Hydrol. Earth Syst. Sci., 22, 19311946, 2018 [620202]

Hydrol. Earth Syst. Sci., 22, 1931–1946, 2018
https://doi.org/10.5194/hess-22-1931-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Deriving surface soil moisture from reflected GNSS signal
observations from a grassland site in southwestern France
Sibo Zhang1,2, Jean-Christophe Calvet1, José Darrozes3, Nicolas Roussel3, Frédéric Frappart3,4, and Gilles Bouhours1
1CNRM, UMR3589 (Meteo-France, CNRS), Toulouse, France
2Fondation STAE, Toulouse, France
3GET, UMR5563 (CNRS, Université Paul Sabatier, UR254 IRD), Toulouse, France
4LEGOS, UMR5566 (CNES, CNRS, IRD, UPS), Toulouse, France
Correspondence: Jean-Christophe Calvet ([anonimizat])
Received: 3 October 2017 – Discussion started: 2 November 2017
Revised: 6 February 2018 – Accepted: 19 February 2018 – Published: 20 March 2018
Abstract. This work assesses the estimation of surface volu-
metric soil moisture (VSM) using the global navigation satel-
lite system interferometric reflectometry (GNSS-IR) tech-
nique. Year-round observations were acquired from a grass-
land site in southwestern France using an antenna consec-
utively placed at two contrasting heights above the ground
surface (3.3 and 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 remov-
ing vegetation effects by the separation of growth and senes-
cence periods and by the filtering of the GNSS-IR observa-
tions that are most affected by vegetation. Antenna height has
no significant impact on the quality of VSM estimates. Com-
parisons between the VSM GNSS-IR retrievals and the in
situ VSM observations at a depth of 5 cm show good agree-
ment (R2D0.86 and RMSED0.04 m3m3/. 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 and 5 cm, especially
during light rainfall events.
1 Introduction
Soil moisture is a key component in the hydrological cy-
cle and in the soil–plant–atmosphere continuum. It is also
important for irrigation management and flood prediction(Rodriguez-Iturbe and Porporato, 2007). However, in situ ob-
servations of soil moisture are very sparse and with small
sampling volumes. On the other hand, L-band satellite-
derived products, for example, from the Soil Moisture Ac-
tive Passive (SMAP) mission or the Soil Moisture and Ocean
Salinity (SMOS) mission, have a coarse resolution of tens
of kilometers (Chan et al., 2016; Kerr et al., 2001). These
products consist of surface volumetric soil moisture (VSM)
and concern the top soil layer (from the soil surface to
a depth of 1 to 5 cm). There is a need to monitor VSM
at the local scale in order to validate model simulations,
and satellite-derived products. The International Soil Mois-
ture Network (Dorigo et al., 2013) has been collecting such
in situ observations. The Committee on Earth Observation
Satellites (CEOS) Land Product Validation group has recom-
mended expanding the soil moisture networks (Morisette et
al., 2006). In particular, the development of new automatic
monitoring techniques to measure VSM is needed.
The global navigation satellite system interferometric re-
flectometry (GNSS-IR) technique has demonstrated strong
potential to monitor VSM using ground-based receivers
(Chew et al., 2014). GNSS antennas measure the signal di-
rectly emitted by the GNSS satellites, together with the sig-
nal reflected by the surface surrounding the antenna. The
GNSS-IR technique allows relating the reflected signal to the
characteristics of the reflecting surface and to retrieve geo-
physical variables. Over land, variables such as soil moisture,
snow depth and vegetation parameters can be observed using
this technique (Larson et al., 2008; Small et al., 2010; Larson
and Nievinski, 2013; Wan et al., 2015; Larson, 2016; Rous-
Published by Copernicus Publications on behalf of the European Geosciences Union.

1932 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
sel et al., 2016; Zhang et al., 2017). GNSS satellites emit
active L-band microwave signals (between 1.2 and 1.6 GHz).
The L-band signal is less affected by vegetation effects than
shorter wavelengths, which is an asset for retrieving surface
soil moisture (Kerr et al., 2001). The GNSS-IR footprint can
cover up to thousands of square meters, depending on the
antenna height and on the satellite elevation angle (Larson et
al., 2010; Vey et al., 2016).
In addition to an antenna specially designed to receive the
reflected GNSS signal from the land surface (Zavorotny et
al., 2014), classical geodetic-quality GNSS antennas can be
used to estimate VSM (Larson et al., 2008). Such antennas
have an antenna gain pattern optimized for right hand circular
polarization (RHCP) and minimized for left hand circular po-
larization (LHCP). A GNSS network called Plate Boundary
Observatory (PBO) H 2O with geodetic-quality on-ground
antennas in western USA is currently used to monitor VSM
(Larson et al., 2013; Larson, 2016; Chew et al., 2016) and
snow depth (Larson et al., 2009). The basic observation used
in this technique is the signal-to-noise ratio (SNR) which is
related to temporal changes in the interference between the
direct and the reflected GNSS signals. Each GPS satellite re-
peats the same orbital cycle from one day to another (offset
of a few tenths of meter between two adjacent cycles). This
property permits monitoring surface changes through time of
the environmental conditions surrounding the receiving an-
tenna.
The present-day Block II R-M (Replenishment Modern-
ized) and Block II F (Follow-on) GPS satellites now trans-
mit a L2C (1227.60 Hz) civilian signal. Power and preci-
sion of the L2C signal are higher than for the L1 C/A signal
(1575.42 Hz) transmitted by all GPS satellites. Several pre-
vious studies, such as Larson et al. (2008, 2010), Chew et
al. (2014), Chew et al. (2016) and Small et al. (2016) exclu-
sively analyzed the SNR data from the GPS L2C signal to
retrieve soil moisture. The Block II F satellites also transmit
the latest L5 signal (1176.45 Hz) as well, which features even
higher power, greater bandwidth and an advanced signal de-
sign. There are now seven Block II R-M satellites (pseudo-
random noise, PRN, numbers 5, 7, 12, 15, 17, 29 and 31,
identifying each satellite) and 12 Block II F satellites (PRN
1, 3, 6, 8, 9, 10, 24, 25, 26, 27, 30 and 32).
Due to the motion of the satellites, the direct and reflected
signals cause an interference pattern in SNR data. The SNR
oscillations depend on known attributes such as the satel-
lite elevation angle, signal wavelength and antenna height.
The SNR amplitude and phase can be solved by using the
least squares estimation (LSE) method (Larson et al., 2008;
Chew et al., 2016). Larson et al. (2008, 2010) empirically
showed that phase correlates with near-surface soil moisture,
with values of the coefficient of determination ( R2/rang-
ing from 0.76 to 0.90. This property was used by Chew et
al. (2014) to develop an algorithm to estimate surface soil
moisture (top 5 cm) for bare soil. They used a physical sur-
face scattering and dielectric permittivity model to derive arelationship between the phase and soil moisture, in volu-
metric units (m3m3/. Vey et al. (2016) validated this al-
gorithm, using field observations acquired during the 2008–
2014 period from a site presenting a high percentage of
bare soil. They obtained the following R2and root mean
square error (RMSE) scores for VSM retrievals: R2D0.80
and RMSED0.05 m3m3. However, for vegetated soil the
phase of the SNR is also affected by vegetation. Chew et
al. (2016) showed that seasonal vegetation effects on phase
have to be considered for soil moisture estimation. They
also observed that amplitude decreased as vegetation grew. A
model database for the SNR from L2C signal was used to re-
move most significant vegetation effects. Small et al. (2016)
compared different algorithms of GNSS-IR soil moisture re-
trieval in the presence of vegetation.
Zhang et al. (2017) used the GNSS-IR technique for a
wheat field throughout the growth and senescence period in
2015. The L1 C/A signal was acquired over a wheat field
during a period of about 7 months using a Leica GR25 re-
ceiver and a Leica AR10 antenna at a constant height of 2.5 m
above the soil surface. They showed that VSM could not be
retrieved when the vegetation canopy is too dense, i.e., plant
height and simulated dry aboveground biomass larger than
one wavelength (19 cm for L1) and 0.08 kg m2, respec-
tively. On the other hand, relative plant height could be re-
trieved in such conditions. In this study, both L2C and L5
signals were acquired over a meadow during a rather long
period of time of about 15 months using the same equipment
(GR25 receiver, AR10 antenna) at contrasting heights (3.3
and 29.4 m) above the soil surface.
The objectives of this study are to (1) investigate VSM
estimation over a meadow, contrasting conditions of plant
phenology (growth, senescence, after and before cutting),
(2) compare the use of L2C and L5 signals (3) assess the
impact of a major change in the height of the receiving an-
tenna above the soil surface, in relation to the SNR sam-
pling interval. Investigating the impact of the sampling in-
terval on VSM retrievals is needed due to the fact that small
sampling intervals (e.g., 1 s) generate a large amount of data
(100 Mb day1for GPS satellites). Larger sampling inter-
vals may be defined to reduce the amount of daily data.
A key difference between this study and Zhang et
al. (2017) is related to the type of observed vegetation
canopy. The meadow considered in this study and the wheat
field considered by Zhang et al. (2017) present contrasting
characteristics. The meadow is cut once a year and consists
of a multi-species permanent grassland incorporating a litter
composed of dead leaves. On the other hand, the wheat crop
in Zhang et al. (2017) consisted of a single plant species with
no litter.
Past microwave remote sensing studies (e.g., Saleh et al.,
2007) have shown that permanent grasslands behave differ-
ently from crops. Because permanent grasslands incorporate
a litter composed of dead leaves, they can intercept precipita-
tion considerably more than annual crops. The short growing
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S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations 1933
cycle of annual crops does not allow for the accumulation of
large amounts of litter material. This property of permanent
grasslands can have a major effect on the microwave signal
and can perturb the retrieval of VSM, even at GPS L-band
(Saleh et al., 2007). Also, the structure of grass canopies dif-
fers from the structure of crops such as wheat and this has an
impact on the attenuation of the microwave signal by vegeta-
tion (Wigneron et al., 2002).
GPS SNR data from both L2C and L5 signals are obtained
using a geodetic-quality GNSS antenna. SNR analysis using
the GNSS-IR technique is used to retrieve VSM over a field
covered with grass using the normalization method based
on the newly established scaled wetness index proposed by
Zhang et al. (2017). Another point to underline is the impact
of the antenna height (here two levels: 3.3 and 29.4 m above
the soil surface) on the VSM retrieval. Moreover, the VSM
retrievals from two kinds of GPS signal wavelengths (24.45
and 25.40 cm for L2C and L5, respectively) are compared
with field observations. We analyze the vegetation effects on
VSM retrieval accuracy. Another important topic addressed
is the influence of the sampling interval on the VSM esti-
mates. As the SNR period changes depending on the antenna
height, satellite elevation angle, elevation angle change rate
and GNSS signal wavelength, the sampling interval has to be
adjusted accordingly in order to maintain the VSM retrieval
accuracy.
Data are described in Sect. 2 and methods in Sect. 3. The
obtained soil moisture retrievals are presented in Sect. 4 and
compared with independent VSM estimates. Results are dis-
cussed in Sect. 5, and the main conclusions are summarized
together with prospects for further research in Sect. 6.
2 Site and data
2.1 Site description and validation data
The study site is located at the premises of Meteo-France in
Toulouse, France, over an experimental field covered with
grass (433402600N, 12202700E). Since 2012, this instru-
mented site has included soil moisture profile observations
from the surface down to a depth of 2.2 m. Other measure-
ments such as turbulent fluxes are made in the framework of
the Meteopole-Flux project (https://www.umr-cnrm.fr/spip.
php?article874&lang=en) and ICOS (Integrated Carbon Ob-
servation System, https://icos-eco.fr/). The fine-earth soil in
the experimental field at a depth of 5 cm consists of 51 %
sand, 14.5 % clay and 34.5 % silt.
The grass height did not exceed 0.3 m during the experi-
ment time period. This is much lower than maximum height
of the wheat crop (1 m) in Zhang et al. (2017). A large dif-
ference could also be noticed in maximum aboveground dry
biomass values: less than 0.5 kg m2for grass (this study)
and about 1 kg m2for wheat (Zhang et al., 2017). The grass
was cut twice during the study period. The cutting processtook several days and the grass was fully cut on 7 Octo-
ber 2015 and 9 July 2016, for the 29.4 and 3.3 m antenna
observing areas, respectively.
Mean in situ VSM observations at 5 and 1 cm depths were
performed using precise Delta-T ML2x ThetaProbes and
low-cost Decagon EC-5 VSM sensors, respectively. Three
ThetaProbes measured VSM at a depth of 5 cm and were lo-
cated within a few meters of each other (red star in Fig. 1).
The mean value was derived from these probes to represent
the in situ VSM observations at 5 cm. Only one EC-5 sensor
was used to measure VSM at 1 cm. Precipitation measure-
ments were made in the experimental field by one rain gauge
close to the in situ soil moisture sensors. A small fraction
of the precipitation time series was missing. Missing data
were replaced by the precipitation data obtained from the
SAFRAN atmospheric analysis (Durand et al., 1993, 1999).
Additionally, scaled VSM observations at a depth of 1 cm
and scaled VSM simulations for the top 1 cm thick soil layer
were used as independent benchmarks for validation.
VSM simulations for the top 1 cm were produced using
the ISBA (Interactions between Soil, Biosphere, and At-
mosphere) land surface model within the SURFEX (ver-
sion 8.0) modeling platform (Masson et al., 2013). In ad-
dition to VSM, simulations included the soil iced water
content and the vegetation aboveground dry biomass. The
ISBA model used the atmospheric forcing data produced by
the SAFRAN atmospheric analysis of Météo-France. The
model version used in this study was designed for generic
country-scale simulations over France at a spatial resolution
of 8 km8 km. It was not calibrated for this particular site.
Sub-grid vegetation types are represented and soil moisture
and soil temperature profiles are simulated for each vege-
tation type, independently of other vegetation types. In this
study, the C3 grassland plant functioning type and a mul-
tilayer representation of the soil hydrology are considered.
The model soil depth is 12 m, with 15 layers, and the layer
thickness increases from the top surface layer to the deep-
est layers (Decharme et al., 2011). It must be noted that the
SAFRAN precipitation forcing is based on ground observa-
tions and is quite realistic (Quintana-Segui et al., 2008). The
ISBA configuration and the SAFRAN atmospheric analysis
used to force the model are described in Lafont et al. (2012).
2.2 GNSS data
In this study, GNSS SNR data were acquired using a Leica
GR25 multi-constellation and multi-band geodetic receiver
equipped with an AR10 antenna for more than 1 year. Two
measurement configurations were explored (Fig. 1). First,
from 1 August 2015 to 5 June 2016, the antenna was placed at
the top of a building close to the studied grassland, at a height
of 29.4 m above the soil surface (433403000N, 12202600E).
Second, from 8 June to 6 October 2016, the antenna was
moved on top of a small technical shed located within the
meadow, close to the in situ sensors, at a height of 3.3 m
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1934 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
Figure 1. Experimental site of Meteopole-Flux. The specular reflection points and first Fresnel zone (FFZ) areas from the selected satellite
tracks are shown in orange for a 29.4 m GNSS antenna (“H” red dot). The specular reflection points and FFZ areas for a 3.3 m GNSS antenna
(“L” red dot) are shown in blue. The red star indicates the location of in situ soil moisture observations. Background geographic information
is from Google Earth.
above the soil surface. During the first 29.4 m antenna height
experiment, the SNR sampling interval was reduced from 10
to 1 s on 19 March. When the antenna height was changed
from 29.4 to 3.3 m, the sampling interval remained at a value
of 1 s. GNSS SNR data were missing for 24 days: from 1 to
11 January, from 17 to 26 May and on 1, 6 and 7 June 2016.
In this study, both L2C and L5 SNR data from the GPS
Block II R-M and Block II F satellites were used. The as-
cending and descending parts of the same satellite were pro-
cessed separately and were considered as independent satel-
lite tracks (Roussel et al., 2015, 2016).
The valid SNR segment for each ascending or descend-
ing satellite track was limited based on the available satellite
elevation angle range (90being defined as zenith). For the
3.3 m antenna height, the multipath signature was small at
elevation angles above 30or below 7, and the reflecting re-
gion (first Fresnel zone, FFZ) often included both ground and
surrounding obstructions. Therefore, only data correspond-
ing to elevation angles ranging from 7 to 30were consid-
ered. For a given satellite track, the field observation area
was about 300 m2, and the observing duration was about 1 h
(Table 1). The range of instantaneous FFZ areas is indicated
in Table 1. After sorting elevation angles, 36 and 21 satellite
tracks were available for L2C and L5 SNR data, respectively.
The corresponding reflecting points and FFZ areas, obtained
using a reflection location model for GNSS-R (Roussel et
al., 2014), are shown in Fig. 1. The successive experimental
configurations are listed in Table 2 and shown in Fig. 2.
Measurements from the antenna at a height of 29.4 m were
affected by surrounding obstructions (buildings and impervi-
ous areas like car park, roads, etc.) and by an under-sampling
issue at a sampling interval of 10 s (see Sect. 4.2). In order tocope with these problems, only six satellite tracks were used
to retrieve VSM from L2C SNR data (GPS PRN 03, 07, 08,
17, 25 and 26), and four satellites tracks from L5 SNR data
(GPS PRN 03, 08, 25 and 26). Satellite track characteristics
and instantaneous FFZ areas are given in Table 1. The se-
lection of satellite tracks and elevation angles was performed
by comparing VSM retrievals with the in situ VSM observa-
tions described in Sect. 2.1. It must be noted that this limi-
tation only affected measurements at a height of 29.4 m and
was caused by the more complex experimental constraints
in this configuration (e.g., possible parasitic signal reflection
on buildings). For the low antenna configuration (3.3 m), this
additional data sorting was not needed and all available satel-
lite tracks with a complete elevation angle range (between 7
and 30) were used. As a result, a larger variety of satellite
tracks could be used for the antenna at a height of 3.3 m with
1 s sampling. With a higher antenna, the size of the observed
reflecting surface markedly increases (Larson et al., 2010).
Although the elevation angle range used for the antenna at
29.4 m is smaller than for the antenna at 3.3 m (Table 1),
a much larger observing area is obtained for each satellite
track. More details about the elevation range, the observing
time period and approximate observing area for each satellite
track are shown in Table 1. The SNR data are typically con-
verted from their native logarithmic units (dB-Hz) to a linear
scale (V V1/(Vey et al., 2016). For a static receiver, the
SNR is governed to a large extent by the interference pattern
(IP). The IP is defined as the coherent summation of direct
and reflected GNSS signals on the in-phase and quadrature
space (Zavorotny et al., 2014). This coherent summation gen-
erates an IP where high and intermediate frequencies, distinct
from noise frequencies, are related to the difference of trav-
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S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations 1935
Figure 2. Timeline of experiment. (a)Daily GNSS VSM retrieval time series ( ND409) using both L2C and L5 SNR data for the whole
experimental period (from 1 August 2015 to 6 October 2016) is shown in red line, together with daily mean in situ VSM observations at a
depth of 5 cm (green line). The blue line represents the daily precipitation in mm day1. The black lines indicate the grass cutting before
7 October 2015 and before 9 July 2016. The retrievals are obtained separately depending on four time segments (Table 2). (b)The red
line represents the aboveground dry biomass (kg m2/of the grass simulated by the ISBA model before grass cutting; the red dashed line
indicates the maximum simulated dry biomass (0.25 kg m2/in 2015. Grass cutting is also shown in black solid lines. The L2C (L5) SNR
normalized amplitude ( Anorm, dimensionless) time series is shown in green (blue). Normalization is performed separately for TS1 and TS2,
and for the period with data acquired from the 3.3 m antenna using a 1 s sampling interval. The latter corresponds to the merged TS3 and
TS4. The black dashed line indicates the Anorm threshold (0.78) for evaluating the vegetation effects.
Table 1. Characteristics of the selected satellite tracks from the GNSS antenna at a 29.4 m height and at a 3.3 m height (north is 0azimuth
angle, clockwise rotation).
Antenna
height Satellite Elevation angle Azimuth angle Areas per Instantaneous FFZ Time duration per
(m) tracks range () range () track (m2/ area range (m2/ track (min)
29.4 GPS PRN 03 14 to 23 216 to 219 900400–150 21.6
GPS PRN 07 168 to 169 21.2
GPS PRN 08 166 to 169 20.3
GPS PRN 17 223 to 228 24.0
GPS PRN 26 168 to 171 20.3
GPS PRN 25 9 to 17 228 to 232 20001000–300 20.7
3.3 36 for L2C 7 to 30 – 300200–10 60
(10 during TS3)
21 for L5
(6 during TS3)
eled distance between direct and reflected waves. The IP can
be characterized with GNSS receivers using either two an-
tennas (e.g., Rodriguez-Alvarez et al., 2011) or one antenna
(e.g., Larson et al., 2008; Chew et al., 2014; Zhang et al.,2017). In this study we used the one-antenna IP technique
as illustrated by Fig. 1 in Larson et al. (2016) for a simple
planar and horizontal ground reflection. A low order polyno-
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1936 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
Table 2. Soil moisture scores for four time segments from the comparison between scaled VSM validation data (in situ VSM observations at
1 and 5 cm and ISBA VSM simulations at 1 cm) and scaled GNSS VSM retrievals (both L2C and L5). Scores in m3m3are given (between
brackets) for in situ observations at 5 cm. MAE is the mean absolute error, RMSE is the root mean square error and SDD is the standard
deviation of difference. Nis the number of observations.
Time segment TS1 TS2 TS3 TS4
Dates from 1 August 2015 from 19 March 2016 from 8 June 2016 from 9 July 2016
to 18 March 2016 to 5 June 2016 to 8 July 2016 to 6 October 2016
Vegetation stages senescence, cutting, growing growing cutting
dormancy
Antenna height
(m)29.4 29.4 3.3 3.3
Sampling interval
(s)10 1 1 1
N 220 68 31 90
Independent soil
moisture
estimatesISBA
1 cmin situ
1cmin situ
5 cmISBA
1 cmin situ
1cmin situ
5 cmISBA
1 cmin situ
1cmin situ
5 cmISBA
1 cmin situ
1cmin situ
5 cm
MAE 0.32 0.33 0.30
(0.031
m3m3/0.47 0.58 0.56
(0.039
m3m3/0.34 0.54 0.65
(0.035
m3m3/0.33 0.33 0.38
(0.013
m3m3/
RMSE 0.40 0.42 0.40
(0.040
m3m3/0.61 0.71 0.65
(0.048
m3m3/0.51 0.69 0.80
(0.043
m3m3/0.42 0.44 0.62
(0.019
m3m3/
SDD 0.40 0.42 0.40
(0.037
m3m3/0.61 0.71 0.65
(0.039
m3m3/0.51 0.69 0.80
(0.036
m3m3/0.42 0.44 0.62
(0.018
m3m3/
R20.84 0.83 0.85 0.66 0.55 0.62 0.75 0.57 0.45 0.83 0.81 0.65
mial curve is fitted to SNR data in order to retain only the
multipath IP (Bilich et al., 2008).
3 Methods
The modulation of the SNR by the multipath frequency can
be expressed as (Larson et al., 2008, 2010; Chew et al.,
2016):
SNRDAcos.4H 0
sin/; (1)
whereAis the amplitude of the modulation, is the phase
offset,is the satellite elevation angle and is the GNSS
signal wavelength. H0is a fixed a priori effective antenna
height for each satellite track, which is not known and has
to be estimated from the SNR data in snow-free and sparse
vegetation conditions (Chew et al., 2016). Based on Eq. (1),
SNR phase (/can be solved by LSE method, and then this
estimatedcan be used to retrieve VSM.
Due to the good linear relationship between and in situ
surface VSM, VSM can be estimated for each satellite track
(Chew et al., 2016):
VSMDS
.min/CVSM resid (2)
TheSparameter (in m3m3degree1/is defined using the
a priori value. A value of SD0.0148 m3m3degree1wasproposed by Chew et al. (2016) for the PBO H 2O network.
This value is adapted to situations of low vegetation den-
sity or cover and is valid for the Trimble antennas used in
the PBO H 2O network. In this equation, the time series is
zeroed using a minimum phase value ( min/for each satel-
lite track. This procedure is useful for ensuring compatibil-
ity among different satellite tracks. minis the mean of the
lowest 15 % of values for each satellite track during the
considered time segment and VSM residis the residual (mini-
mum) volumetric soil moisture value.
3.1 A new normalized SNR phase method (Zhang et
al., 2017)
In this study, the method proposed by Zhang et al. (2017)
is used. Normalizing time series ensures compatibility
among different satellite tracks (Zhang et al., 2017). Here,
is normalized with zero minimum in order to obtain the
scaled wetness index ( index/as the following:
indexDmin
maxmin; (3)
whereminandmaxare the mean of the lowest and highest
15 % of the statistical distribution of for each satellite track
during the considered time segment (TS), respectively. This
averaging procedure is used in order to filter out outliers cor-
responding to abnormally high or low estimates. Negative
index values are replaced by zero.
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S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations 1937
Moreover,index can be used to estimate VSM as follows:
VSMDindex
/parenleftbig
VSM obs_maxVSM obs_min/parenrightbig
CVSM obs_min: (4)
Similarly to phase computation and in order to avoid arti-
facts, VSM obs_min and VSM obs_max are set as the mean of the
lowest and highest 15 % of daily mean in situ VSM observa-
tions at a depth of 5 cm during the considered time segment,
respectively. The median VSM estimate from all available
satellite tracks is considered as the final VSM estimate per
day. In order to better correct for vegetation effects, vegeta-
tion growth and senescence were considered as independent
time segments instead of applying Eqs. (3)–(4) to the whole
period.
3.2 Assessment of vegetation effects
SNR amplitude ( A/is affected by vegetation, which can
be used to assess whether or not vegetation effects are sig-
nificant. Chew et al. (2016) defined the normalized ampli-
tude (Anorm/as the ratio of amplitude to the average of
the top 20 % amplitude values. Anorm (dimensionless) val-
ues below 0.78 indicate that vegetation effects are significant
and cannot be neglected. When vegetation effects are sig-
nificant, theSparameter value may depart from the value
used in Eq. (2). A way to cope with this issue is to apply
the Zhang et al. (2017) method for a given time segment
presenting consistent vegetation properties. Phase is scaled
andSis not needed. The time series in this study is sepa-
rated into four time segments: (1) TS1, from 1 August 2015
to 18 March 2016 (a vegetation senescence and dormancy
period with data acquired from the antenna at 29.4 m us-
ing a 10 s sampling interval); (2) TS2, from 19 March to
5 June 2016 (a vegetation growing period with data acquired
from the antenna at 29.4 m using a 1 s sampling interval);
(3) TS3, from 8 June to 8 July 2016 (a vegetation growing
period with data acquired from the antenna at 3.3 m antenna
using a 1 s sampling interval); and (4) TS4, from 9 July to 6
October 2016 (after the grass cutting with data acquired from
the antenna at 3.3 m using a 1 s sampling interval).
Another step is to select relevant satellite tracks under sig-
nificant vegetation effects. This is particularly challenging in
dense vegetation conditions. Even in conditions presenting
significant vegetation effects, some satellite tracks can be se-
lected to retrieve VSM. This occurs during TS3, correspond-
ing to lowAnorm values (Fig. 2). In order to select satellite
tracks in such conditions, only tracks presenting a continuity
of VSM retrievals with the following vegetation senescence
period (TS4) are kept. Only tracks giving similar VSM esti-
mates (difference lower than 0.06 m3m3/at the end of TS3
and at the beginning of TS4 are used for TS3. This proce-
dure eliminates the tracks corresponding to the most densely
vegetated areas in the grass field.
Figure 3. Scatter plot of daily mean in situ VSM observations
(ND409) at a depth of 5 cm vs. GNSS VSM retrievals (from both
L2C and L5) for the whole experimental period from 1 August 2015
to 6 October 2016.
4 Results
4.1 VSM estimates
Figure 2 presents the VSM estimates derived from both the
L2C and L5 SNR data using the normalized SNR phase
method (see Sect. 3.1) and the vegetation correction method
(see Sect. 3.2). Results are shown for the whole experiment
period from 1 August 2015 to 6 October 2016, and for all
the experimental configurations of antenna height, sampling
interval and grass cutting (time segments).
The first grass cutting event occurs during TS1 but has no
effect onAnorm because the aboveground biomass is rela-
tively low (less than 0.25 kg m2/, as shown in Fig. 2. On the
other hand, the second cutting occurring before 9 July 2016
has a significant effect on Anorm because, at that time, veg-
etation is not yet senescent (aboveground biomass is about
0.50 kg m2/. Another reason to separate TS3 and TS4 is that
mean L2CAnorm values are significantly smaller during TS3
(0.56 and 0.94 for TS3 and TS4, respectively).
The scaled wetness indexes ( index/and VSM estimates
are obtained for each of these four time segments. The VSM
scores for the four separated time segments are recorded in
Table 2. The mean absolute error (MAE), RMSE and R2
scores for senescent/dormant and cut vegetation (TS1 and
TS4) are better than during the vegetation growing period
(TS2 and TS3). Scatter plot of the in situ VSM observations
(ND409) at a depth of 5 cm versus GNSS VSM retrievals
is shown for the whole experiment in Fig. 3. The RMSE
and the standard deviation of difference (SDD) scores are
RMSED0.038 m3m3and SDDD0.035 m3m3, respec-
tively. TheR2score is equal to 0.86 for merged L2C and
L5 SNR data. About the same value is found using only L2C
data (R2D0.85). The mean bias (0.02 m3m3/is positive,
because the VSM estimates are generally larger than in situ
VSM observations at 5 cm depth.
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1938 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
Table 3. Soil moisture scores between daily mean in situ VSM ob-
servations at a depth of 5 cm and GNSS VSM retrievals (either L2C
or L5) during TS1 (SNR data from the 29.4 m antenna with 10 s
sampling interval from 1 August 2015 to 18 March 2016). MAE,
RMSE, SDD and Nare defined in Table 2.
Signal L2C L5
N 220 220
Mean bias (m3m3/0.016 0.017
MAE (m3m3/ 0.032 0.033
RMSE (m3m3/ 0.042 0.042
SDD (m3m3/ 0.039 0.038
R20.83 0.84
Figure 2 shows that the GNSS VSM retrievals are more
sensitive to light rainfall events than in situ VSM observa-
tions at 5 cm depth. Such events occur during the summer
and autumn of 2016. It can be observed that while GNSS
VSM estimates peak at the same time as light rain, the dif-
fusion of water in the soil does not reach the probes at 5 cm
depth. This is why the GNSS VSM tends to be larger than
in situ VSM. This difference reduces the correlation and in-
creases the errors and can be attributed to a GNSS-IR sensing
depth less than 5 cm (Chew et al., 2014; Shellito et al., 2016),
in relation to vegetation litter effects (see Sect. 5.3).
In the following subsections, more detailed comparisons
are presented for antenna heights of 29.4 and 3.3 m.
4.2 VSM estimates from a GNSS antenna at 29.4 m
above the soil
In most previous studies, VSM was retrieved from GNSS an-
tennas at about 2 or 3 m above the soil surface. Increasing the
antenna height can significantly expand the size of the ob-
served areas. In this study, the impact of using a 29.4 m an-
tenna on VSM retrievals is assessed using TS1 and TS2 data.
The whole observation area for each track is about 900 m2or
even larger. The grass is cut in TS1, before 7 October 2015.
Before grass cutting, the maximum simulated aboveground
dry biomass is about 0.25 kg m2(Fig. 2). For TS1, Anorm
values are more often than not above 0.78 (Fig. 2). Above
this threshold value, the vegetation effects are not significant
(Chew et al., 2016). From mid-August to mid-September
(before the start of grass cutting), Anorm is slightly smaller
than the threshold value, but VSM can be estimated at these
dates. Moreover, no grass cutting effects are observed in the
Anorm values, which also shows that vegetation effects are
not significant. The VSM retrievals, using the L2C SNR data,
are compared in Fig. 4 with in situ VSM observations at a
depth of 5 cm. Figure 5 shows that VSM retrievals tend to
be larger than the in situ observations. Similar results are ob-
tained from the L5 SNR data (Fig. 5). The L2C and L5 VSM
retrieval scores are presented in Table 3.Figure 5 and Table 3 show that VSM retrievals using L5
SNR data are very close to those derived from L2C SNR data.
The retrieval accuracies from L2C and L5 SNR data are sim-
ilar (Table 3), showing that both L2C and L5 SNR data can
be used to retrieve VSM. In Table 2, L2C and L5 SNR data
are combined. Results for TS1 in Table 2 show slightly im-
proved scores with respect to those in Table 3. This can be
explained by the larger number of available satellite tracks
per day. It is interesting to note that results very similar to
those presented in Fig. 5 can be obtained by multiplying the
Svalue used by Chew et al. (2016) by 0.6 (not shown).
Overall, the scores obtained during TS1, at a height of
29.4 m and a sampling interval of 10 s are comparable to
those obtained in other time segments, including TS2 with
a sampling interval of 1 s. The scores (Table 2) in TS2 are
similar to the scores in TS1. This does not mean that there
is no effect from the sampling interval because vegetation
conditions are different in TS1 and TS2. TS2 corresponds to
a vegetation growing period. Vegetation growth impacts the
reflecting surface and has an impact on the SNR data as illus-
trated by the fast decrease of Anorm values in Fig. 2. More-
over, the SNR data in TS4 (after grass cutting) are used to
assess the impact of changing the sampling interval, with-
out change in vegetation conditions. This is discussed in
Sect. 5.4.
4.3 Removing vegetation growth effects from VSM
retrievals
Substantial vegetation effects are observed during TS3, at
the end of the growing season of 2016. This is evidenced
byAnorm values lower than 0.78 (Fig. 2). Grass is cut at the
end of TS3 (before 9 July 2016). After grass cutting, the SNR
Anorm values gradually rise to a relatively large value (above
0.78). For example, the daily mean L2C Anorm values are
0.67, 0.69, 0.75 and 0.86 from 6 to 9 July 2016, respectively.
In order to remove vegetation effects, the SNR data be-
fore and after cutting are considered as distinct datasets (see
Sect. 3.1 and 3.2). SNR data are used, time segment by time
segment, to obtain soil wetness index and then VSM esti-
mates. The observed soil moisture minimum and maximum
values are derived for each time segment. For L2C (L5), 10
(6) satellite tracks out of 36 (21) are selected for use during
TS3. Figure 6a shows the VSM retrievals for each time seg-
ment TS3 and TS4 for L2C SNR data after removing vegeta-
tion effects by applying the Zhang et al. (2017) method. The
corresponding scores are listed in Table 4. Similar results are
obtained for L5 and both L2C and L5 SNR data (Table 4).
Results obtained by applying the Zhang et al. (2017) method
to the merged time segments (TS3 and TS4) for L2C SNR
data are also shown in Fig. 6 and in Table 4. In this case,
SNR-derived VSM are too dry before the cutting and too wet
after the cutting (Fig. 6b).
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S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations 1939
Figure 4. Median of the daily VSM retrievals ( ND220, red dots) and their daily statistical distribution (grey box plots) for six available
satellite tracks. Daily mean in situ VSM observations at a depth of 5 cm are shown by the green line. The black line indicates the grass
cutting before 7 October 2015. The blue line represents the rainfall (daily precipitation in mm day1/. The L2C SNR data acquired by the
29.4 m antenna with a 10 s sampling interval were used to retrieve VSM during TS1 (vegetation senescence and after cutting). Boxes: 25th–
75th percentiles; bars: maximum (minimum) values below (above) 1.5 IQR (interquartile range; corresponding to the 25th–75th percentile
interval); dots: data outside the 1.5 IQR interval.
Figure 5. Scatter plots of daily mean in situ VSM observations at a
depth of 5 cm vs. GNSS VSM retrievals ( ND220): from (a)L2C
SNR data, and (b)L5 SNR data. The SNR data acquired by the
29.4 m antenna with a 10 s sampling interval during TS1 were used.
5 Discussion
5.1 Why should growth and senescence be treated
separately?
While VSM could not be retrieved by Zhang et al. (2017)
after wheat tillering, i.e., for plant heights larger than 0.2 m,
we could retrieve scaled VSM values throughout time seg-
ments of the grass growing and senescence phases. However,
retrieving VSM values in m3m3was challenging and re-Table 4. Soil moisture scores between daily mean in situ VSM ob-
servations at a depth of 5 cm and GNSS VSM retrievals (either L2C
or L5 or both) during TS3 and TS4 (SNR data from the 3.3 m an-
tenna with 1 s sampling interval from 8 June to 6 October 2016).
The Zhang et al. (2017) method is used for separated time segments,
and also for merged time segments. MAE, RMSE, SDD and Nare
defined in Table 2.
Time segments Separate TS3 Merged TS3
and TS4 and TS4
Signal L2C L5 L2C and L5 L2C
N 121 121 121 121
Mean bias (m3m3/0.010 0.011 0.010 0.025
MAE (m3m3/ 0.019 0.018 0.018 0.044
RMSE (m3m3/ 0.027 0.027 0.027 0.050
SDD (m3m3/ 0.026 0.025 0.025 0.044
R20.55 0.60 0.57 0.03
quired a seasonal rescaling to account for vegetation effects
(see Fig. 7).
Section 4.3 showed that the VSM retrieval from SNR
data during TS3 is of lower quality than during TS4, i.e.,
after cutting the vegetation. Not all satellite tracks can be
used (Table 1) and skill scores are systematically worse (Ta-
ble 2). Moreover, Fig. 6 shows that a specific calibration (see
Sect. 3.2) of the retrieval method is needed for TS3. Because
the retrieval method is based on the minimum phase which
is related to the vegetation height and density, the lack of a
priori information about this factor is likely to trigger marked
discrepancies.
Based on Eq. (1), SNR amplitude Aand SNR phase 
are calculated using the LSE method, assuming that the rel-
ative antenna height ( H0/for each satellite track is constant
across dates and ignoring the impact of the elevation angle
change inA(Larson et al., 2008, 2010). The median value
of the derived effective antenna height from the SNR data
by the Lomb–Scargle periodogram method is considered as
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1940 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
Figure 6. Median of the daily VSM retrievals (red lines) with (a)separate TS3 and TS4 and removing vegetation effects, and (b)merged
TS3 and TS4, using L2C SNR data (from the 3.3 m antenna with 1 s sampling interval) during TS3 and TS4 (from 8 June to 6 October 2016).
Daily mean in situ VSM observations at a depth of 5 cm are shown by the green lines. The blue lines represent the rainfall (daily precipitation
in mm day1/. The black/orange dashed line indicates the grass cutting before 9 July 2016.
Figure 7. Scatter plots of daily mean in situ VSM observations
(ND121) at a depth of 5 cm vs. GPS L2C retrievals: (a)after vege-
tation effect correction (with separate TS3 and TS4, corresponding
to Fig. 6a) and (b)before correction (with merged TS3 and TS4,
corresponding to Fig. 6b). The L2C SNR data acquired by the 3.3 m
antenna with 1 s sampling interval were used. Black dots represent
the retrievals ( ND31) during TS3; red dots ( ND90) represent the
retrievals during TS4 (after grass cutting).
the value of the a priori H0for each satellite track (Chew et
al., 2016). This hypothesis is only valid for the dates when
the surface is not covered with snow or dense vegetation. Al-
though the real effective antenna height may vary from one
day to another, a constant value of H0is used through time
for a given satellite track. This assumption is made in order
to ensure the consistency of time series across dates. Thea prioriH0value affects the sinusoid fit, and might cause a
systematic bias of Aandacross dates. When there are sig-
nificant vegetation effects, the vegetation height affects the
effective antenna height (Zhang et al., 2017). This explains
why the obtained VSM retrieval time series with merged time
segments are not continuous (Fig. 6). Segment by segment
normalization is useful for removing such systematic biases
and to remove vegetation effects from VSM retrieval. It can
be considered as a vegetation correction method.
Figure 7 illustrates the improvement associated with the
vegetation correction. The systematic bias caused by the mis-
match inH0is shown. Without vegetation correction, the
VSM retrievals do not correlate with the observed VSM
(R2D0.03). On the other hand, the vegetation correction re-
moves the differences between TS3 and TS4 caused by using
the sameH0in both time segments, and the VSM retrievals
are more consistent ( R2D0.55). Figure 7 clearly shows that
using GNSS-IR to retrieve VSM values in m3m3when sig-
nificant changes in vegetation effects occur is challenging.
The need to harmonize VSM retrievals from TS3 and TS4 is
related to the cutting of the grass when vegetation effects are
pronounced ( Anorm is lower than 0.78, see Fig. 1).
As a consequence, monitoring VSM using a GNSS net-
work could be difficult when vegetation effects are notice-
able. However, we show that one may use the information
fromAnorm data to define time segments for which scaled
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S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations 1941
VSM time series are valid. For example, grass cutting can be
detected from the rapid rise in Anorm value.
In this study, we used independent VSM in situ observa-
tions to harmonize the VSM time series across TS3 and TS4.
Since in situ observations are not extensively available, this
technique is not readily applicable at other sites. In practice,
one could possibly use a data assimilation framework able
to integrate the VSM retrievals into model VSM simulations
such as those produced by the ISBA land surface model (Al-
bergel et al., 2017). In such land data assimilation systems
(LDASs), a complex seasonal rescaling of VSM observa-
tions is needed (Reichle and Koster, 2004; Draper and Re-
ichle, 2015), especially when the observations are not prop-
erly decontaminated from vegetation effects (Stoffelen et al.,
2017). Our results show that using this rescaling technique
would be feasible since the ISBA simulations of VSM corre-
late well with the retrieved VSM (Fig. 8). The main reason
for this result is that ISBA is forced by the SAFRAN atmo-
spheric analysis, incorporating a large number of in situ rain
gauge observations (Sect. 2.1). This is another way of using
ancillary in situ observations.
5.2 Are grassland and cropland vegetation effects
comparable?
The effects of vegetation on GNSS SNR data are threefold:
from plant height, aboveground biomass and litter. At the
end of the growing season, plant height and aboveground
biomass values can be much larger for annual crops than for
grass. On the other hand, while litter is usually missing dur-
ing the growing phase of annual crops, litter is characteristic
of grasslands (Quested and Eriksson, 2016).
Over our grassland site, the measured grass height at the
end of the growing period is 30 cm on 22 June 2016. The
grass height is then only slightly larger than one GNSS
wavelength (25 cm for L5). The simulated aboveground
biomass by ISBA is shown in Fig. 2. During the summer
of 2015, the maximum aboveground biomass slightly ex-
ceeds 0.25 kg m2. This short period coincides with Anorm
values slightly lower than the 0.78 threshold. In June 2016,
before the cutting, the aboveground biomass ranges between
0.25 and 0.50 kg m2. The corresponding Anorm drops be-
low 0.78, showing that vegetation effects are significant.
The simulated green aboveground biomass is 0.39 kg m2
on 22 June 2016, very close to the observed value of
0.37 kg m2. The litter dry mass is not simulated but a value
of 0.29 kg m2is obtained from in situ observation at the
same date, consisting of 0.23 kg m2of dead leaf material
and of 0.06 kg m2of decomposed leaves. This represents
44 % of the total aboveground organic material.
Zhang et al. (2017) showed that over a wheat field the
vegetation gradually replaces the soil as the dominant re-
flecting surface when plant height becomes comparable to,
or larger than, one wavelength, even at relatively low values
of the aboveground biomass (an estimate of 0.08 kg m2isgiven). In such conditions the Anorm drops below 0.78 and
the SNR phase is no longer related to soil moisture (Zhang et
al., 2017).
This study shows that VSM retrieval above these biomass
and plant height thresholds are feasible for grass. However,
a limited number of suitable tracks, less affected by vegeta-
tion, have to be selected using the grass cutting event (see
Sect. 3.2). In real practical applications, such tracks are not a
priori known and retrieving VSM would be challenging when
vegetation effects are significant.
5.3 Does the litter affect the GNSS VSM retrieval?
In order to analyze the possible impact of litter on the differ-
ences between GNSS VSM and either in situ VSM or ISBA
VSM, in situ VSM observations at 5 cm, in situ VSM ob-
servations at 1 cm and ISBA VSM simulations at 1 cm are
compared with the GNSS VSM retrievals. The GNSS VSM
is retrieved applying the Zhang et al. (2017) method to both
L2C and L5 SNR data, and the vegetation effects are re-
moved from the retrievals. For ensuring the comparability of
these various soil moisture estimates, GNSS retrievals, ISBA
1 cm simulations, in situ 1 cm observations and in situ 5 cm
observations are scaled to dimensionless values.
Figure 8 shows a comparison between the four scaled
VSM time series during TS3 and TS4. Soil moisture val-
ues tend to increase drastically during precipitation events.
Most of the VSM peaks observed in 1 cm in situ observa-
tions are also found in 5 cm observations, except for 5–7 July
and 5 August 2016. On the other hand, GNSS VSM peaks
can occur while in situ VSM observations do not display
any response to rain, e.g., on 8–14, 25 and 30 June, 30–
31 July, and 29 August 2016. A contrasting result is found
comparing GNSS and ISBA VSM estimates, which peak,
more often than not, at the same time. As a consequence,
the GNSS VSM estimates correlate much better with ISBA
VSM (R2D0.82) than with in situ VSM observations at 1 cm
(R2D0.63) and at 5 cm ( R2D0.57).
The scores resulting from the comparison between scaled
VSM validation data and GNSS VSM estimates are sepa-
rately recorded in Table 2 for each time segment. The highest
correlations are with ISBA simulations at 1 cm, for all time
segments. The scores based on in situ VSM observations at
1 cm are similar to those based on in situ VSM observations
at 5 cm. For TS4, the correlation with in situ VSM observa-
tions at 1 cm is much higher than with those at 5 cm. The
main difference between observations at 1 cm and at 5 cm is
that the former respond to rainfall events more rapidly. This
is illustrated by Fig. 8 for events occurring after 9 July 2016
(TS4). The differences observed between GNSS VSM esti-
mates and in situ VSM observations at 1 cm can be explained
by the interception of light rain by the litter. Water contained
in the litter tends to directly reflect the GNSS signal and to
prevent the GNSS signal from further penetrating into the
soil. This difference is not observed with ISBA simulations
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1942 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
Figure 8. (a) Scaled GNSS VSM retrieval time series (red line, ND121) using both L2C and L5 SNR data during separate TS3 and TS4,
scaled ISBA 1 cm simulations (green line) and scaled in situ VSM observations at 1 cm (grey solid line) and at 5 cm (grey dashed line). The
SNR data acquired by the 3.3 m antenna with 1 s sampling interval were used during TS3 and TS4. The black/orange dashed line indicates
the grass cutting of 9 July 2016. (b, c, d) Scatter plots of scaled ISBA VSM simulations at 1 cm, scaled in situ VSM observations at 1 cm and
scaled in situ VSM observations at 5 cm vs. scaled GNSS VSM retrievals, respectively.
Figure 9. L2C SNR VSM retrieval time series using GPS PRN 10 ascending tracks with different sampling intervals: (a)1 s,(b)10 s
and(c)100 s. The L2C SNR data acquired by the 3.3 m antenna during TS4 (after grass cutting in July 2016) were used. Their corresponding
scatter plots are shown in (d),(e)and(f), respectively. Daily mean in situ VSM observations at a depth of 5 cm (black lines) are shown in the
left subfigures, and the blue lines represent the daily precipitation in mm day1.
because the litter is not implemented in this version of the
ISBA model. The good correspondence between ISBA and
GNSS VSM estimates can be considered as an artifact: ISBA
simulates a VSM peak which does not exist, and the GNSS
SNR data are sensitive to a sudden increase in the litter wa-ter content and/or to the rain intercepted by the litter or by
the leaves. Another demonstration of the impact of the litter
effects can be made, removing rainy days from TS4. The R2
score in Table 2 then rises from 0.65 to 0.83.
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S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations 1943
Figure 10. Two examples of L2C SNR data sets (from the GPS PRN
10 ascending tracks) acquired by the 3.3 m antenna at two contigu-
ous dates: (a)28 July and (b)29 July 2016. SNR data with three
different sampling intervals at 1, 10 and 100 s are shown in black,
orange and red lines, respectively.
5.4 Does the sampling interval affect the VSM
retrieval?
When the antenna height increases, the size of the observ-
ing areas is extended. But at the same time the period of the
SNR data decreases (Eq. 1), and a smaller sampling interval
is needed to ensure the usability of the SNR data for VSM
retrieval. On the other hand, because the SNR period from a
high antenna is much smaller, it is possible to use smaller
elevation angle ranges and shorter observing time periods
per track. The number of complete SNR waveforms is much
larger than using a low antenna. We investigate the impact of
under-sampling for the 3.3 m antenna and for the 29.4 m an-
tenna. It should be noted that in the examples illustrated by
Figs. 9, 10, and 11 the SNR frequency is always lower than
the Nyquist frequency.
First, an example of the impact of the sampling interval
for the 3.3 m antenna is shown in Fig. 9. L2C SNR obser-
vations (ND90) from GPS PRN 10 ascending tracks dur-
ing TS4 (after grass cutting) are used to retrieve VSM using
various sampling intervals. The Zhang et al. (2017) method
is used based on the original 1 s sampling interval and on
degraded sampling intervals of 10 and 100 s. During TS4,
Anorm is above 0.78 (Fig. 2), which also shows that vegeta-
tion effects are not significant (Chew et al., 2016). This is
a rather dry period, but a few rainfall events are observed.
They cause changes in the in situ VSM observations at 5 cm,
which range between 0.07 and 0.21 m3m3. In Fig. 9, the
highest correlation ( R2D0.68) is for the smallest sampling
intervals (1 and 10 s), and the lowest correlation ( R2D0.55)Table 5. Soil moisture scores from the comparison between daily
mean in situ VSM observations at a depth of 5 cm and GNSS VSM
retrievals during TS4 (after grass cutting, from 9 July to 6 Octo-
ber 2016). The L2C SNR data from GPS PRN 10 ascending tracks
were used, which were acquired by the 3.3 m antenna. MAE is the
mean absolute error, RMSE is the root mean square error and SDD
is the standard deviation of difference.
Sampling interval 1 s 10 s 100 s
N 90 90 90
Mean bias (m3m3/0.009 0.008 0.012
MAE (m3m3/ 0.013 0.013 0.018
SDD (m3m3/ 0.018 0.018 0.021
RMSE (m3m3/ 0.020 0.020 0.025
R20.68 0.68 0.55
is observed for the largest sampling interval (100 s). The
corresponding statistical scores, resulting from the compar-
ison between in situ VSM observations at a depth of 5 cm
and GNSS VSM retrievals are shown in Table 5. As for
R2, RMSE and SDD for 1 and 10 s sampling intervals are
similar (RMSED0.020 m3m3and SDDD0.018 m3m3/,
and denote lower skill for the 100 s sampling interval
(RMSED0.025 m3m3and SDDD0.021 m3m3/. Much
more day-to-day variability is observed in the retrievals us-
ing a 100 s sampling interval. The impact on the SNR infor-
mation content of degrading the sampling interval may vary
from one day to another. This is illustrated by Fig. 10 for two
contiguous days (28 and 29 July 2016). The under-sampling
effect at 100 s is more pronounced on 29 July than on 28 July.
More pit and peak information is missing on 29 July. This
tends to trigger a sharp decrease in the retrieved VSM val-
ues. On the other hand, under-sampling tends to increase the
retrieved VSM on 28 July. As a result, the retrieved VSM
drops by0.050 m3m3from 28 to 29 July while the in situ
VSM at 5 cm only changes by 0.004 m3m3.
SNR amplitudes are also affected by the sampling interval
in TS4. For 29 July 2016, the estimated SNR amplitude is
26 V V1for both 1 and 10 s sampling intervals, but only
18 V V1for the 100 s sampling interval. For this example
track data acquired by the 3.3 m antenna, the SNR period
is about 330 s. There are about 330, 33 and 3 samples in a
complete waveform for 1, 10 and 100 s sampling intervals,
respectively. Obviously, the 100 s sampling interval does not
provide enough samples to retrieve VSM. On the other hand,
using a 10 s sampling interval is sufficient for the SNR data
acquired by the 3.3 m antenna after cutting the grass.
For the 29.4 m antenna, the sensitivity to the sampling in-
terval is more critical. Figure 11 shows the SNR oscillations
for the GPS PRN 25 ascending track. The SNR period is only
about 38 s. With 10 s sampling interval, three or four sam-
ples are available for a complete waveform. This is about the
same situation as for the 100 s sampling interval for the 3.3 m
antenna. Figure 11a shows that pit and peak information is
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1944 S. Zhang et al.: Deriving surface soil moisture from reflected GNSS signal observations
Figure 11. Two examples of L2C SNR data sets (from the GPS
PRN 25 ascending tracks) acquired by the 29.4 m antenna at two
contiguous dates: (a)18 March 2016 (with 10 s sampling interval)
and(b)19 March 2016 (with 1 s sampling interval).
missing on 18 March 2016 with respect to the 1 s sampling
interval data on the next day in Fig. 11b. Nevertheless, Ta-
ble 2 shows that the 10 s under-sampling had a limited im-
pact on VSM retrievals during TS1 since the best scores are
observed during this segment. This paradoxical result can be
explained by the prior use of the in situ VSM data to se-
lect the satellite tracks and the satellite elevation angles (see
Sect. 2.2).
6 Conclusions
GPS L2C and L5 SNR data were obtained at a grassland site
in southwestern France during a period of 15 months. A di-
mensionless scaled wetness index was derived from the SNR
observations based on the GNSS-IR technique, using indis-
criminately L2C or L5 signals. Surface soil moisture was
derived from this scaled wetness index. We show that accu-
rately estimating soil moisture in units of m3m3over such
a densely vegetated site is challenging. In order to efficiently
limit the impact of perturbing vegetation effects, the grass
growth period and the senescence period should be treated
separately. While the vegetation biomass effect can be cor-
rected for, the litter water interception influences the observa-
tions and cannot be easily accounted for. Overall, a precision
of 0.035 m3m3is achieved for the whole meadow grow-
ing cycle, and of 0.018 m3m3after grass cutting. A suitable
sampling interval should be used, dependent on the antenna
height and elevation angle range. Positioning the antenna
high up (at 29.4 m in this study) in order to observe a larger
area enhances the impact of under-sampling. The signal sam-pling interval should be better than 10 s in this case. More
experiments over contrasting vegetation types are needed to
further examine the feasibility of integrating GNSS-IR re-
trievals in land surface models. Land data assimilation sys-
tems are usually used for satellite observations but can also
integrate ground observations. In such a framework, model
simulations of vegetation biomass and soil moisture could
be combined with GNSS-IR retrievals. Proposing a complete
protocol to apply this method to local GNSS antennas would
require observations at a large number of sites. More research
is needed to use GNSS-IR in densely vegetated areas.
Data availability. The data used in this work are available for re-
search from the corresponding author.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. The work of Sibo Zhang was supported by the
STAE (Sciences et Technologies pour l’Aéronautique et l’Espace)
foundation, in the framework of the PRISM (Potentialités de la
Réflectométrie GNSS In-Situ et Mobile) project. The authors would
also like to thank Eric Moulin and Joel Barrié (CNRM) for their
technical support during the field campaign and Anne Belleudy and
Diane Tzanos (CNRM) for performing biomass observations.
Edited by: Miriam Coenders-Gerrits
Reviewed by: two anonymous referees
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