Journal of
Hydrology ELSEVIER
Journal of Hydrology 188-189 (1997) 361-384
Retrieval of soil moisture and vegetation characteristics by use of ERS-1 wind scatterometer over add and semi-add areas R.D. Magagi*, Y.H. K e r r Centre d'Etudes Spatiales de la BlOsphbre ( CNES/CNRS/UPS), 18 avenue E. Belin, 331055 Toulouse, France
Abstract The aim of this study is to use the information provided by the ERS-1 wind scatterometer (WSC) over land surfaces in arid and semi-arid environments to infer soil moisture in the presence of vegetation. Driven by dielectric properties and surface roughness, the soil contribution is attenuated by a factor which depends on canopy characteristics (water content, shape, height, density) and scatterometer viewing conditions. To describe the influence of vegetation on the signal, a semiempirical 'water-cloud' model (a first-order radiative transfer solution) was used. The optical thickness (r) and the single scattering albedo (oJ) are the parameters used to quantify vegetation contribution to the measured signal. Through a simulation analysis for different soil moisture and viewing (incidence angle) conditions, we show the importance of r and o0 on the signal partition between vegetation and soil contributions. To quantify the effect of vegetation on the signal, we used information on the green vegetation acquired from NOAA-AVHRR, visible and near-IR data combined with WSC satellite data in a water-cloud model to extract ~ and o~.The temporal evolution of the various contributions to the signal was then compared for different angular ranges. This semiempirical model was then applied within suitable angular ranges to retrieve soil moisture.
1. Introduction Soil moisture is a key variable in hydrology. It is a driver for water exchanges at the surface-atmosphere interface (Avissar, 1995), a factor controlling runoff, and a boundary condition for moisture profile and mass transfer models. Surface soil moisture can also be used in global circulation or mesoscale models as a boundary condition and, if global maps of soil moisture were available, as an input variable. Owing to its high temporal and spatial variability, soil moisture cannot be mapped using conventional ground measurements and * Correspondingauthor. 0022-1694/97/$17.00 © 1997- Elsevier Science B.V. All fights reserved I'll S0022-1694(96)03166-6
362
R.D. Magag~ Y.H. Kerr~Journalof Hydrology 188-189 (1997) 361-384
thus satellite-borne remote sensing systems offer the only prospect of inferring global soil moisture. Retrieval of soil moisture by remote sensing has been attempted by several means, but the most likely solution is to use low-frequency (L or C band) microwave sensors owing to their sensitivity to liquid water and their ability to penetrate clouds and vegetation. As no passive microwave system operating below 10 GHz is in orbit at present, one has to rely on active systems. Most research related to retrieval of soil moisture with use of active microwaves has been performed using ground based scatterometers (Alphonse, 1988; Oh et al., 1992) over controlled sites. Relationships between the backscattering coefficient a ° and surface parameters have been formulated. Within the framework of HAPEX-Sahel (Goutorbe et al., 1994; Prince et al., 1995) we have tested the use of the ERS-1 wind scatterometer (WSC) to retrieve soil moisture over semi-arid areas (in the Sahelian zone), where there is a close relationship between rainfall, soil moisture and roughness, and vegetation cover. The vegetation contribution to the measured signal has been studied by various workers (Ulaby et al., 1979, 1982b, 1986; Brunfeldt and Ulaby, 1984), and several models describing the interactions of microwaves with vegetation and soils exist. It has been shown that, to infer soil moisture, one has to take into account the contribution of vegetation and surface parameters such as surface roughness to the signal. To deconvolve the various contributions to the signal, and when only one frequency is available, one can use different view angles and/or data in the visible and near-IR domain. In a recent study (Kerr and Magagi, 1993), it was shown that, by using the different viewing conditions available with the ERS-1 Wind scatterometer (WSC), it was possible to: (1) derive the slope and the intercept of the a ° vs. angle relationship, and relate it to surface roughness (slope) and vegetation biomass or soil moisture (intercept); (2) monitor soil moisture and surface roughness in the absence of vegetation using the temporal variations and variety of viewing angles obtained by WSC as well as data obtained in the visible and ncar-IR from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) meteorological satellites. The last steps are then to retrieve soil moisture in the presence of vegetation and to study the limits of the retrievals (i.e. for very dense vegetation or high water content where soil moisture retrieval is not possible at 5 GHz). Within the HAPEX-Sahel framework (Goutorbe et al., 1994), we intended to estimate and monitor soil moisture at satellite scale using an synergistic approach between active microwave (WSC) and optical data (NOAA-AVHRR, visible and near-IR). A semi-empirical model (Attema and Ulaby, 1978), which is a first-order radiative transfer solution, combined with WSC data has been used to assess vegetation characteristics (optical depth (r), single scattering albedo (o~)) and to take into account its influence for soil moisture estimation. Studying the validity range of the first-order solution, Ulaby et al. (1986) have shown that the difference between the exact numerical solutions and those resulting from the first-order solution is less than 1 dB if the single scattering albedo is less than 0.5 and optical thickness is close to one. Using passive microwave data, Fung and Eom (1984) have shown that, at C band or lower frequencies, the first-order solution is useful when the product r*oJ is less than 0.2. When this condition is met, the error is less than 2%. In another study, Fung and Eom (1985) compared vegetation effects on soil moisture estimation in passive and active
363
R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997) 361-384
AIMdmO. 1 (a)
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Fig. 1. Comparison of sensitivity reduction owing to vegetation in (a) active and (b) passive sensing (from Fung and Eom, 1985). [12], [14] and [15] are some references used by Fung and Eom (1985).
364
R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997) 361-384
sensing. They reached the conclusions that increasing 00 contributes to an improvement of signal sensitivity to soil moisture in passive sensing, whereas for active sensing increasing ~0 adversely affects the sensitivity. For both passive and active sensing, increasing r decreases the signal sensitivity. For any given opacity r, increasing ~0 gave a better sensitivity to soil moisture in active than in passive sensing (Fig. 1). Fig. l(a) indicates that the decrease of the signal sensitivity to soil moisture with vegetation is slower at low incidence angles than at high. This results from the fact that at high incidence angles, the signal coming from the soil is reduced to the benefit of vegetation contribution. Consequently, the signal becomes less sensitive to soil parameter and in particular to soil moisture.
2. Study area The study area is a 50km by 5 0 k m square centred at Banizoumbou (13°31'08'qN, 02°39'37"E) (Niger Republic). It covers the East Central Super Site of HAPEX-Sahel Experiment (Prince et al., 1995). One major characteristic of this region, which is representative of Sahelian area, is the duration of the rainy season (4 months) from June to September. Annual rainfall is on average 550 mm (Fig. 2), with strong spatial and Banizoumbou 45 * MSAVI*I00
40
bar rainfall(mm) 35
7
30 °, the backscattering coefficient loses its dependence on incidence angles. This point has been shown with WSC data (Kerr and Magagi, 1993) by deriving the relationship between the slope of the a ° and the view angle. For this reason, the curve corresponding to the 18°-30 ° range appeared more uneven than the other two, owing to the stronger angular dependence at low incidence angle, which increases with the temporal evolution of surface feature (soil roughness, soil moisture and vegetation cover) effects. A phase difference can also be seen between the temporal evolution of the a ° measured in the 18°-30 ° range when compared with the other two ranges at higher angles. In the 18°-30 ° range the signal reaches a maximum earlier, as at low incidence angles the vegetation contribution is less important than at higher angles. Consequently, at low incidence angles the signal is more sensitive to the soil moisture variations. However, if we compare the temporal evolution of the MSAVI (Fig. 2) with the temporal evolution of backscattering coefficient acquired within the 30°-40 ° and 400-46 ° angular ranges, the curves are synchronous. Banizoumbou mid beam values -8
angular ranges : o [18, 30] * [ 3 0 , 40] x [40, 46]
o
-9 -10
o
o~.,
o
o
-11 0 v
-12
0 O
-13
o
o
O
[ ~
o
[
o
o
/
0~
-14
~ 0
O
i /
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/
°
0
O
O
~ ~
o O
",, , ~
oo
-15 -16 -17 100
".....7 . 150
200
250
3
350
400
day of year 1992 Fig. 3. Temporalevolutionof angular signaturesof a ° mid beam during the study period overthe HAPEX-Sahel Central East Supersite (Banizoumbou).
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R.D. Magagi, Y.-H. Kerr/Journal of Hydrology 188-189 (1997) 361-384
In summary, these analyses show that the WSC data are in agreement with theoretical studies of backscattering coefficient response to soil moisture and vegetation. One can also note, in the temporal signal, the 'spikes' occurring at DOY 132 and 182. They are due to respectively 2 mm and 10 mm rains (EPSAT data) occurring just before a satellite overpass, leading to a wet surface and thus a much higher signal. Because of the decrease of the signal sensitivity to soil moisture with the incidence angle, and the various time differences between rain event and satellite overpasses, the 400-46 ° curve responds less to the soil moisture resulting from 10 mm rainfall than does the 18°-30 ° curve to the wetness condition of 2 mm rainfall.
4. Background As shown by several workers (Ulaby et al., 1979, 1982b, 1986; Brunfeldt and Ulaby, 1984) the angular behaviour of o ° is related to target characteristics (soil moisture, roughness, vegetation properties) for any configuration (polarization, frequency, incidence angle) of a scatterometer. In the presence of vegetation, two processes occur, i.e. surface scattering coming from underlying soil (wet soil) and volume scattering in the vegetation layer. The result is that o ° decreases slowly with incidence angle 0 owing to the attenuation of the soil contribution by vegetation. Ulaby et al. (1982a, 1986) have shown that the predominant process depends on soil moisture with frequencies around 5 GHz, and incidence angles (0) ranging from 7 ° to 17°. Recently, a theoretical model of canopy backscatter MIMICS (Ulaby et al., 1990) has been developed. However, owing to its large number of parameters, this model is cumbersome to use over large heterogeneous fields of view. Its main advantage is in clarifying the various scattering processes within the canopy layer. To make the inversion problem easier, attention is focused on simpler empirical models requiring fewer input parameters. In this study, we used a semi-empirical 'water-cloud' model developed by Attema and Ulaby (1978), which parameterizes the backscattering coefficient within the canopy as a function of volumetric soil moisture (sra, in m 3 m-3), plant water content (W, in kg m -3) and plant height (h, in m). In this model, the vegetation is treated as identical, and spherical water particles, characterized by height and density, which is related to the volumetric water content of the vegetation. Within the canopy layer, the distribution of scatterers is assumed to be uniform, with no interaction between the signal reflected by the soil and the incident wave within the canopy layer, so the soil and vegetation contributions can be added incoherently. Only single scattering is considered. Consequently, this model represents the first-order radiative transfer solution. The model consists of a coarse description of vegetation but nevertheless reproduces the temporal behaviour of the backscattering coefficient for various crops (Attema and Ulaby, 1978). According to the assumptions, the signal is not affected by the changes in the scatterers' geometry (size, shape and orientation) during the vegetation growth but rather by their density. This involved the neglect of variations of extinction properties and thus several backscattering processes within the vegetation cover. Also, it may be possible that the intensity backscattered includes some interaction between soil/or vegetation components. Eom (1986)
R.D. Magagi, Y.-H.Kerr~Journalof Hydrology 188-189 (1997)361-384
369
parameterized the interaction term as O°nt(O)= 2a°eg(O)lRvvl2(0)exp(- rsec(0))
(1)
where Rvv is the Fresnel reflection for vertical polarization. Based on Rayleigh scattering medium, this parameterization is built on the assumption that the underlying ground is a perfect plane (fiat). It was assumed that the interaction term resulted from the scattering process between vegetation scattering coefficient from the top of the canopy to the ground (a °) and soil refiectivity (IR vvl2) attenuated from the ground to the top of canopy by a factor exp(~'sec(O)). This term is not negligible, particularly at the beginning of the vegetation growth, for wet soil conditions. In such cases, it compensates the attenuation of the soil contribution by the vegetation cover. Fung and Eom (1985) showed that for thicker optical depth, the interaction term reduces the loss in the signal sensitivity to soil moisture at small incidence angles more than when the optical depth is small. This implies that, even if the low values of the albedo of vegetation cover (w < 0.2) permitted treatment of the medium as a weakly scattering one, where multiple scattering is neglected, this assumption induces some errors in the soil moisture estimation. The backscattering coefficient of the whole canopy includes a vegetation contribution and a soil signal attenuated by vegetation, and can be written aOa.(O)=
0 2 (o)Oso.(O) 0 Ov0g(O)+3'
(2)
with a°oil(O)=A(O)exp(D sm)
(3)
3"2(0) = exp[ EWhsec(O)]
(4)
a°eg(O) = Bcos(0)[ 1 - 3,2(0)]
(5)
-
where 32 is the canopy two-way transmitting factor. In Eqs. (3)-(5), A and D depend on surface roughness, and B and E depend on frequency and vegetation type (Attema and Ulaby, 1978). Describing the canopy as a Rayleigh scattering medium (Fung and Eom, 1981), Eq. (4) and Eq. (5) can be modified so as to use only two parameters: the single scattering albedo 60 and the optical thickness of vegetation r: 32 (0) = exp[ - 2rsec(O)]
(6)
a°eg(0) = 0.75wcos(O)[ 1 - 32 (0)]
(7)
where w=K,/(K~+Ka) z=(K~ + Ka)d
Ke=K~+Ka where K~ is the scattering coefficient, Ka is the absorption coefficient, Ke is the extinction coefficient and d is the height of the canopy layer.
R.D. Magagi, Y.-H. Kerr~Journalof Hydrology 188-189 (1997) 361-384
370
r is the product of the extinction coefficient and the vertical path of the wave into the canopy; it combines absorption and scattering mechanisms and represents the vegetation extinction factor. To take into account spatial heterogeneity of natural areas with both vegetation patches and bare soil within the field of view, some workers (Ulaby et al., 1982b, Kerr and Njoku, 1990) include in their equations a term for vegetation fractional cover Cv. The backscattering coefficient can then be expressed by:
o°(0) = (1 - Cv)o~o,(O) + Cvo°an(O)
(8)
As we used the VV polarization, we assumed that the surface-volume interaction term can be neglected (Ulaby et al., 1986); the error resulting from this assumption is less than 1 dB. Including Eq. (2) and Eq. (3) in Eq. (8), we write o°(0) = { 1 - Cv[1 - 3,2(0)] }A(O)exp(D sin) + Cvo°eg(O)
(9)
5. Simulation study Eq. (9) expresses the water-cloud model over semi-arid areas as seen from space. To assess the possibility of inverting this model, we studied the sensitivity of o ° to vegetation sm=5,(%voi); CV--0.2
-8
sm=lO(%vol); CV=0.2 i
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viewing angle (degrees) : "15 o 25 x 35 + 45
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R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997) 361-384
371
parameters (r and oJ) through a simulation analysis for different values of soil moisture and different viewing conditions (Fig. 4). Fig. 4 (continuous curve) shows that, for a low value of vegetation single scattering albedo (~ = 0.05) and soil moisture equal to 5%, 10% and 20% (volumetric), a ° decreased with optical thickness even if incidence angles increased. This behaviour confirms that the predominant process in this case was surface scattering attenuated by vegetation. For a higher value of o~(~ = 0.18), but not changing the other parameters (soil moisture, optical thickness and incidence angle), Fig. 4 (dashed curve) shows that the sensitivity (positive or negative) of a ° to r depends on incidence angle and soil moisture. Consequently, for the same viewing conditions, soil moisture drives the signal partition. Also, as soil moisture increased, the transition from surface scattering to volume scattering appeared at larger incidence angles. It is important to note that in both surface and volume scattering mechanisms, the backscattering coefficient is sensitive to optical thickness, but at the transition between volume and surface scattering, o ° loses its sensitivity to optical thickness. Therefore, for both volume and surface scattering, knowledge of the exact vegetation optical thickness is necessary to retrieve soil moisture. In addition to these cases, observation of all the curves indicates that a saturation effect appears for r >- 1. In those situations, theoretically, model inversion is not possible. Another point demonstrated by this simulation analysis is that, without any consideration of soil moisture conditions, the sensitivity of the signal to ~ (Oa°/&o) increases with incidence angles and optical thickness r. This indicates that the estimation of the vegetation single scattering albedo ~ is more efficient at high incidence angles, but when soil moisture increases, Oa°lOo~decreases for the same viewing conditions, which implies a smaller effect of the vegetation contribution to the signal.
6. Inversion method
We are now going to study Eq. (9), which parameterizes the backscattering coefficient a°(0) as a function of soil characteristics (soil moisture, surface roughness), vegetation characteristics (optical thickness r, single scattering albedo o~) and sensor viewing conditions (incidence angle /9). The analysis of Eq. (3) shows that A(O) corresponds to the contribution of bare and dry soil, so it represents the roughness response. For this reason, A(O) was derived from an exponential fit of a ° as a function of 8 for wind scatterometer data corresponding to the dry season (April-May). For mid-beam antenna, we obtained A (/9)= 0.1488exp( - 0.04280)
(10)
We assumed that the surface roughness parameters are constant for the period studied. Hence the .fit (Eq. (10)) is used in Eq. (9) to represent soil roughness response during the whole study period. In Eq. (9), this assumption reduces to three the number of unknown parameters: soil moisture, single scattering albedo and optical thickness. To express Eq. (9) as a function of vegetation parameters (r and oJ) only, we consider fore- and mid-beam data for each node, and we assume that the soil moisture sensitivity D is constant for both configurations. In the following, subscripts 1 and 2 denote fore- and mid-beam data, respectively. They are acquired quasi-simultaneously, with incidence angles differing
372
R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997) 361-384
by 10° or less, and 01 > 02. Considering the two configurations, the fit of A(0) is given by
A(Oi ) =O.O932exp(-0.029701) A (02) = 0.1488exp( - 0.042802 )
( 11)
Using Eq. (9) and Eq. (11) to eliminate soil moisture sensitivity D, we obtain for each acquisition 0 A(02) 0 0 0 172(02)=f(01,02, Cv)A--~I)[o'1 (01 ) Cvorveg(01)] "{-Cv0rveg(02)
(12)
- -
where
f(OI, 02, Cv) =
-- '~2(02)]
1 -C~[1
1-Cv[1 -~2(01)]
Thus Eq. (12) is not explicitly dependent on soil moisture. To solve for r and oJ (through "y2 and O°eg)with Eq. (12) alone is mathematically incorrect: we need several observations to solve the two unknowns. For this reason, we took advantage of the high temporal resolution and multiangular observations of WSC data. To derive simultaneously r and o~, we selected WSC measurements acquired at 'nearby dates'. Depending on data availability, the criterion of nearby dates covered 4 days to 1 week. This assumption is valid as such a banizoumbou 1992 -8
,
,
,
,
,
,
,
- I: l line
-9
,
,
j
*
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: ••
-10 -11 E
-12
%,**
O0
**** • ***
-13 0
E
-14 -15 -16
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-17 -17
i
i
i
i
i
i
i
I
-16
-15
-14
-13
-12
-11
-10
-9
simulated
-8
sigmaO(dB)
Fig. 5. Comparison between measured WSC data and simulated backscattering coefficient given by Eq. (9),
R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997)361-384
373
time lapse corresponds to an insignificant change in vegetation cover. The minimization process was then applied for WSC data corresponding to a slight variability of vegetation cover in terms of r and ~0 retrievals using a non-linear procedure of minimization of the r,m.s.e, between measured and calculated values of a ° given by Eq. (12). In other words, we solve the following function F in term of r and oJ estimations: 7
F= ~, [trOeas(O)-trO(o)]2 i=1
0
where ar,eas(O) is the WSC mid-beam data and a ° is the simulated backscattering coefficient given by Eq. (12).
7. Results and discussion
7.1. Temporal evolution of vegetation parameters The values of r and oJ are obtained with an r.m.s, of 0.318 dB data and a correlation coefficient of 0.98. Fig. 5 is the comparison between measured WSC data and simulated backscattering coefficient given by Eq. (9). To take into account the path of the signal within the vegetation layer, we used the oblique optical thickness rob = r/cos(0) instead of r. In Fig. 6, the temporal evolution of rob agreed well with MSAVI behaviour. This allowed us to introduce a qualitative relationship between chlorophyll (vegetation
Banizoumbou vegetation parameters 1.6
1.4
(*) MSAVI*3
~.
(+) albedo
~'1.2
: ."i/ ~
(x) tau/cos(teta)
.~ ~
~ ~'
,
~x ~
~0.8
x.~/
.--0.6 >
0.4 0.2 "MI"++I~
o
"I~
1so
-a~,-4- -l'Ir':
li "~
200
3so
day of year 1992 Fig. 6. Temporal evolution of rob (*) and to (+) over the HAPEX-Sahel Central East Supersite (Banizoumbou).
R.D. Magagi, Y.-H. Kerr~Journalof Hydrology 188-189 (1997) 361-384
374
(a)
B a n i z o u m b o u mid beam [18,30] degrees 0.16
i
+- (measured) 0.14 f
x (soil)
I
* (vegetation)
0.12 0.I E 6
f~
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+, ,,
~----~, ,,,
0.08 0.06 ~t
0.04
0.02
I00
150
200
L
I
250
300
350
day of year 1992 Banizoumbou mid beam [30,40] degrees
(b) 0.1
+- (measured) 0.09 i
x (soil)
, i
0.08
* (vegetation)
0.07
E E E e~a
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0.06
II
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/
0.05
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:.
•
•
,.•
0.01 0 100
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..... Jl.......'I, 9
150
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200
i
250
day of year 1992
350
R.D. Magagi, Y.-H. Kerr/Journal of Hydrology 188-189 (1997) 361-384
(c)
375
Banizoumbou mid beam [40,46] degrees 0.06
'
0.05
'*,
+- ( m e a S u r e )
,,' ' " "
x (soil) * (vegetation)
0.04 J
~,
~. ~.
~-+
t'q
E
v
0.03
0.02 Q
I
I
.0
•
0.01
0 100
150
200
I
i
250
300
350
day of year 1992 Fig. 7. Comparison between measured signal (+) and the respective contributions of soil ( x ) and vegetation (*) (in m 2 m -2) for the angular range 18°-30 * (a), 30°-40 ° (b) and 4 0 ° - 5 0 ° (c).
index) properties and the dielectric-structurai properties of vegetation. The advantage of such relationship is to relate vegetation index properties accessible by optical remote sensing to dielectric-structural characteristics (optical thickness) obtained by using independent observations of backscattering coefficient (or brightness temperature in passive sensing). Over agricultural areas, some workers have developed empirical relationships between optical thickness and plant water content (Jackson et al., 1982). However, these relationships cannot be rigorously applied over semi-arid regions, where vegetation is sparse and heterogeneous, particularly in terms of plant water contents. The single scattering albedo oJ (Fig. 6) first increases with vegetation growth from DOY 211, and then remains almost constant for different time intervals (DOY 243-258 and 262303). This is probably due to the fact that ~0is related to the plant water content, which has a small temporal variability when vegetation is mature. In contrast to a previous study by Van de Griend and Owe (1993), we found that o~varied with vegetation growth. This difference is probably due to the fact that the radiometer response (passive microwave) is much less sensitive to ~0than is the scatterometer response (active microwave) as shown in Fig. 1. The main objective of ~" and o~ estimation is to eventually assess the influence of vegetation dielectric and structural properties on backscattering coefficient. Once this relationship is derived, it is possible to estimate soil moisture through vegetation cover.
376
R.D. Magagi, Y.-H. Kerr~Journal@Hydrology 188-189 (1997) 361-384
(a)
Banizoumbou mid beam Soil contribution 0.14 A
angular range + [18,30]
0.12 I
/ /
0.1
/
0.08
H
I
0.06
0.04
0.02
0 i00
150
200
250
30o
day of year 1992 Banizoumbou mid beam Vegetation contribution
(b) 0.35
i
!
,
,,'~&__ ~
0.3
',
r,o
E
* MSAVI + [18,30] x [30,40] o [40,50]
i
0.25
.¢ ¢q
350
0.2
0.15
0.I
0.05
0
100
150
200
2 0
day of year 1992
300
350
R.D. Magagi, Y.-H. Kerr~Journalof Hydrology 188-189 (1997) 361-384
377
7.2. Comparison of contributions to the microwave backscatter signal
The sensitivity of the signal to soil and vegetation characteristics is linked to incidence angle. To reduce this influence on the signal, results are presented separately for different angular ranges of the mid-beam antenna: 18°-30°; 30°-40°; 40°-46 °. Assuming the validity of water-cloud model over semi-arid areas, the retrieved values of r and o~ were used to compute the second part of the second member of Eq. (9). Then, from WSC mid-beam antenna data and the expression for o ° given by the water-cloud model over semi-arid areas, we derived the soil contribution from the first term of the second member of Eq. (9). Fig. 7(a), Fig. 7(b) and Fig. 7(c) show comparison between o ° measured by the WSC and the contributions of soil and vegetation derived from Eq. (9). We can see that the relative contributions of surface and volume scattering depend on viewing angle. For high viewing angles (30o-40 ° and 40°-46°), the phenological stage of the vegetation during the growing season strongly affects the signal partition. 1. Within the 18°-30 ° range, the soil contribution was approximately equal to the measured values of o°(0) up to day of year (DOY) 223. After that date, the soil response decreased slightly owing to vegetation growth (see Fig. 1). In this angular range, the signal, which is weakly attenuated by the canopy, penetrates the vegetation layer and thus includes information coming from the underlying soil. Consequently, within the 180-30 ° range, for a relatively small vegetation cover, the soil has an important role in the observed signal. 2. For the 300-40 ° range, during the growing season, the soil contribution was about the two-thirds of the measured signal. 3. Within the 400-46 ° range, when the vegetation effect is at its maximum, soil and vegetation contributions were approximately equal. The results show that the increase in incidence angle leads to an increase in the vegetation contribution, to the detriment of the soil response. If we analyse simultaneously the temporal evolution of the soil contribution (Fig. 8(a)) within the three angular ranges, we find that this signal decreased strongly with incidence angle whereas the temporal evolution of the vegetation part (Fig. 8(b)) did not vary significantly. These results are in agreement with surface and volume scattering theory.
8. Soil moisture estimation 8.1. Vegetation effect
The retrieved optical thickness and single scattering albedo were used in this section to correct the signal from vegetation effects. During the HAPEX-Sahel Experiment, some
Fig. 8. Temporalevolutionof (a) soil contributionand (b) vegetationcontributionwithinthe threeangularranges over the East Central site.
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(1997) 361-384
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soil moisture measurements were made in coincidence with the dates of satellite overpasses. Table 1 indicates the ground measurements and WSC data used to compute the signal sensitivity to soil moisture. We can see that the WSC data were acquired from mid-beam incidence angles of 180-42 °. Considering the dependence of 0 on the parameter D (Eq. (3)), the latter was derived for low incidence angles (18°-23 °) and for higher angles (26°-42°); we obtained respectively DI' = 0.258 and D2' = 0.234 in (dB (% vol.)-I). These values were assumed to be constant during the study period. The slight difference between D ~' and D2' is in agreement with the fact that at 0 > 20 ° the sensitivity of the scatterometer to soil moisture remains approximately the same (Ulaby et al., 1986). To discuss the vegetation attenuation on the backscattering coefficient response to soil moisture, we can write from Eq. (9) (expressed in dB) #a°(dB) _
D'
Osm
C~ t2(O)]} aoeg(O) l + l l_Cv~_
(13)
a°oil(O)
with
1)1= aa°°it(dB) =4.34D c~sm
where D' is the sensitivity of bare soil (expressed in dB (% vol.)-l). Eq. (13) indicates that the sensitivity of the signal to soil moisture decreases with vegetation cover. It is useful to note that this sensitivity is also a function of signal partition 0 0 through the factor aveg(O)lasoit(O), which includes viewing conditions, soil and vegetation characteristics. Thus, for high soil moisture and low incidence angles, the effect of vegetation on the signal is negligible and the sensitivity equals D'. However, if soil and vegetation parameters are combined so that
Cva°eg -< [1 -Cv(1 _ 2 ) ] or Cv~°og-> (1-C~[1-'r2)] vegetation reduces signal sensitivity in both situations. Considering our study area, as vegetation never completely covers the soil, we can eliminate the case where Cva°eg >> [1 -- Cv(1 - - ' y 2) ] a s o0i l for which aa°(dB) as------~---"0 Fig. 9 shows a loss of sensitivity to soil moisture with time and when optical thickness increases. This results from the increasing signal attenuation from vegetation. Fig. 9(a) shows that the temporal trend of Oa°lOsmdecreases with vegetation growth, which is in agreement with Fig. 9(b). At the end of the rainy season, when there is almost no green vegetation, the sensitivity of the signal to soil moisture increased. This is certainly due to the decrease of the amount of vegetation, which increases the signal response to soil moisture, in particular when the soil is slightly wet.
8.2. Soil moisture retrieval To estimate soil moisture, the WSC data (mid-beam antenna) corresponding to minimum vegetation attenuation were used; in particular, acquisition in the 18°-30 °
R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997) 361-384
380
range and the WSC data listed in Table 1, which allowed a calibration of the backscattering coefficient sensitivity to soil moisture. Then from Eq. (9), using D t or D2 according to the incidence angle, we deduced soil moisture values. Fig. 10 shows the temporal evolution of the estimated soil moisture together with ground measurements made by three teams over the East Central Super Site (ECSS) (Monteny, 1993; Van Ovelen et al., 1992). The ground measurements illustrate the spatial and temporal distributions of soil moisture over the study area. We can see an overestimation of some retrieved soil moisture at the end of the rainy season when the surface is slightly wet. Another source of error resulted from the radar penetration depth of the soil, which is not necessarily equal to the depth of the soil moisture measurements (0-5 cm). Moreover, soil moisture measurements and satellite overpasses are not synchronous. It should also be stated that the optimal radar incidence angles (7°-17 °) (Ulaby et al., 1979) are not available to estimate soil moisture. Consequently, surface roughness affects the soil moisture estimation. Therefore, although the angular dependence of Oo°lOsmhas been take into account, the assumption of constant surface roughness is bound to introduce small errors. Owing to the spatial variabilities of soil moisture, some problems are inherent to our approach, i.e. the use of satellite data to retrieve representative soil moisture over large
(a)
Banizoumbou
0.26
,
,
(b)
,
0.24
. . . .
0.24 ll( ~
0>
Banizoumbou
0.26
It ==
~
0.22
0.22
0.2
0.2 )E )E
Ol,
)E
)E
~E
"o
E0.18
~
i
~0.18
|
)E
~0.16
t~ "1o
"1o
0.14
0.14
0.12
0.12 MI
0.1
150
2()0 DOY 1992
250
0"10'.4 0'.6 018
1
112
tau/cos(teta)
Fig. 9. (a) Temporal behaviour of Oa0IOsm; (b) Oa°lOsm as a function of optical thickness.
R.D. Magagi, Y.-H. Kerr/Journal of Hydrology 188-189 (1997) 361-384
381
areas. To address this problem, the first step is to calibrate the signal sensitivity to soil moisture D, using point measurements together with WSC data over 50 km x 50 kin. This can induce some errors resulting from the spatial variabilities of soil moisture, soil structure and vegetation (roughness is assumed to be constant). To avoid the influence of the spatial variabilities of soil moisture, one should consider ground data after a heavy rain event provided they are synchronous with satellite data. In such conditions, soil moisture is distributed uniformly, therefore its spatial distribution does not affect the parameter D determination. One of the goals of the of HAPEX-Sahel Experiment is to assess scaling effects (i.e. how to relate ground measurements to GCM or mesoscale model grid scales) and how to derive surface characteristics at regional or global scales. The retrieved soil moisture variability can be strongly affected by the vegetation characteristics and the scatterometer viewing conditions. However, to extend the discussion related to the aforementioned difficulties, we notice that much of the inaccuracy in soil moisture estimation can be explained by the method used to quantify the input variables for the models and the accuracy of these parameters.
9. Conclusion The signal measured by remote sensing includes the response of the whole target without any discrimination between soil and vegetation contributions. These contributions can Banizoumbou 18 16 14
X
* (retrieved) + (measured) x (measured) o (measured) bar (rainfall/5)
X
X X
12
+ X
10
X
J
o
8 6
0
4 2
......... •120 ,i>i 140 160
180
i200 220,hlt[.240
--,,,
26O
Oh,
~80
300
day o f y e a r l 9 9 2 Fig. 10. Temporal evolution of measured and retrieved (*) soil moisture (% vol.) over the HAPEX-Sahel Central East Supersite (Banizoumbou). Measurements made by Van Ovelen (+), Chanzy ( x ) and Laguerre (©).
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R.D. Magagi, Y.-H. Kerr~Journal of Hydrology 188-189 (1997) 361-384
be separated through the use of models together with the knowledge of vegetation characteristics (r,00) over a given soil. Through a simulation analysis, the sensitivity of the semi-empirical 'water-cloud' model has been assessed for different soil moisture conditions. The following features were found: 1. for a given soil, the sensitivity (positive or negative) of the signal to optical thickness (r) is related to the single scattering albedo (co) and to incidence angle (0). This showed that co should not be considered as a constant. In the literature, the value given for w over semi-arid regions is generally 0.05 (Choudhury et al., 1987; Kerr and Njoku, 1990), By using this value, the model only accounts for surface scattering, which means that the vegetation contribution is only modelled through its attenuation. Actually, this is not true when vegetation is well developed. 2. The sensitivity to co increased with 0 and decreased with soil moisture availability. 3. There is a transition zone between surface and volume scattering for which o °(dB) is not sensitive to optical thickness. When r --- 1 there is a saturation effect and the soil contribution is masked by the canopy. Consequently, in conditions of dense vegetation cover, the detection of soil moisture is not feasible. The 'water-cloud' model combined with NOAA-AVHRR and WSC data allowed us to extract vegetation optical thickness (r) and single scattering albedo (co). These vegetation variables modify the signal coming from the underlying soil and decrease the signal sensitivity to soil moisture. Through the analysis of the angular and temporal behaviour of the backscattering coefficient, it appeared that the magnitude of the signal attenuation within the canopy is strongly linked to the incidence angle. This property influenced soil moisture retrieval. In this study, a synergistic use of optical and radar data allowed us to remove the vegetation contribution from WSC measurements, to allow soil moisture estimation. The sensitivity to soil moisture (Oo°/Osm) computed for the lOP (Intensive Observation Period) data of the HAPEX-Sahel Experiment over given areas has been successfully used. It must be stressed that the present study relies on an important assumption, i.e. constant surface roughness. As surface roughness has a high spatial variability and probably a temporal 'cycle' it is now necessary to devote some effort to taking the variability of surface roughness into account in the inversion algorithm.
Acknowledgements The authors would like to thank Dr. J.P. Wigneron (INRA Avignon) and S. Maggion (CESBIO) for helpful discussions, N. Mognard Campbell (CESBIO), the Programme National de Ttledttection Spatiale (PNTS), the Programme Environnement Vie et Socitt6 of the Centre National de Recherche Scientifique, ESA, CERSAT and AGRHYMET for providing us with satellite data, T. Lebel for EPSAT data, and T. Valero and S. Wagner for satellite data pre-processing. The authors are indebted to all those who took part in soil moisture and surface roughness measurements during the HAPEX-Sahel Experiment: A. Chanzy, P. van Ovelen, L. Laguerre and A. Chehbouni. This study was carried out in the framework of the NASA MTPE EOS programme. Finally, the authors wish to thank the reviewers for their helpful comments.
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