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We study the functional relationship between the dielectric constant of soil-water mixture and penetration depth of microwave signals into the ground at different frequency (L&S) band and incidence angles. Penetration depth of microwave signals into the ground depends on the incidence angle and wavelength of radar pulses and also on the soil properties such as moisture content and textural composition. It has been observed that the longer wavelengths have higher penetration in the soil but the penetration capability decreases with increasing dielectric behaviour of the soil. Moisture content in the soil can significantly increase its dielectric constant. Various empirical models have been proposed that evaluate the dielectric behaviour of soil-water mixture as a function of moisture content and texture of the soil. In this analysis we have used two such empirical models, the Dobson model and the Hallikainen model, to calculate the penetration depth at L- and C-band in soil and compared their results. We found that both of these models give different penetration depth and show different sensitivity towards the soil composition. Hallikainen model is more sensitive to soil composition as compared to Dobson model. Finally, we explore the penetration depth at different incidence angle for the proposed L- and S-band sensor of upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission by using Hallikainen empirical model. We found that the soil penetration depth of SAR signals into the ground decreases with the increase in soil moisture content, incident angle and frequency.
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ANALYSIS OF THE EFFECT OF INCIDENCE ANGLE AND MOISTURE CONTENT ON
THE PENETRATION DEPTH OF L- AND S-BAND SAR SIGNALS INTO THE GROUND
SURFACE
Abhilash Singh1
, Ganesh Kumar Meena1, Shashi Kumar2, Kumar Gaurav1
1Indian Institute of Science Education and Research, Department of Earth and Environmental Sciences, Bhopal, India
- (sabhilash,gkmeena,kgaurav)@iiserb.ac.in
2Indian Institute of Remote Sensing, ISRO, Dehradun 248001, India. - shashi@iirs.gov.in
Commission V, WG SS: Natural Resources Management
KEY WORDS: Soil moisture, Penetration depth, Volumetric moisture content, Soil texture, Incidence angle, SAR
ABSTRACT:
We study the functional relationship between the dielectric constant of soil-water mixture and penetration depth of microwave signals
into the ground at different frequency (L&S) band and incidence angles. Penetration depth of microwave signals into the ground
depends on the incidence angle and wavelength of radar pulses and also on the soil properties such as moisture content and textural
composition. It has been observed that the longer wavelengths have higher penetration in the soil but the penetration capability decreases
with increasing dielectric behaviour of the soil. Moisture content in the soil can significantly increase its dielectric constant. Various
empirical models have been proposed that evaluate the dielectric behaviour of soil-water mixture as a function of moisture content
and texture of the soil. In this analysis we have used two such empirical models, the Dobson model and the Hallikainen model, to
calculate the penetration depth at L- and C-band in soil and compared their results. We found that both of these models give different
penetration depth and show different sensitivity towards the soil composition. Hallikainen model is more sensitive to soil composition
as compared to Dobson model. Finally, we explore the penetration depth at different incidence angle for the proposed L- and S-band
sensor of upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission by using Hallikainen empirical model. We found that the
soil penetration depth of SAR signals into the ground decreases with the increase in soil moisture content, incident angle and frequency.
1. INTRODUCTION
Penetration depth of microwave signals into the ground vary sig-
nificantly with the moisture available in the soil. Soil moisture
is a measure of temporary storage of water contained in the soil
pores (Srivastava et al., 2006). It is a major component of soil that
supports the growth of plants and helps to predict droughts. Each
plant species requires a different range of soil-water for efficient
absorption of water and nutrients. Some of them require dry roots
with minimum soil moisture, whereas some require wet roots to
stabilize the plant. On the basis of the soil moisture content, we
can easily estimate the type of plant species that is expected to
grow in a habitat. The first 200 cm of soil from the ground surface
is classified as “root zone soil moisture” and serves as an impor-
tant component of the Earth’s critical zone. Moisture in this zone
describes the water that is available for crops and plants. During
drought, there is a deficit of moisture in the root zone which re-
sults in the depletion of crop production. Moisture in root zones
also regulates the soil temperature that helps in the chemical and
biological activities of the soil. Therefore a continuous monitor-
ing of soil moisture in the root zone is required on regular basis
to improve the crop yield forecasting and irrigation planning (SU
et al., 2014).
There are generally two approaches that are used to quantify soil
moisture namely in-situ and remote sensing approach. In-situ
measurement involves various point measurement methods to es-
timate the soil moisture. Gravimetric methods of soil moisture
estimation is most widely used in-situ approach and considered
Abhilash Singh
as the benchmark for calibration of all other soil moisture estima-
tion techniques. In-situ techniques are useful for the estimation
of soil moisture on a local scale, it is less accurate when applied
to large spatial scale and it requires large number of samples to
characterize an area. Recent advancement in the satellite and air-
borne remote sensing enable the mapping of soil moisture at re-
gional as well as global scale with higher accuracy. Soil moisture
estimation either by satellite or airborne remote sensing depends
on the quantification of the electromagnetic (EM) energy that has
been either emitted or reflected from the soil surface. One of
such remote sensing techniques is microwave imaging that uses
EM radiation of greater wavelength (1 mm 1 m) as compared
to visible and infra-red radiation. SAR sensor is the most com-
monly used microwave imaging sensor. It transmits signals in
the microwave band of EM spectrum and records the backscat-
tered value. This backscattered value is related to the dielectric
constant which can be used to estimate soil moisture.
Several models have been proposed to formulate the dielectric
behaviour of soil-water mixtures in the microwave region of EM
spectrum. For example, Dobson et al., (1984) examined the ef-
fect of soil texture on microwave emission and backscattering.
They have established an empirical relation for the estimation of
real and imaginary part of the dielectric constant at frequencies
1.4 and 5 GHz (Dobson et al., 1984). Later, Hallikainen et al.,
(1985) expressed the dielectric behaviour of soil-water mixture
as a function of moisture content and soil texture. They estab-
lished an empirical relation for dielectric constant of soil samples
measured at frequencies between 1.4 and 18 GHz (Hallikainen et
al., 1985).
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018
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197
We recalculate the soil penetration depth at L- and C-band using
both these empirical models (Dobson et al., 1984, Hallikainen et
al., 1985) at different incident angles. We then compare the sen-
sitivity of these two models in estimating the penetration depth
into the ground in L-band and with varying soil textural compo-
sition. Finally, we have calculated the ground penetration depth
for L- and S-band at different incidence angle using Hallikainen
empirical model.
2. THEORITICAL BACKGROUNG AND METHOD
2.1 Dobson empirical model
Dobson et al., (1984) proposed an empirical model that relates
soil dielectric constant and volumetric moisture content. The
Dobson model reads;
=a0+ (a1+b1S+c1C)w
+(a2+b2S+c2C)w2
+(a3+b3S+c3C)w3
(1)
where Sand Cis the percentage of sand and clay respectively. w
is the volumetric water content (VWC) in [m3/m3]. Other terms
a0,a1,a2,b1,b2,b3,c1,c2, and c3are constant factors and
calibrated for real (0) and imaginary part (00) of the dielectric
constant at frequencies 1.4 GHz (L-band) and 5 GHz (C-band).
For frequency 1.4 GHz, the dielectric constant (real and imagi-
nary part) reads:
0
1.4GHz = 2.37 + (5.24 + 0.55S0.15C)w
+(146.04 0.74S0.85C)w2(2)
00
1.4GHz = 0.06 + (6.69 + 0.0367S0.0620C)w
+(16.17 0.30S+ 0.27C)w2(3)
Similarly, for frequency 5 GHz, the dielectric constant (real and
imaginary part) reads:
0
5GHz = 2.46 + (13.07 + 0.14S0.44C)w
+(132.11 + 0.38S+C)w2
+(103.86 1.16S0.49C)w3
(4)
00
5GHz = 0.12 + (4.7 + 0.0646S0.2356C)w
+(30.65 0.61S+ 1.12C)w2
+(34.29 + 1.36S1.16C)w3
(5)
2.2 Hallikainen empirical model
Hallikainen et al., (1985) proposed a quadratic polynomial fitting
model (7) based on different textural composition of the soil to
compute the dielectric constant of soil-water mixture. The coef-
ficients of the equation are calibrated for entire RADAR band but
for a discrete set of frequencies bands such as, (1.4 GHZ, L), (4
GHz, S), (6 and 8 GHz, C), (10 and 12 GHz, X) and (14, 16 and
18 GHz, Ku). The Hallikainen model reads;
= (a0+a1S+a2C)
+(b0+b1S+b2C)w
+(c0+c1S+c2C)w2
(6)
where Sand Crepresents the percentage of sand and clay re-
spectively. Other terms a0,a1,a2,b1,b2,b3,c1,c2, and c3are
constant factors and calibrated for real and imaginary part of the
dielectric constant for frequencies ranging from 1.4GHz to 18
GHz.
For 1.4 GHz (L-band), the Hallikainen equation to compute the
dielectric constant (real and imaginary) reads;
0
1.4GHz = (2.862 0.012S+ 0.001C)
+(3.803 + 0.462S0.341C)w
+(119.006 0.500S+ 0.633C)w2
(7)
00
1.4GHz = (0.356 0.003S0.008C)
+(5.507 + 0.044S0.002C)w
+(17.753 0.313S+ 0.206C)w2
(8)
Similarly, for 6 GHz (C-band), the Hallikainen equation to com-
pute the dielectric constant (real and imaginary) reads;
0
6GHz = (1.993 + 0.002S+ 0.015C)
+(38.086 0.176S0.633C)w
+(10.720 + 1.256S+ 1.522C)w2
(9)
00
6GHz = (0.123 + 0.002S+ 0.003C)
+(7.502 0.058S0.116C)w
+(2.942 0.452S+ 0.543C)w2
(10)
Both these empirical models (Dobson et al., 1984, Hallikainen et
al., 1985) are valid only up to a VWC of 50% (Nolan and Fatland,
2003).
2.3 NISAR Mission: An Overview
The NISAR mission is a joint collaboration between the National
Aeronautics and Space Administration (NASA) and the Indian
Space Research Organization (ISRO). It is a multi-disciplinary
radar mission. The complete NISAR system consists of a dual
frequency (L- and S-band), fully polarimetric radar, with an imag-
ing swath greater than 240 km. The combination of L- and S-
band imagery will allow estimating depth dependent soil mois-
ture variability and will help to isolate and remove soil moisture
phase noise in targeted deformation interferograms. The pres-
ence of L-band SAR sensor will ensure sensing of soil moisture
from deeper layers as compared to shorter wavelengths. Accord-
ing to the NISAR mission science user handbook, the frequency
used for L- and S-band is 1.25 GHz and 3.22 GHz respectively
with variation in the incident angle of 330to 470for both L-
and S-band (Falk, 2018). In this study, we have considered a
frequency of 1.4 GHz in L-band and 4 GHz in S-band with vari-
ation in the incident angle of 330to 470for both L- and S-band.
This consideration is because of the constraint on the empirical
model. NISAR system will provide complete global coverage
in a 12 day exact repeat period to generate interferometric time-
series and perform systematic global mapping of the changing
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018
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198
surface of the Earth. It is the first NASA radar mission that stud-
ies systematically and globally about the solid Earth, ecosystems
and ice masses. It will measure ice mass, the land surface mo-
tions and changes, ecosystem disturbances, and biomass explain-
ing the underlying processes. These measurements will improve
the forecast and assessment of changing ecosystems, the response
of ice sheets, and natural hazards like floods, drought etc.
2.4 Soil moisture and penetration depth of Microwave pulses
The soil may be homogeneous or non-homogeneous. In homo-
geneous soil, we assume uniform properties with depth whereas
non-homogeneous soil have layered structures having transmis-
sion losses. For simplicity, we will adhere to homogeneous soil.
The power (Pp)of an EM wave of a known polarization (p), in
homogeneous soil, transmitted at a depth dis given by (Bruckler
et al., 1988)
Pp=Pop γe2jKzd(11)
where γrepresents the transmissivity at the air-soil interface, jis
the complex number, Pop is the power of the incident EM wave
with known polarization (p). Kzis the zcomponent of wave
number in soil, given by (Ulaby et al., 1981)
Kz=qω2µ01K2
0sin2θi(12)
where θiis the incident angle of the EM wave at the air-soil in-
terface, K0is the wave number given by 2π,ωis the angular
frequency, 0is permittivity of the free air, 1is the relative di-
electric constant, µ1=µ0represent the magnetic permeability.
Depth of penetration, δp, of an EM wave is defined as the depth at
which the power of the incident EM is reduced by 1/e = (0.37)
or to 37%. This penetration depth is significantly different from
skin depth, δa. The skin depth is defined as the depth at which
the amplitude of EM is reduced by 1/e = (0.37) or to 37%.δpis
estimated by assuming a homogeneous soil profile at nadir inci-
dence angle and it only considers the absorption by the soil. The
absorption is because of the conductive properties of soil. The
conductive properties of the soil reduces the intensity of the EM
wave. This reduced energy is converted to another form, such as
heat, and not backscattered. This loss depends on frequency, ,
ratio of 00/0(loss tangent:tanδ) and incident angle θi. Penetra-
tion depth is reached when
Pp
(Pop γ)=e1(13)
Comparing Equation 13 with Equation 11 we get;
2jKzd= 1 (14)
from Equation 14 and Equation 12 we get;
2π
λ|Im[]|d=1
2(15)
The value of dfor which Equation 15 and Equation 14 hold is δp.
δpcan be expressed as
δp=λ
4πr[(1 + ( 00
0)2)1/21] 0
2(16)
Assuming that tanδis less than 0.1(e.g 00/00.1 ). δpcan be
expressed as
δp=λ0
2π00 (17)
where δpand λboth are in millimetres. Due to the huge differ-
ence between the permittivity or dielectric constant of soil and
water, and because the amount of water in soil is variable, soil
moisture dominantly controls the permittivity of the soil and thus
penetration depth as well.
δpSinθί
δpCosθί
δp
Soil
θί
θr
Nadir
Figure 1. Relationship between incidence angle and penetration
depth
0 5 10 15 20 25 30 35
Volumetric Water Content in %(w)
0
100
200
300
400
500
600
Depth of penetration [mm]
For S = 82% and C = 01% (Soil type: Sand) at Nadir
Dobson et al. for L-Band (1.4 GHz)
Hallikainen et al. for L-Band (1.4 GHz)
Figure 2. δpVs VWC For S = 82% and C = 01% (Soil type:
Sand) at Nadir
2.5 Effect of incidence angle on penetration depth of Mi-
crowave pulses
Equation 17 represents the penetration depth at nadir incidence
angle (e.g θi= 0). But the penetration depth changes with
change in incidence angle. Using vector decomposition, we can
extend the Equation 17 to estimate the penetration depth at dif-
ferent incidence angle.
From Figure 1, we have
δ0
pδpcosθr(18)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018
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199
0 5 10 15 20 25 30 35
Volumetric Water Content in %(w)
0
100
200
300
400
500
600
Depth of penetration [mm]
For S = 65% and C = 04% (Soil type: Sandy loam) at Nadir
Dobson et al. for L-Band (1.4 GHz)
Hallikainen et al. for L-Band (1.4 GHz)
Figure 3. δpVs VWC For S = 65% and C = 04% (Soil type:
Sandy loam) at Nadir
Figure 4. δpVs VWC For S = 07% and C = 31% (Soil type:
Silty clay loam) at Nadir
where δprepresents the maximum depth within a medium at nadir
incidence angle, δ0
prepresents the penetration depth at incidence
angle, θias from satellite, the incidence angle is different from
Nadir and θrrepresents the angle of refraction.
Using Snell’s law and 00 0,θiθr. The Equation 18 can be
written as
δ0
pδpcosθi(19)
Equation 19 allows to calculate the penetration depth for different
incident angle.
3. RESULTS AND CONCLUSION
Using the equations (1-19), we can conclude that the relationship
between penetration depth VWC is non-linear and varies consid-
erably with the SAR wavelength.
0 5 10 15 20 25 30 35
Volumetric Water Content in %(w)
0
100
200
300
400
500
600
700
800
900
Depth of penetration [mm]
For S = 07% and C = 31% (Soil type: Silty clay loam) at Nadir
Dobson et al. for C-Band (5 GHz)
Dobson et al. for L-Band (1.4 GHz)
Hallikainen et al. for C-Band (6 GHz)
Hallikainen et al. for L-Band (1.4 GHz)
Figure 5. δpVs VWC For S = 07% and C = 31% (Soil type:
Silty clay loam) at Nadir
0 5 10 15 20 25 30 35
Volumetric Water Content in %(w)
0
50
100
150
200
250
300
350
400
450
Depth of penetration [mm]
For S = 93% and C = 0.8% (Soil type: Sand) at Nadir
Hallikainen et al. for L-Band (1.4 GHz) at i=00
Hallikainen et al. for S-Band (4 GHz) at i=00
Figure 6. δpVs VWC For S = 93% and C = 0.8% (Soil type:
Sand) at Nadir
3.1 Comparison of Dobson and Hallikainen models
We have calculated the penetration depth of L-band (1.4 GHz)
SAR sensor into the ground by using the Dobson and Hallikainen
empirical models. In doing so, we have considered three dif-
ferent types of soil samples, sandy (with sand = 82% and clay
= 1%), sandy loam (sand = 65% and clay = 4%) and silty clay
loam (sand = 7% and clay = 31%). The VWC of the soil varies
from 0 (completely dry) to 35%. We have performed this analysis
for the different incidence angles θi= 00(nadir), θi= 330and
θi= 470). Fig. (2-4) show the penetration depth as a function
of moisture content for three different types of soil at nadir point.
The complete results are presented in Tables (1-3).
The simulation results depict that a non-linear relationship exists
between VWC and soil penetration depth. The soil penetration
depth decreases with increase in soil moisture content. The de-
crease in the soil penetration depth is significant for the first 10%
increase in VWC, however, further increase has a reduced effect
on soil penetration depth. This trend is supported by the fact
that when moisture content in the soil increases, it leads to high
backscatter value and results in less penetration. As the water
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
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200
0 5 10 15 20 25 30 35
Volumetric Water Content in %(w)
0
100
200
300
400
500
600
Depth of penetration [mm]
For S = 93% and C = 0.8% (Soil type: Sand)
Hallikainen et al. for L-Band (1.4 GHz) at i=330
Hallikainen et al. for S-Band (4 GHz) at i=330
Hallikainen et al. for L-Band (1.4 GHz) at i=470
Hallikainen et al. for S-Band (4 GHz) at i=470
Figure 7. δpVs VWC For S = 93% and C = 0.8% (Soil type:
Sand)
Table 1. Observation Table for S = 82% and C = 01% (Soil type:
Sand) (Simulation based)
Band VWC
(%)
Incidence Angle
(in Degree)
Penetration Depth
(mm)
L[1.4 GHz]
in Dobson
empirical model
0 0 875
10 0 97
0 33 734
15 33 69
0 47 597
15 47 58
L[1.4 GHz]
in Hallikainen
empirical model
0 0 458
10 0 95
0 33 384
15 33 69
0 47 313
15 47 56
content in the soil changes from zero to non-zero, there is a sig-
nificant increase in the imaginary part of the permittivity. This
results in the decrease in soil penetration depth. The penetration
depth also decreases with increase in the incidence angle.
Tables (1-3) show that for a same set of VWC and incident an-
gle, the results for Dobson and Hallikainen models is not co-
herent in L-band (1.4 GHz). Dobson empirical model gives a
higher penetration depth in the ground as compared to the Hal-
likainen model. In Dobson model, for completely dry soil (e.g
VWC = 0%) the penetration depth is about 875, 734 and 597
mm for incidence angles θi= 00,θi= 330and θi= 470recep-
tively Table (1-3). This can also be observed from Equation (1),
if w= 0 (e.g VWC = 0%) then it becomes =a0. This means
when VWC = 0% then dielectric constant becomes constant and
it will give same penetration depth irrespective of soil texture.
We did not observe this trend in Hallikainen empirical model, be-
cause if w= 0 (e.g VWC = 0%) in Equation (6) then it becomes
= (a0+a1S+a2C)which is not constant and depends on soil
texture.
With decrease in the percentage of sand (S) in soil, the penetra-
tion depth also decreases for the same set of VWC and incident
angle in both the models. However, the rate of change is higher in
case of Hallikainen empirical model. From the above two argu-
ments, we can conclude that the sensitivity of Hallikainen empiri-
cal model is more towards the soil texture as compared to Dobson
model. Finally we conclude this subsection with an overview of
Fig. (5). In this figure we have compared penetration depth at
Table 2. Observation Table For S = 65% and C = 04% (Soil
type: Sandy loam) (Simulation based)
Band VWC
(%)
Incidence Angle
(in Degree)
Penetration Depth
(mm)
L[1.4 GHz]
in Dobson
empirical model
0 0 875
10 0 94
0 33 734
15 33 65
0 47 597
15 47 53
L[1.4 GHz]
in Hallikainen
empirical model
0 0 382
10 0 89
0 33 320
15 33 63
0 47 260
15 47 52
Table 3. Observation Table For S = 07% and C = 31% (Soil
type: Silty clay loam) (Simulation based)
Band VWC
(%)
Incidence Angle
(in Degree)
Penetration Depth
(mm)
L[1.4 GHz]
in Dobson
empirical model
0 0 875
10 0 85
0 33 734
15 33 51
0 47 597
15 47 41
L[1.4 GHz]
in Hallikainen
empirical model
0 0 657
10 0 72
0 33 551
15 33 42
0 47 448
15 47 34
both L- and C-band for silty clay loam (with S = 07% and C =
31%) at nadir. It also follows the same general trend. The pene-
tration depth for C-band is less as compared to L-band as higher
wavelength penetrates more as compared to shorter wavelength.
3.2 Comparison of L- and S-band penetration depth using
Hallikainen model
Since Hallikainen empirical model is more sensitive towards the
soil texture, we used this model to explore the penetration depth
of L- and S-band. Penetration depth at L- and S-band for sandy
soil type (with S = 93% and C = 0.8%) at nadir is shown in Fig.
(6) and at different incident angle (330and 470) is shown in Fig.
(7). From Table (4) we can observe that for dry soil or field, as the
incident angle changes from 330to 470the soil penetration depth
changes from 535 mm to 435 mm in L-band and from 137 mm to
111 mm in S-band. For 15% VWC as the incident angle changes
from 330to 470the soil penetration depth changes from 73 mm
to 59 mm in L-band and from 28 mm to 23 mm in S-band. At
Table 4. Observation Table For S = 93% and C = 0.8% (Soil
type: Sand) (Simulation based)
Hallikainen
empirical model
VWC
(%)
Incidence Angle
(in Degree)
Penetration Depth
(mm)
L-Band
at [1.4 GHz]
0 0 638
10 0 99
0 33 535
15 33 73
0 47 435
15 47 59
S-Band
at [4 GHz]
0 0 163
10 0 52
0 33 137
15 33 28
0 47 111
15 47 23
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018
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201
nadir incidence angle for a dry field, the soil penetration depth is
638 mm for L-band and 163 mm for S-band. We found that pen-
etration depth varies considerably with SAR wavelength. Pene-
tration depth is more in L-band SAR signal as compared with the
S-band SAR signal. In general, the penetration depth decreases
with increasing frequency. When the incidence angle is fixed at
330, then for first 15% increase in the VWC there is 86.35% and
79.56% decrease in soil penetration depth in L- and S-band re-
spectively whereas when the incidence angle is fixed at 470, then
for first 15% increase in the VWC there is 86.43% and 79.27%
decrease in soil penetration depth in L- and S-band respectively.
Usually, soil moisture is not constant with depth, it typically varies
with depth, and this variation results in a noticeable effect on pen-
etration depth. Although soil dielectric constant is calculated as
a function of VWC and soil texture but much remains to be done
in this regards especially if we can a incorporate soil roughness,
and the presence of organic matter in estimation of soil dielectric
constant.
ACKNOWLEDGEMENTS
This work was funded by the NASA-ISRO synthetic Aperture
Radar (NISAR) mission through grant Hyd-01. We would like to
acknowledge IISER Bhopal and IIRS Dehradun for all the neces-
sary institutional support. AS would like to thank to Department
of Science and Technology, Govt. of India for providing research
fellowship as DST-INSPIRE fellow.
REFERENCES
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APPENDIX
The complete MATLAB code is made available for all.(Download)
Revised September 2018
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-5-197-2018 | © Authors 2018. CC BY 4.0 License.
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... It has a temporal resolution of 12 days (in combination with Sentinel-1B the temporal resolution is 6 days). Microwave signals at C-band can penetrate up to 5 cm deep below the soil surface [51,52]. The Sentinel-1A level-1 data is categorised into two product types: Ground Range Detected (GRD) and Single Look Complex (SLC). ...
... To reduce the measurement uncertainty, we have calibrated the TDR, removed the organic matter from soil the soil samples, and collected soil samples in the field according to the simulated penetration depth of the C-band SAR signal in the ground [51]. Model error (or bias) is mainly due to the assumptions followed by its use. ...
Article
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Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02m3/m3 ). However, this has reduced significantly ( 0.001m3/m3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.
... The penetration depth of Sentinel-1 SAR pulses ranges between 1-5 cm depending upon the target and sensor properties. To ensure that the backscattered SAR pulses sense the same moisture content, we measured the soil moisture at a depth below 5 cm from the ground surface (Singh et al., 2018;Singh et al., 2019). We split the study area into small grids (4 km × 4 km) and then randomly selected a grid using the universal random grid sampling approach. ...
Article
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We use surface soil moisture content as a proxy to assess the effect of drainage congestion due to structural barriers on the alluvial Fan of the Kosi River on the Himalayan Foreland. We used Sentinel-1 satellite images to evaluate the spatial distribution of soil moisture in the proximity of structural barriers (i.e., road network). We applied modified Dubois and a fully connected feed-forward artificial neural network (FC-FF-ANN) models to estimate soil moisture. We observed that the FC-FF-ANN predicts soil moisture more accurately (R = 0.85, RMSE = 0.05 m3/m3, and bias = 0) as compared to the modified Dubois model. Therefore, we have used the soil moisture from the FC-FF-ANN model for further analysis. We identified the road network that traverses on the Kosi Fan horizontally, vertically, and with inclination. We create a buffer of 1 km along either side of the road. Within this, we assessed the spatial distribution of soil moisture. We observed a high concentration of soil moisture near the structural barrier, and decreases gradually as we move farther in either direction across the orientation of the road. The impact of structural barriers on the spatial distribution of soil moisture is prominent in a range between 300 to 750 m within the road buffer. This study is a step towards assessing the effect of structural interventions on drainage congestion and flood inundation.
... In the literature, P-band is assumed to be able to estimate soil moisture across the root-zone of the soil (up to approx. 2 m depth) [5,49,50]. Consequently, a wide range of Pband penetration and sensing depths can be found in the literature, from some centimeters up to one meter or more [5,39,46,51]. Following the assumption that soil depths are accessible "in the order of one half to one tenth of the [employed] wavelength [52]" [45,47,53], P-band penetration depths of 6.97 to 34.86 cm at 430 MHz (λ = 69.72 cm) are potentially realistic. ...
Article
Full-text available
A P-band SAR moisture estimation method is introduced for complex soil permittivityand penetration depth estimation using fully polarimetric P-band SAR signals. This methodcombines eigen- and model-based decomposition techniques for separation of the totalbackscattering signal into three scattering components (soil, dihedral, and volume). Theincorporation of a soil scattering model allows for the first time the estimation of complex soilpermittivity and permittivity-based penetration depth. The proposed method needs no priorassumptions on land cover characteristics and is applicable to a variety of vegetation types. Thetechnique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSScampaign (2012–2015). The estimated complex permittivity agrees well with climate and soilconditions at different monitoring sites. Based on frequency and permittivity, P-band penetrationdepths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in theliterature. Comparison of the results is challenging due to the sparsity of vertical soil in situsampling. It was found that the disagreement between in situ measurements and SAR-basedestimates originates from the discrepancy between the in situ measuring depth of the top-soil layer(0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm).
... They operate at a frequency of 5.405 GHz and measure the uninterrupted backscattered signals from the earth's surface in all weather conditions. Depending on the soil type and moisture conditions, at this frequency, the SAR signals can penetrate up to 5 cm of the topsoil surface [54,55]. Sentinel-1 satellites have a temporal resolution of 12 days, that jointly (1A and 1B) result in a 6-day repeat pass over the equator [56,57]. ...
Article
Full-text available
We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data. Keywords: surface roughness; Sentinel-1; Sentinel-2; machine learning models; AutoML; backscatter models
Article
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The assessment of water resources in soil is important in understanding the water cycle in the natural environment and the processes of water exchange between the soil and the atmosphere. The main objective of the study was to assess water resources (in 2010-2013) in the topsoil from satellite (SMOS) and in situ (ground) measurements using the SWEX_PD approach (Soil Water EXtent at Penetration Depth). The SWEX_PD is a result of multiplying soil moisture (SM) and radiation penetration depth (PD) for each pixel derived from the SMOS satellite. The PD, being a manifold of the wavelength λ 0 equal to 21 cm, was determined from the weekly SMOS L2 measurement data based on the real and imaginary part of complex dielectric constant. The SWEX_PD data were compared with soil water resources (WR) calculated from the sum of components derived from multiplication of soil moisture (SM) and layer thickness in nine agrometeorological stations located along the eastern border of Poland. Each study site consisted of seven neighbouring Discrete Global Grid pixels (nodes spaced at 15 km) including the central ones with agrometeorological stations. The study area included different types of soils and land covers. The agreement between the water resources obtained from the SWEX_PD and ground measurements (WR) was quantified using classical statistics and Bland-Altman's plots. Calibrated Layer Thickness (CLT = d bias) from 8 to 28 cm was obtained with a low values of bias (close to zero), limits of agreements, and confidence intervals for all the SWEX_PD, depending on the pixel location. The results revealed that the use of the SWEX_PD for assessing soil water resources is the most reliable approach in the study area. Additionally, the data from Bland-Altman plots and the equation proposed in these studies allowed calculation of the Equivalent Layer Thickness (ELT = d SWEX ei), which corresponds to the water resources derived from the SMOS satellite at the same time as (SM) measurements performed in the agrometeorological stations. The ranges of the mean, standard deviation, minimum, maximum, and coefficient of variation (CV) of ELT among all pixels and stations were 8.28-28.7 cm, 3.27-12.66 cm, 3.03-10.87 cm, 19.23-94.97 cm, and 24.72-98.79%, respectively. The ranges of the characteristics depended on environmental conditions and their means were close to the values of the calibrated layer thickness. The impacts of soil texture, organic matter, vegetation, and their interactive effects on the differentiation and agreement of soil water resources obtained from SWEX_PD vs. data from ground measurements in the study area are discussed. Further studies are required to address the impact of the environmental factors to improve the assessment of soil water resources based on satellite SM products (retrievals). Soil water resources play a significant role in the agriculture and the entire environment 1-4. They influence soil-atmosphere relations through exchange of energy, fluxes of water and greenhouse gases, and latent heat flux during evaporation 5-8. Consequently, they are an important variable for weather predicting and climate projection 9-11 , including forecasting extreme events 12. Monitoring the soil moisture and water resources is necessary for numerous applications such as agricultural drought assessment, irrigation scheduling, soil and crop management 13,14 , and ground water recharge 15. Soil water resources data can be acquired from ground-based measurements or globally, using satellite techniques 16,17. Satellite remote sensing facilitates investigation of large-scale areas where field observations are
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Soil salinity is one of the crucial factors which undermines agricultural production in the semi-arid region. The study was attempted for the Vellore district, one of the worst-hit semi-regions of Tamil Nadu by salinization. The Sentinel-1 data product of C-band frequency (5.36 GHz) was instrumental in the development of the model. In the present research, a semi-empirical dielectric model was proposed, and its potential in demonstrating the rate of soil salinity was validated with in-situ measurements under semi-saturated conditions (soil moisture<50%). The dielectric behavior of saline and non-saline soil was simulated by investigating the paradigm of the parameters, namely the backscattering coefficient of VV polarization, soil texture, and in-situ dielectric constant in the three-dimensional density space. The imaginary part of the dielectric constant was retrieved by simulating the dielectric loss from the partition observed between the dielectric constant of saline and non-saline soils in the third dimension. The resultant product of the proposed model has achieved the best fit (R2 = 0.85, RMSE = 1.14, Bias = −0.65) with the in-situ EC measures of sandy soil compared to other categories of soil due to the presence of soluble salts in the free water. The proposed simulation demonstrated that the dielectric behavior of saline affected soil is dependent on soil textural characteristics under the semi-saturated condition. The simulation has shown the highest prediction performance (R2 > 0.7) in quantifying the soil EC covered with short crops with a mean height of 0.5 m. This statistical significance reveals the feasibility of the C-band SAR data to quantify the rate of salinity over bare and vegetated soil.
Presentation
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Here we study the effect of soil texture, incidence angle and soil moisture content on the penetration depth of microwave pulses into the ground. In doing so, we have compared the penetration depth of SAR signals in soil using the Dobson empirical, Dobson semi-empirical and Hallikainen empirical model over samples collected at three different locations in Bhopal, Madhya Pradesh, India. We observed all these models results in different penetration depth for the same set of soil parameters at the same frequency. Further, we explored the potential of the proposed L- and S-band sensor of the upcoming NISAR mission for the estimation penetration depth in soil at different incident angles. According to the NISAR mission science user handbook, the frequency used for L- and S-band SAR systems are 1.25 GHz and 3.22 GHz respectively with variation in the incident angle of 33 degrees to 47 degrees respectively.
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Microwave remote sensing is one of the most promising tools for soil moisture estimation owing to its high sensitivity to dielectric properties of the target. Many ground-based scatterometer experiments were carried out for exploring this potential. After the launch of ERS-1, expectation was generated to operationally retrieve large area soil moisture information. However, along with its strong sensitivity to soil moisture, SAR is also sensitive to other parameters like surface roughness, crop cover and soil texture. Single channel SAR was found to be inadequate to resolve the effects of these parameters. Low and high incidence angle RADARSAT-1 SAR was exploited for resolving these effects and incorporating the effects of surface roughness and crop cover in the soil moisture retrieval models. Since the moisture and roughness should remain unchanged between low and high angle SAR acquisition, the gap period between the two acquisitions should be minimum. However, for RADARSAT-1 the gap is typically of the order of 3 days. To overcome this difficulty, simultaneously acquired ENVISAT-1 ASAR HH/VV and VV/VH data was studied for operational soil moisture estimation. Cross-polarised SAR data has been exploited for its sensitivity to vegetation for crop-covered fields where as co-pol ratio has been used to incorporate surface roughness for the case of bare soil. Although there has not been any multi-frequency SAR system onboard a satellite platform, efforts have also been made to understand soil moisture sensitivity and penetration capability at different frequencies using SIR-C/X-SAR and multi-parametric Airborne SAR data. This paper describes multi-incidence angle, multi-polarised and multi-frequency SAR approaches for soil moisture retrieval over large agricultural area.
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Full-text available
This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.
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Soil moisture content has paramount importance in dictating engineering, agronomic, geological, ecological, biological and hydrological characteristics of the soil mass. Though earlier researchers have employed various techniques of moisture content determination of soils, both in laboratory and in situ conditions, ascertaining the applicability of these techniques to soils of entirely different characteristics and the ‘types of moisture content’, which they can measure, is still a point of debate. As such, a critical review of all the established and emerging soil moisture measurement techniques with respect to their merits and demerits becomes necessary. With this in view, efforts have been made in this paper to critically evaluate all the soil moisture measurement techniques, limitations associated with them and the influence of various soil-specific parameters (viz., mineralogy, salinity, porosity, ambient temperature, presence of the organic matter and matrix structure of the soil) on the measured soil moisture content. This paper also highlights the importance of various innovations based on Micro Electro Mechanical Systems (MEMS) and nano-sensors that are emerging in this context.
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Soil moisture, water potential, and bulk density measurements were performed on a 0.4 ha bare field (27.2% clay, 61.7% fine and coarse loam) concurrent with backscattering coefficient measurements: 17 different volumetric water content profiles from 0 to 10 cm were sampled including wet, intermediate, and dry conditions. Backscattering coefficients were measured using a 4.5 GHz frequency, HH polarization and 15–20° incidence angle microwave sensor configuration, in order to minimize the soil surface roughness effects. First, the statistical analysis of the data (“classical calibration procedure”) exhibited satisfactory regression lines between the backscattering coefficient and the volumetric water content calculated over arbitrary soil depths, as described by many authors (correlation coefficients between 0.859 and 0.899). Furthermore, the same results were obtained when water potential data were considered (log scale). Second, taking into account the dependence between the water content profile and the microwave penetration depth, the experimental relationship between backscattering coefficient and volumetric water content became nonlinear and exhibited smaller residuals than the “classical regression line.” Finally, a statistical procedure for predicting the mean and standard deviation of volumetric water content profiles from the soil surface to the microwave penetration depth is presented. Results showed that the proposed procedure is promising for obtaining near-surface water content estimates used as boundary conditions for “soil/atmosphere” water transport modeling.
Article
Microwave frequency measurements of moist soil dielectric properties are noted to challenge the validity of percent-of-field-capacity as a moisture indicator that is independent of soil texture in terms of microwave sensitivity. In arriving at this view, gravimetric, volumetric, and percent-of-field-capacity were tested for their ability to reduce dielectric behavior divergence between soil textures at 1.4 and 5.0 GHz. The most congruent dielectric behavior between soil textures is found to occur when soil moisture is expressed on a volumetric basis that is proportional to the number of water dipoles/unit volume. An inadequate characterization of soil bulk density in the field, combined with the dependency of bulk density on water retention at field capacity, offers the most plausible explanation for the earlier conclusions.
Article
The three components of microwave remote sensing (sensor-scene interaction, sensor design, and measurement techniques), and the applications to geoscience are examined. The history of active and passive microwave sensing is reviewed, along with fundamental principles of electromagnetic wave propagation, antennas, and microwave interaction with atmospheric constituents. Radiometric concepts are reviewed, particularly for measurement problems for atmospheric and terrestrial sources of natural radiation. Particular attention is given to the emission by atmospheric gases, clouds, and rain as described by the radiative transfer function. Finally, the operation and performance characteristics of radiometer receivers are discussed, particularly for measurement precision, calibration techniques, and imaging considerations.
Article
We use prior theory and experimental results to construct a quantitative relationship between soil moisture and the penetration depth of synthetic aperture radar (SAR) microwaves at L-, C-, and X-bands. This relationship is nonlinear and indicates that a change of 5% volumetric water content (VWC) can cause between 1 and 50 mm of change in C-band penetration depth depending on initial VWC. Because these depths are within the range of differential interferogram SAR (DInSAR) measurement capability, penetration depth may be a viable proxy for measuring soil moisture. DInSAR is unlikely to detect a measurable change in penetration depth above 30% VWC, though certain clay rich soils may continue to cause surface deformation above that level. The possibility of using clay swelling as a proxy for soil moisture was found to be less feasible than penetration depth. Soil moisture may also be a significant, and previously unrecognized, source of noise in the measurement of subtle deformation signals or the creation of digital elevation models using repeat-pass DInSAR.
Nasa-isro sar ( nisar ) mission science users handbook
  • A Falk
Falk, A., G. B. A. B. S. B. S. C. M. C.. H. Z., 2018. Nasa-isro sar ( nisar ) mission science users handbook. 22(2), pp. 1-350.
  • Isprs Tc V Mid-Term
  • Symposium
ISPRS TC V Mid-term Symposium "Geospatial Technology -Pixel to People", 20-23 November 2018, Dehradun, India