea state. It is
then possible to develop the algorithms to retrieve signifi36

cant wave height [181], [182]. This approach is suitable for
ground-based scenarios.
2) Scatterometric Performance
The first analysis of the scatterometric performances expected from spaceborne GNSS-R based on UK-DMC data
was investigated in [52]. The performance obtained was
1.8 m/s rms error for weak and moderate winds. The most
recent re-validation study of the UK-DMC results is presented in [116]. It is based on wind retrieval that combines
five different GNSS-R observables applied to UK-DMC
low-Earth orbiter GNSS-R data, and compares the inferred
values to collocated buoy information.
The performance obtained is of the order of 1.7 m/s error in a range of winds from 2.4 to 10.7 m/s, which very
close to the results obtained in [52]. This is also consistent
with former estimates of the scatterometric performance
for airborne platforms such as in [10] and [167]. The windspeed uncertainties found in [167], using stratospheric
GNSS-R data, resulted in precision from 0.1 to 2 m/s in a
range of wind speeds from 1 to 8 m/s. And in [10], the precision achieved from aircraft altitudes is of the order of 1 m/s.
An intense experimental work on wind retrieval under
hurricane-like conditions, achieving +4 m/s precision in
retrievals of high winds was compiled in [20]-[22], [183].
There is a general agreement that GNSS-R is sensitive to
anisotropies and wind direction with 180° ambiguity [16],
[18], [168], [177], [180]. However, more recent data analysis
strategies permitted inferring non-Gaussian features of the
surface slopes statistics, including the sense (up- or downwind) direction, and breaking the 180° ambiguity [108].
The inhomogeneities of the wave field within the glistening zone can also be explored, as explained above in this
Section. (see, e.g., [179]).
C. Soil MoiSture and Vegetation
As described in Section IV, the permittivity of the reflecting surface has direct impact on the received power,
through the Fresnel reflection coefficient (see (12)). In its
turn, the permittivity of the soil depends on its moisture
content and vegetation cover (e.g., [76]). Being L-band signals, GNSS reflections are mostly sensitive to the upper
1-2 cm layer of the soil [34], [40]. When the soil is covered
with dense vegetation, the bistatic scattering around the
specular direction is essentially influenced by the attenuation [184], so that the reflection power decreases with increasing plant biomass.
The use of GNSS signal surface multipath interference
for ground dielectric permittivity monitoring data of GNSS
ground-based stations was first proposed in [185], [186].
To mitigate an adverse effect of the surface roughness in
GNSS-R moisture retrievals, a reception of the scattered
signal at two orthogonal linear polarizations with analyzing their ratio was suggested in [187]. A simpler approach
was examined experimentally for which only the circular
cross-polar component of the reflected field was acquired
ieee Geoscience and remote sensing magazine

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