SAR Data

Estimation of Watershed Soil Moisture Index from ERS/SAR Data A. Quesney,* S. Le He´garat-Mascle,* O. Taconet,* D. Vidal-Madjar,* J. P. Wigneron,† C. Loumagne,‡ and M. Normand‡

The aim of this article is to show that a watershed hy-

drological index could be derived from ERS/SAR measurements. Indeed, it is well known that, over bare soil, the SAR signal is a function of the geometric and dielectric surface properties. The problem to estimate soil moisture is to free from the effects of the space and time fluctuations of soil roughness and from the vegetation cover attenuation and scattering. The methodology presented here is based on the selection of land cover types or “targets,” for which the SAR signal is mainly sensitive to soil water content variations, and for which the vegetation and the roughness effects (in SAR signal) can be estimated and removed if needed. This method has been validated over an agricultural watershed in France. We show that the accuracy of the retrieved soil moisture is ⫾0.04–0.05 cm3/cm3, except during May and June, when vegetation cover is too dense to get reliable soil information. Elsevier Science Inc., 2000

INTRODUCTION Soil moisture is a key variable for many applications of various fields of physics research: agriculture and vegetation growth monitoring, meteorology and weather prediction, hydrology and stream flow prediction. For agriculture it is a rather obvious statement. For hydrological and atmospheric sciences, soil moisture is particularly important for the modeling of surface processes such as soil–vegetation–atmosphere interactions, which involve * CETP/CNRS, Ve´lizy, France † INRA/Bioclimatologie, Avignon, France ‡ CEMAGREF/Hydrologie, Antony, France Address correspondence to S. Le He´garet-Mascle, CETP/CNRS, 1012 avenue de l’Europe 78140 Ve´lizy, France. E-mail: sylvie.mascle@ cetp.ipsl.fr Received 25 August 1999; revised 11 September 1999. REMOTE SENS. ENVIRON. 72:290–303 (2000) Elsevier Science Inc., 2000 655 Avenue of the Americas, New York, NY 10010

water and energy exchanges. In particular, mean surface soil moisture plays an important role for: • real evaporation and evapotranspiration representation; • rainfall partitioning between surface runoff and infiltration; • control of the infiltration from surface soil reservoir to deeper reservoirs. For example, Rowntree and Bolton (1983) showed that an error in the soil moisture initialization could lead to large errors in weather forecasts. In hydrological models, Loumagne et al. (1991) showed that the assimilation of a soil moisture index (representative of the catchment hydrological state) into a rainfall–runoff model significantly improves stream flow forecasting. However, a difficulty for this study was to obtain a soil moisture index representative of the whole catchment hydrological state (about 100 km2). The derivation of soil moisture indices from satellite remote sensing data appears promising to overcome this problem. The aim of this article is to show that it is possible to define, from spaceborne SAR data, an index which is representative of, or contains information on, the way the mean surface soil moisture is varying along the year at a small agricultural watershed scale. This surface soil moisture index is built up to fit with the present parametrizations of soil–vegetation–atmosphere water exchanges (Noilhan and Planton, 1989; Ottle´ and Vidal-Madjar, 1994). In the past 10–20 years, several ways to derive soil moisture indices from airborne or spaceborne active measurements have been proposed (see, e.g., Dubois et al., 1995; Wang et al., 1997). In the early years, Ulaby et al. (1986) demonstrated that an optimized instrument could be defined for soil moisture estimation: C-band and about 10⬚ for the incident angle. At such incidence angles the soil backscatter signal does not depends on 0034-4257/00/$–see front matter PII S0034-4257(99)00102-9

Estimation of Watershed Soil Moisture Index from ERS/SAR Data

soil roughness state, but only on soil moisture. If the C-band frequency requirement is easy to fulfill, the low incident angle is not possible if high ground resolution has to be achieved. The two spaceborne SAR systems which are now in operation, ERS/SAR and RadarSAT, are not able to acquire images at smaller incidence angles than 20⬚ (23⬚ for ERS). This will be also the case for EnviSAT SAR, scheduled to be launched in 2000. At such high incidence angles, the radar signal backscattered from vegetated areas can be divided into three components: i) a signal from the soil surface volume, which is mainly dependent on the soil moisture content; ii) a signal from the soil surface which is driven by the soil roughness; and iii) a signal from the vegetation canopy overlying the soil. Moreover, the presence of a vegetation cover induces an additional attenuation of the soil backscattered signal. Then, to estimate soil moisture, it is necessary to find a way to cope with soil roughness and vegetation effects. Several ways have been proposed and tested. They are based on the use of multiparameter radar: i) two or more incidence angles (e.g., Autret et al., 1989, studied the possibility to derive soil roughness in the case of bare soils); ii) diversity in frequency and/or in polarization which helps to separate the three signal components. Most of them have been implemented and tested using the SIR-C/X-SAR mission (Evans et al., 1997; Kasischke et al., 1997), which offered the possibility to deal with a full multiparameter SAR. Although some success has been obtained (Schmullius and Evans, 1997), the main drawback of the proposed methods is that they need multiple imagery. At the present time, all these images cannot be obtained simultaneously with a single (operational) satellite, and delays between two data acquisitions are generally not compatible with soil moisture or plant density evolution. Therefore, it clearly appears that it is still necessary to examine some ways to derive soil moisture content using simple radar such as the one onboard the ERS 1&2 satellites, which is the purpose of this article. We will show that by using the high resolution capability of such radars, it is possible, for most of the year, to select fields (called here “targets”) over which it is possible to derive soil moisture without being bothered by vegetation or soil roughness. The use of such targets could lead to the definition of a space–radar index representative of soil water content at basin scale. This work carries on with some studies which showed that soil moisture index can be retrieved from ERS 1&2 SAR measurements (Beaudoin et al., 1990; Cognard et al., 1995; Taconet et al., 1996; Griffiths and Wooding, 1996; Blyth, 1997; Moran et al., 1998; Weimann et al., 1998). At field scale, Cognard (1996) showed that the relation between the backscattering radar signal and the surface soil moisture depends strongly on the crop type (mainly because vegetation contribution is related to crop parameters). It is worth pointing out

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that, for a given crop, the ratio between this vegetation component and the soil one evolves during the vegetation cycle and affects more or less the radar sensitivity to soil moisture variations. A way to overcome the vegetation component from single parameter radar data consists in modeling the microwave response of the vegetation layer. The canopy contribution being quantified, the global radar response can be corrected in accordance to retrieve the “bare” soil contribution. Because of the evolution of the plant features (geometry and biomass) during the vegetation cycle, this modeling requires regular ground truth measurements, satellite vegetation indices, or a priori knowledge for each studied crop. In this study, ground truth measurements on plant parameters are used to run the microwave/plant interaction model we used to validate the soil moisture retrieval methodology we describe. However, the information which is really necessary to apply the method is sufficiently crude (mainly crop density and height) to be accessible from a priori knowledge on plant phenology and local agricultural practices and from visible space remote sensing through the use of classical vegetation indices. Our approach is empirical in the sense that some of the parameters used in the algorithms are calibrated against ground data. However, it is supported by wellestablished theoretical electromagnetic models, such as the integral equation model (IEM) proposed by Fung et al. (1992). In particular, concerning the roughness effect, it is assumed, following this model, that, as long as the roughness remains more or less constant, the sensitivity of soil moisture does not depend much on the absolute value of the roughness: The different curves representing the backscattered signal intensity versus soil water content, for different soil roughness values, mainly differ by their offset value. Then, the study of the soil moisture variations from radar measurements should be possible only knowing roughness variations. The integral equation model has also been systematically used to get simulations, which were compared to the empirical results we found over the Orgeval test site, and then ensure the possibility of extending these empirical results to other watersheds. This article is conducted in four sections. In the next section, the Orgeval site is briefly described and the data acquired over this basin during the two years of study (1995–1996 and 1996–1997) are presented. In the third section, we study the winter wheat case. For this crop, the roughness state is about constant during the entire growing season and the correction of the vegetation contribution is possible using a first-order radiative transfer model derived from the equations of Karam et al. (1992). Therefore, the wheat can be used as a reference crop for which the variations in the soil backscattered signal are only due to some soil moisture changes. In the fourth section, the multiselective method is described. It is based on the selection, image per image, of appropriate

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Table 1. Percentages of the Different Land Cover Types Present over the Orgeval Watershed in 1995–1996 and 1996–1997 Agricultural Years

1995–1996 1996–1997

Wheat

Corn

Peas

Barley

Flax

Colza

Sugar Beet

Forest

Grass Land

Bare Soil

Others

30% 30%

10% 8%

8% 11%

3% 6%

3% 2%

3% 3%

3% 2%

20% 20%

6% 3%

2% 3%

12% 12%

targets. To be suitable, a target has to fit the following requirements: i) The measured radar signal must contain significant information about the soil contribution, and ii) the number of such targets within the watershed must be sufficiently large to obtain a soil moisture index representative of the whole watershed. Actually, the proposed soil moisture index is calculated considering only these targets. It will be representative of an index at watershed scale if the selected fields are sufficiently numerous and regularly distributed within the site, and/or soil moisture not too heterogeneous over the basin—two conditions which are generally both satisfied for agricultural plain watersheds. The validation of the method is done using a comparison with the wheat reference case, and its robustness is evaluated through a multiyear analysis. The fifth section gathers the conclusions. DATA ACQUIRED OVER THE ORGEVAL TEST SITE Presentation of the Orgeval Watershed The Orgeval watershed is located at about 70 km at the East of Paris (France). This basin was chosen as an experimental and representative basin for hydrological research. It has being managed for 35 years by the CEMAGREF. It is a rather flat area 104 km2 large. Its main part is covered with a thick table-land loess (up to 10 m thick), characterized by low permeability. The soil is a very homogeneous leached out brown soil. Regarding the texture, the upper layer is a silt loam. Geophysics characteristics being homogeneous, the main heterogeneousness factor of the Orgeval watershed is induced by the different soil cover type features. It is mainly an agricultural area: about 60 % of the total area is covered by crops. Table 1 shows the percentages of the different land cover types which could be found there in 1995–1996 and 1996–1997 “agricultural years.” The Orgeval agricultural year lasts from November (beginning of soil practices) to October of the following year (end of the harvest). The main crops are wheat, corn, peas, and barley. The remaining crops are flax, colza, and sugar beet. In Table 1, the differences in crop distribution observed between two consecutive years are due to the crop rotation cycle, which, in the case of the Orgeval, lasts 3 years. Data The needed ground truth measurements are i) the surface soil moisture, ii) the soil roughness state, and, in or-

der to estimate the vegetation contribution to the backscattered radar signal, iii) the biomass and the geometric features of leaves and stalks (dimensions and orientations). In 1995–1996 agricultural year as well as in 1996– 1997 one, these measurements have been regularly collected on eight test fields (about 30 samples are collected in each test field), chosen to be equally spatially distributed over the watershed and representative of the Orgeval crops. Figure 1 shows the 1995–1996 Orgeval land cover map, derived from satellite imagery, with the test fields pointed by a star. To estimate volumetric soil moisture at watershed scale (characterizing the whole hydrological state of the basin), we first converted gravimetric soil moisture contents measured on test fields into volumetric ones (this conversion was done according to the known bulk density of soil), and, then, we performed a weighed averaging according to the crop distribution. Finally, we note that the ground truth soil moisture values have been estimated in the upper 0–5 cm soil layer. This sampling depth was a compromise between the convenience of sampling campaign, the reliability of ground truth estimation, and the penetration depth of a microwave signal at C-band (which depends on soil water content, but is of same order as the wavelength) The radar data we used in this study are ERS1 and ERS2 images. The ERS (European Remote Sensing Satellites) Synthetic Aperture Radar is a C-band (wavelength 5.6 cm) and VV polarization system. In the case of 4-look images, the pixel resolutions in distance and in azimuth are both equal to about 12.5 m. From November 1995 to October 1997, 32 ERS PRecision Images (SAR-PRI) were acquired over the Orgeval site (15 images during 1995–1996 and 17 in 1996–1997). Ground truth campaigns have been systematically conducted simultaneously to satellite data acquisition. First, all ERS images have been set in the same geometry. The error of this process was between 1 and 2 pixels both in row and in column. Secondly, for each agricultural year of the study, we performed image classification in order to obtain the annual land cover map (changing every year because of crop rotation). Actually, we need to know the vegetation characteristics of the target corresponding to a given pixel (backscattered radar signal), and we chose to get it by using unsupervised classification method. The used method combines multitemporal ERS/SAR images with a LANDSAT visible/infrared image (Le He´garat-Mascle et al., 2000). The two

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Figure 1. The Orgeval watershed culture types in 1996–1997 (1.2 cm⫽1 km). The stars indicate the test fields.

LANDSAT images used (one per year) have been respectively acquired on 20 July 1996 and on 29 May 1997. Table 2 shows, as the results of this multisource classification, the identification rates of the different land cover types present over the Orgeval site. The differences between the two studied years in terms of identification rates can be explained first by a difference in the number of ERS images used for classification (10 in 1996–1997 against 6 in 1995–1996) and secondly by a difference in the acquisition dates of ERS and LANDSAT images. For our purpose, since most of land cover types were well identified, we could study each of them independently.

DERIVATION OF THE REFERENCE RELATION BETWEEN RADAR MEASUREMENT AND SOIL MOISTURE: THE WHEAT CASE As seen in the Introduction, the retrieval of soil moisture from radar measurement requires a two-step analysis. The first step consists in identifying and separating the soil contribution from the vegetation one. The second step consists in separating the roughness and soil moisture effects in the soil contribution to the backscattered SAR signal. In this section, we present the basic strategy which enables us to free from the soil roughness and vegetation dependencies in the case of some particular targets: the

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Table 2. Unsupervised Multisource Classification Results: Identification Rates of the Different Land Cover Types Present over the Orgeval Site in 1995–1996 and 1996–1997 Agricultural Years Identification Rates

1995–1996

1996–1997

Wheat Corn Peas Barley Flax Colza Sugar Beet Forest Grass land Bare soil

98% 88% 91% 82% 99% 86% 66% 93% 64% 60%

92% 93% 96% 97% 73% 85% 41% 94% 69% 33%

wheat fields. We focused on wheat because Taconet et al. (1996) showed that, for this crop, soil moisture can be derived from radar measurements, and because it is the main crop present on the Orgeval basin. We first show how the information about vegetation features (geometry and biomass) allows the determination of the period when soil moisture can be retrieved from wheat field backscattered signal and by the means of a correction of the global radar response. Then, we determine an empirical linear relationship between radar measurements and soil water contents. This is the relationship which will be used, in the following sections of the article, as the reference to describe the radar sensitivity to soil moisture variations. Vegetation Effect From November to July, that is, during most of the agricultural year, wheat fields are covered by a vegetation layer. Wheat field radar response is thus composed of a soil contribution and a vegetation one, whose proportions vary during the crop cycle. In this part, we quantify these components (soil and vegetation) to determine the possibility or not of retrieving soil moisture during the agricultural year. For this purpose, we use a first-order radiative transfer model derived from the equations given in Karam et al. (1992). Wheat canopy is modeled as a half-space containing sparsely distributed elliptic-shaped leaves and cylindrical-shaped stalks. Wheat ears are not considered in this modeling. Table 3 gathers the Orgeval values of the main input parameters of the model. The two dimensions of the leaf: length l (major axis) and width w (minor axis) respectively increase up to 20 cm and 1.6 cm, whereas the leaf thickness is supposed constant and equal to 0.2 mm. The wheat stalk dimensions: height h and diameter d increase up to 90 cm and 0.4 cm. The leaf and stalk angular probability distribution functions p(a), p(b), and p(c), where (a,b,c) are the Eulerian angles, were supposed constant. p(a) is assumed

to be uniform, according to the azimuthal symmetry, both in case of leaves and stalks. p(b)⫽sin2(b) (respectively cos3(b)), and p(c)⫽d(0) (respectively is uniform) in the leaf modeling (respectively stalk modeling). These functions have been derived from (Karam et al., 1992). The third column of Table 3 presents the overall height of the wheat layer H, equal to the height of wheat stalks h from April to July. Measurements of the wheat field scatter densities have been done only during the second year of the study. They were about 500 stalks m⫺2 and 2000 leaves m⫺2. Because of the lack of data, we have assumed constant i) these densities from March to July and ii) from the first to the second year of study. We note that there was no attempt to take into account the differences in the apparent density depending on whether we are in the row direction or not (no row structure modeling), although agricultural fields obviously present row structures. The dielectric constants of leaf (el) and stalk (est) have been estimated from gravimetric water content measurements and using the dielectric model of Ulaby and ElRayes (1987). The soil dielectric constant has been performed from volumetric soil moisture and from the soil textural composition (Dobson et al., 1985). Finally, Table 3 gathers the main output of the model. The optical thickness s increases from November to May, the highest value being equal to 3.1. The radar signal attenuation through the plant canopy is at its maximum at the beginning of June. From June to July (which corresponds to the senescent period), the decrease of the plant water content induces a decrease of the vegetation contribution to the attenuation of the radar signal from soil. Comparing the contributions of soil and vegetation, the backscatter from the soil was found negligible in May and June. During these 2 months, the measured radar signal is about equal to the calculated vegetation term and thus no information about soil features can be inferred from it. Finally, the last column of Table 3 shows the correction terms corr r in dB which should be applied to the measured value of r0 expressed in dB, to retrieve the soil contribution in dB: rmes⫽rveg⫹rsoil·e⫺2·s⇒corr r (dB)

冢

冣

r ⫺20·s ⫺10·log10 1⫺ veg , ln(10) rmes

⫽

(1)

where rmes is the measured wheat field radar response, rveg is the radar backscatter calculated for the vegetation layer, rsoil is the radar backscatter from the soil, and s is the optical thickness of the vegetation layer. Excepting months of May and June, these corrections vary from 0 dB to 5 dB. These results are in good agreement with the corrections given in Taconet et al. (1996) using a much simpler model and much less a priori information.

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Table 3. Wheat Parameters Fitted on the Orgeval Databasea Main Input of the Model Date 1

Mar 04

9

Mar 20

9

Apr 23

5

May 13

—

May 28

9

Jun 17

6

Jul 03

1

Mar 24

9

Apr 09

9

Apr 28

6

May 14

—

Jun 02

9

Jun 18

7

Jul 07 Jul 23

Results

Dim. Leaf (cm)

Dim. Stalk (cm)

H (cm)

el

est

es

s

rveg (dB)

corr r (dB)

l⫽5 w⫽0.5 l⫽10 w⫽1 l⫽20 w⫽1.6 l⫽20 w⫽1.6 l⫽20 w⫽1.6 l⫽20 w⫽1.4 l⫽20 w⫽1.2

h⫽0 d⫽0 h⫽2 d⫽0.2 h⫽30 d⫽0.3 h⫽60 d⫽0.4 h⫽70 d⫽0.4 h⫽80 d⫽0.4 h⫽90 d⫽0.4

5

30.4⫺j1.44

30.4⫺j14.4

15.6⫺j2.7

0.0

⫺42.0

0.0

8

31.2⫺j12.9

31.2⫺j12.5

11.2⫺j1.7

0.1

⫺29.3

0.8

30

31.5⫺j11.6

31.5⫺j11.2

16.5⫺j10.8

0.5

⫺16.2

1.8

60

31.3⫺j12.7

31.3⫺j12.3

5.3⫺j0.6

2.3

⫺15.6

3.5

70

31.5⫺j11.6

31.5⫺j11.2

14.9⫺j2.5

3.1

⫺15.3

9.5

80

10.0⫺j3.3

19.2⫺j6.1

6.9⫺j0.8

2.2

⫺14.1

3.1

90

6.5⫺j2.1

10.0⫺j3.4

5.7⫺j0.6

1.1

⫺14.5

1.6

l⫽10 w⫽1 l⫽16 w⫽1.2 l⫽20 w⫽1.6 l⫽20 w⫽1.6 l⫽20 w⫽1.6 l⫽20 w⫽1.4 l⫽20 w⫽1.2 l⫽20 w⫽1

h⫽2 d⫽0.2 h⫽20 d⫽0.3 h⫽40 d⫽0.3 h⫽60 d⫽0.4 h⫽70 d⫽0.4 h⫽80 d⫽0.4 h⫽90 d⫽0.4 h⫽90 d⫽0.4

8

31.2⫺j12.9

31.2⫺j12.5

13.8⫺j2.3

0.1

⫺29.2

0.8

20

31.6⫺j11.2

31.6⫺j10.8

8.9⫺j1.3

0.2

⫺19.0

1.0

40

31.5⫺j11.8

31.5⫺j11.8

14.1⫺j2.3

0.7

⫺14.5

3.8

60

31.5⫺j11.9

31.5⫺j11.5

13.8⫺j2.3

2.3

⫺15.3

8.1

70

19.2⫺j6.8

31.6⫺j10.5

7.0⫺j0.9

2.9

⫺15.6

3.6

80

10.0⫺j3.5

19.2⫺j6.5

13.7⫺j2.2

2.2

⫺14.1

6.9

90

6.5⫺j2.1

10.0⫺j3.3

13.6⫺j2.2

1.1

⫺14.1

5.1

90

6.5⫺j2.0

6.5⫺j2.0

8.1⫺j1.1

0.6

⫺16.3

2.4

a Leaf length l and width w in cm, stalk height h, and diameter d in cm, vegetaion layer height H in cm, dielectric constant of leaf el, stalk est, and soil es, vegetation layer optical thickness s, vegetation backscattered signal rveg in dB, and correction value in dB.

Concerning May and June, rveg⬇rmes. Thus, a small error in rveg estimation may induce large error in corr r estimation [Eq. (1)]. This is underlined by the dispersion of the correction values obtained for these 2 months. We now consider, quantitatively and throughout the vegetation cycle, the decrease of the radar signal sensitivity to soil moisture variations due to the vegetation canopy. Figure 2 represents the variation of the wheat field backscattering coefficient (⌬r in dB) caused by an increase of soil moisture (⌬Wv in %) equal to 5%, 10%, 15%, or 20%. For this figure, we estimate soil contribution assuming an empirical linear relation between rsoil, and soil moisture Wv (Griffiths and Wooding, 1996; Taconet et al., 1996; Weimann et al., 1998). The used slope 0.3, which has been fitted considering only measurements over bare soil, is consistent with IEM simulations using soil parameters similar to those encountered over the Orgeval. We underline that the curves presented in Figure 2 are independent of soil roughness value provided that this value remains about constant all along the year. Indeed, an increase (respectively decrease) of soil roughness would induce a positive (respectively negative)

Figure 2. Variation of the wheat field backscatter (⌬r in dB) in response to an increase of soil moisture (⌬Wv in %) equal to 5%, 10%, 15%, or 20%, simulated from November to July: evolution of radar sensitivity to soil moisture along a vegetation cycle.

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translation of the curves representing the backscattering signal versus the soil moisture. From Figure 2, we note that ⌬r (for a given ⌬Wv) is constant during the bare soil period, then it decreases during the sparse vegetation period to become minimum during the dense vegetation period, and finally it reincreases during the senescent period. Clearly, this agrees with the fact that the smaller the soil contribution term is (relatively to the vegetation one), the less precise is the soil information we can deduce from radar measurements. To determine the periods during which no information on soil moisture can be retrieved, its is assumed that radar signal variations smaller than 1 dB cannot be interpreted in terms of significant radar signal change. This minimum radar resolution (1 dB) is determined by the different sources of error or noise which affect the measured radar signal: ERS calibration, speckle, model simulations, etc. Then, Figure 2 shows that, during some periods of year, the induced radar increase (⌬r) becomes smaller than 1 dB (bold line) and thus the radar signal cannot provide useful information about the corresponding soil moisture variation. For example, in the case of ⌬Wv⫽20%, ⌬r is smaller than 1 dB from the middle of May to the middle of June. When considering smaller variations of soil moisture, the “blind” period increases. In this study, we want to be able to detect soil moisture variations equal or greater than ⫾5%. This implies that, from the beginning of May to the end of June, radar signal is useless to detect soil moisture variations at the desired precision. During the rest of the year, we can use it, provided that the corrections of the wheat canopy effect are applied when needed, that is, from March to April and in July (sparse vegetation periods). For the following of the study, the wheat canopy corrections (during the sparse vegetation period) have been performed, leading to “soil equivalent” radar signal. Roughness Effect We now focus on the changes, at watershed scale, induced by the soil roughness differences observed from one wheat field to another and through the agricultural year. The aim is to determine, at this scale, the periods characterized by a constant mean roughness effect (in order to attribute the changes in radar measurement values to some changes in the soil water content). Over bare soils, the backscattering coefficient depends on: • The soil physical properties through the dielectric constant (Dobson et al., 1985). • The soil geometrical features (roughness) for a given configuration of observation. Two main spatial scales of roughness (large scale: rows; small scale: clods) influence the radar response (e.g., Beaudoin et al., 1990; Rakotoarivony et al., 1996).

On the Orgeval basin, some soil practices are done on stubble fields from September to October. The aim is to prepare soil to receive green manure. During this period, roughness states are highly variable and difficult to control. Thus, we limited the study of roughness effects to the period from November to August. For a given period of study, the basic idea is to overcome the soil roughness dependency in the relative radar measurements by considering the areas which have approximately a constant roughness effect. Over wheat fields, soil roughness effects due to small scale features can be considered as isotropic and constant during almost the entire growing season (Taconet et al., 1996). Thus, in this section we focus on the study of the large scale roughness effects. First, the wheat fields have been classified in three classes according to their furrow direction as viewed by the radar beam: perpendicular, diagonal, or parallel look angle. These three classes have been obtained from image classification. For each class, the angular tolerance is about ⫾20⬚ so that all the wheat fields present on the Orgeval site are considered. The aim of this angular classification is to distinguish between three main classes of large scale roughness contribution (while the possible variability within each of these classes is not taken into account). Each of these three classes is assumed to be homogeneous in terms of large scale roughness contribution. Secondly, we distinguished between two periods: (P1) from November to March, and (P2) from April to August, excepting May and June. During each of these two periods, we assume the three angular classes to be characterized by an about constant mean roughness effect. Figure 3a shows the ERS/SAR r0 (in dB), measured over the wheat fields exhibiting a perpendicular look angle, versus volumetric soil water content (in %). Each point represents a different acquisition date (16 in all): Black points correspond to P1, and white ones to P2. For a given point, the represented r0 value is the average of all the r0 values measured (on this date) over perpendicular wheat fields, and the represented soil moisture value Wv is the average of all the volumetric soil moisture values measured on wheat test fields (ground truth measurements). The r0 versus Wv dependency has been fitted by two linear relations respectively corresponding to each of the two periods P1 and P2. The slopes ⳵r1/⳵Wv and ⳵r2/⳵Wv (of these two linear relations) have been reported with their respective error bars (Berington, 1969). Figures 3b and 3c are similar to Figure 3a in the cases of diagonal and parallel furrow directions: The same dates (thus, the same number of points) have been considered, but the r0 average was not performed on the same wheat fields. First, we note the good agreement between the slopes of all these linear relations (about 0.27). However, we also note the highly variable nature of the offset of these relations, either comparing the different cases of furrow direction (Figs. 3a, 3b, and 3c), or

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Figure 3. Backscatter (r0 in dB) of wheat fields versus volumetric soil moisture (Wv in %) in the case of furrow direction: a) perpendicular, b) diagonal, c) parallel relative to the radar look angle, or d) without distinction between furrow direction. P1 period (from November to March): 䊉, solid line; P2 period (from April to August, excepting May and June): 䊊, dashed line. ⳵r1/⳵Wv and ⳵r2/⳵Wv are the respectively obtained slopes with their error bars.

comparing P1 and P2 periods in the perpendicular or parallel cases. Indeed, the main effect of the roughness is to modify the offset of the relation. For example, the r0 values corresponding to the perpendicular case are greater (whatever be the period P1 or P2) than those corresponding to the parallel or diagonal case, and the “parallel” r0 values are the smallest. This is a well-known effect produced by regular roughness such as furrows on the radar signal (Ulaby et al., 1986). Concerning Figures 3a, 3b, and 3c, some additional comments may be given: The dispersions of the scatter diagrams are higher during P1, especially for measurements at perpendicular look angle (correlation coefficient R⫽0.59). In fact, during P1, from a date to another, wheat fields row pattern (soil and vegetation) is not strictly constant. From November to March, the soil row pattern is regularly smoothed by weathering (decrease of the soil ridge height) and the vegetation furrow pattern also decreases because of the plant overlap. (Conversely, during P2, roughness and anisotropy vegetation effects are stabilized, and the dispersion of perpendicular measurements significantly decreases: R⫽0.94). The greater sensitivity to large scale roughness of the signal backscattered by perpendicular fields rather than parallel ones has been checked by simulations from the integral equation model (IEM), which showed that a small RMS height variation of the large scale roughness (from 0.5 to 0.9 cm) induces a higher dispersion for the perpendicular fields than for the parallel ones (2.7 dB against 0.7 dB). The anisotropy effect of the wheat field furrow direction, estimated by the mean difference between the perpendicular and the parallel r0, is more important dur-

ing P1, 3.8⫾0.3 dB, than during P2, 1.6⫾0.6 dB. In the case of the perpendicular fields, P2 points exhibit smaller values because of the row pattern smoothing (soil, vegetation). In the case of the parallel fields, the roughness to consider is the small scale one (clods), which is about stabilized at the beginning of winter (after the first rain events as a first guess). Then, the difference (of about 0.5 dB) between the P1 and P2 linear relations cannot be “attributed” to soil roughness change between P1 and P2. We think it is caused by an overestimation of the real vegetation contribution in the case of parallel fields. This overestimation may be due to the fact that the anisotropic distribution of wheat rows is not taken into account in the modeling of the vegetation effect. To come back to our purpose (soil moisture estimation from SAR signal), we conclude, from Figures 3a, 3b, and 3c, that, if one wants to retrieve a wetness index from radar signal measured over wheat fields, he can do it without knowing roughness absolute value, provided that he distinguishes between P1 and P2 periods if considering perpendicular or parallel fields. Otherwise, the slope of the r0 versus Wv relation will be increased artificially from 0.28 to 0.34⫾0.04 for the perpendicular case, or decreased artificially from 0.25 to 0.22⫾0.02 for the parallel case. In the case of the diagonal fields the distinction between P1 and P2 is not needed. In Figure 3d, the distinction between the three classes of view angle was no longer considered: the r0 average was performed over all wheat fields. The considered dates are still the same and the distinction between P1 and P2 periods is still represented. We first note that the resulting points are very close to the “diagonal” points

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(Fig. 3b). This can be explained by the fact that diagonal furrow fields are in majority over the Orgeval watershed (about uniform distribution of furrow direction relative to the radar look angle, between ⫺90⬚ and 90⬚, and diagonal fields corresponding either to ⬇⫹45⬚ or ⬇⫺45⬚ directions), and by the fact that the perpendicular and parallel r0 values are about symmetrical relative to the diagonal ones. Then, comparing P1 and P2 relations (Fig. 3d), we note that they are very close (about same slope and offset). Moreover, the dispersion remains weak (correlation coefficient R⫽0.90 and 0.97 respectively for P1 and P2). Hence, we conclude that, at watershed scale, wetness indices could be retrieved from ERS/SAR measurements, over wheat fields, through a single linear relation. Estimation of Soil Moisture on Wheat Target To verify the possibility to estimate soil moisture from radar measurements over wheat fields with a single relation, a linear relation has been fitted between the radar signal and the ground truth volumetric soil moisture considering both the P1 and P2 periods. The relation is given in Eq. (2): r0 (dB)⫽⫺14.19⫹0.28⫻Wv (%),

(2)

where the volumetric soil moisture value Wv corresponds to the average of the ground truth measurements collected over the wheat test fields, and the r0 value to the radar signal average over all the Orgeval wheat fields. Taking into account the uncertainties of the parameters of the linear regression (Berington, 1969), the slope of this fitted relation is 0.28⫾0.02. We note that it is slightly higher than the one obtained when considering the two periods separately: 0.27⫾0.06 or 0.27⫾0.03. However, these values remain consistent if we consider their respective uncertainties. The obtained slope value is also consistent with the radar sensitivity over bare soils pointed out with another set of data by Taconet et al. (1996): 0.31. Finally, the small dispersion (R⫽0.95) confirms the fact that, at sufficiently large scale, a linear relation is a rather good approximation of the relation between the soil moisture and the backscattering radar signal. The possibility to use a single linear relation is a rather convenient result since it means that, at the watershed scale, and only considering relative variations of soil moisture, the roughness effect does not have to be taken into account. We underline that this is not true at some significantly smaller scales than the watershed one. At that scale, the noise on the relation introduced by roughness differences at field scale tends to be blanked by the spatial averaging process taking into account the distribution of wheat field row direction. At smaller scale, for example, at field scale, it would not be so. In this last case, soil moisture estimation from ERS/SAR measurements requires, for each field, some corrections of its specific contribution depending on the characteristics of the soil practice (row direction, RMS height, and

other roughness parameters). Same remark applies in the case of a test site presenting a majority of perpendicular wheat fields, where at least two relations should be used to estimate reliable soil moisture indices. Finally, we emphasize that even if soil moisture index is derived at watershed scale, the high spatial resolution of the SAR data is necessary to select the targets over which it will be estimated. From wheat field measurements, the retrieval of soil moisture index is possible during 2/3 of the whole agricultural year (from November to August) and only if wheat fields are mainly present over the considered watershed. In the next section, we propose a method called “multiselective,” which enables the relaxation of these two constraints. As in the case of the wheat selective method (with no distinction between the different classes of roughness), the validation of the method will be based on the comparison of the obtained radar sensitivity to soil moisture with the reference sensitivity: ⬇0.27. MULTISELECTIVE METHOD Methodology This method is based on the selection, image per image, of targets where the backscattered radar signal will be used for soil moisture estimation. This selection is done according to the possibility or not to retrieve information about soil moisture from ERS/SAR measurements over the considered target. In the following, we call a “sensitive target” a target where soil moisture retrieval is possible. As in the previous section, the presence of a vegetation canopy is a key parameter for target selection, which is mainly done according to the growth state of the plant canopies. In the case of the Orgeval basin, we focus on four main crops: wheat, barley, corn, and peas. This was done in order to consider only large (representative of the basin) surfaces. The vegetation growth states of these four Orgeval main crops are given in Table 4. Growth states have been separated in three classes: bare soil, sparse vegetation, and dense vegetation. Sparse vegetation periods include senescent periods. An interpretation in terms of soil component retrieval is also reported: for each month, the presence or not of a black point indicates if the radar signal (backscattered by the corresponding crop fields) can be used for soil moisture estimation or not. For example, as seen previously (Fig. 2), wheat is considered a “sensitive” target from November to August (except May and June months). We note that, in Table 4, wheat is also selected as a “sensitive target” during the period from September to October. Indeed, the target selection does not take into account the soil roughness state (conversely to the previous section) but only focuses on the vegetation features. Then, the harvested wheat fields (stubble or plowed fields) present during these months are also selected. For the other

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Table 4. Vegetation Growing States of the Main Orgeval Crops during an Agricultural Year a Month

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

Jun.

Jul.

Aug.

Sep.

Oct.

Wheat Barley Corn Peas

A* A* B B

A* A* A* A*

A* A* A* A*

A* A* A* A*

B* A* A* B

B* B B B

C C B C

C C C C

B* A* C C

A* A* C A*

A* A* C A*

A* A* C A*

A ⫽ bare soil, B ⫽ sparse vegetation, C ⫽ dense vegetation, and * ⫽ sensitive target.

crop types, the vegetation contributions have not been corrected. Indeed, the modeling used to correct the wheat canopy contribution is difficult to transpose to peas due to its complex geometry. In the case of corn and C-band SAR data, the ratio between the plant dimensions and the wavelength is out of the model validity domain. Finally, for the barley case, we do not have at our disposal the ground truth measurements needed for vegetation corrections. Therefore, in this study, all these fields are selected as “sensitive” targets only during their bare soil period. However, other studies are conducted in order to be able to model more various kinds of crops. The aim of this method being to get soil moisture indices representative of as large as possible part of the watershed (to get a reliable approximation of values at watershed scale), we averaged the SAR signal over all the areas (image pixels) which correspond to “sensitive” targets. For example, from December to February, ERS r0 is averaged over wheat, barley, corn, and peas fields (given by land cover map), whereas, in March, only wheat, barley, and corn are considered and, in April, only wheat is used. Before presenting the obtained results, two main comments have to be made about the proposed method: • In this method, no attempt is made to take into account the roughness states, since the target selection is only based on considerations about the presence or lack of vegetation and the possibility to correct for its effect. Thus, it is based on the assumption that mean soil roughness effect is about constant (during the year) at the watershed scale. Indeed, we saw that such an assumption was empirically validated for wheat from November to August (Fig. 3d). Here, we extend this hypothesis to the case of the “multiselective” target method, and its validation will be also empirically verified by comparison with the slope reference (0.27) of r0 versus soil moisture relation. • Since the size of the area for sensitive target varies from one month to another (from about 30% of the whole watershed in April, to more than 50% in December, January, or February), the representation of the soil moisture index is a priori not the same. However, in every case, it is greater than (or equal to) the representation of soil moisture indices derived from single target methods.

RESULTS AND COMMENTS Figure 4 represents the averaged r0 in dB (according to the multi-selective method) versus the mean volumetric soil moisture in %. For each point, which corresponds to a different acquisition date, the mean volumetric soil moisture value was obtained by performing the weighted (according to the crop distribution, cf. the second subsection of the second section) average of all the ground truth soil moisture measurements (conversely to the previous case where only wheat field measurements were considered). The empirical linear relation fitted on this scatter diagram is represented in solid line. The first important point showed by Figure 4 is the validation of the proposed method. We said that, in this method, no restriction on surface roughness has been considered for target selection, and that the validation of the constant roughness effect assumption will be done by comparison with the reference slope. Indeed, let us assume that roughness effect changes during the year. Then, either the dispersion of the scatter diagram would be greater or the slope of fitted curve would change, for example, if roughness is smaller in summer (where soil moisture can reasonably be assumed low), the slope will be increased artificially. Since, on the one hand, the global

Figure 4. Relation between the radar signal (r0 in dB) and volumetric soil moisture (Wv in %) established by the multiselective method, and during the whole agricultural year except from May and June months. ⳵r0/⳵Wv is the slope of the fitted linear relation and r0 (Wv ⫽ 0%) the offset.

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dispersion of the scatter diagram is small (which is either shown by the correlation coefficient R⫽0.94, or by the slope error bar: ⫾0.02) and very close of those obtained in Figure 3d, and, on the other hand, the slope is also very close to reference one (respectively 0.28 and 0.27), we conclude in favor of the validation of our assumption: At watershed scale, the relative radar measurements measured over “sensitive” targets are mainly dependent on soil moisture state. The performance of the multiselective method was evaluated through the accuracy of retrieved soil moisture indices. Under Gaussian assumption, the error bar including 90% of the Figure 4 points is equal to ⫾1.3 dB, which corresponds to an accuracy equal to ⫾4.5% for the retrieved soil moisture values. To explain the dispersion of the scatter diagram and thus the observed accuracy (or inaccuracy) of soil moisture estimations, several arguments may be put forward: • The ERS calibration errors. • The speckle (due to the coherent sum of scattering by targets within the pixel cell). Some studies, for example, Olivier and Vidal-Madjar (1994) or Griffiths and Wooding (1996), show that a sample size of 60 pixels is required to give a mean radar signal dispersion smaller than ⫾0.25 dB. • The fact that the modeling used for wheat corrections does not take into account the anisotropy effects. • The spatial variability of soil moisture at watershed scale. As seen in the second subsection of the second section, “ground truth” soil moisture values are estimated from measurements collected on a limited number of representative fields. Considering the Orgeval basin as belonging to an homogeneous pedological and geomorphic unit, we assume that the soil moisture variability over the basin is about equal to the variability between the test fields. In our data, we found the soil moisture variability to be smaller than 4–5%; • The approximation induced by the use of a linear relation (rather than more complex relations). Among these sources of inaccuracy (for soil moisture estimation), the three first ones concern the bare soil backscattering coefficient (the vegetation contribution having eventually to be corrected). The smaller the fitted curve slope is, the more important will be the soil moisture inaccuracy induced by a given inaccuracy on r0 values. Therefore, we underline the importance of the selection of “sensitive” targets. For example, if one intends to use a more simple way to interpret the SAR by identifying agricultural areas over the watershed (excluding forests and urban) and by taking the average of r0 over these areas, a slope equal to 0.25 is obtained. The advantage of such a method is that it can be used during the whole year. However, in

Figure 5. Evolution of the volumetric soil moisture of the Orgeval test site during two consecutive years: measured values (blue line), retrieved values from ERS/SAR data using the wheat selective method (green line), the multiselective method (red line), or the noselective method (black line). The hatched areas underline the periods during which some of these methods are not valid.

this case, as the dispersion of the scatter diagram is higher, ⫾3.3 dB on the r0 values, this leads to an inaccuracy of soil moisture estimation equal to ⫾12.8%, which is far greater than the ⫾4.5% obtained with the selective method. Thus, the interest of taking advantage of the high resolution capability of SAR to select suitable fields is clearly shown. Figure 5 summarizes the previous results. It represents the time evolution, during the two years studied, of the soil moisture indices estimated from ERS data at watershed scale, using: a) the wheat-selective method, green line; (b) the multiselective method, red line; and c) the nonselective method (which considers all the cultivated lands), black line. The measured soil water content (ground truth) is reported in the blue line. The periods during which soil moisture indices cannot be retrieved from the backscattering radar signal are represented by the hatched areas: brown for multiselective method; brown and green for the wheat-selective method (excluding dense vegetation period and stubble months, cf. the third subsection of the third section); and none for the nonselective method, which can be applied during the whole year. The time validity and the accuracy of each of these three methods both clearly appear in Figure 5. Robustness of the Multiselective Method from One Year to Another Before concluding this study, we tested the time robustness of the proposed method. For this, the r0-Wv relations relative to each agricultural year (1995–1996 or 1996–1997) have been studied separately. Figure 6 pre-

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model) and volumetric soil moisture is equal to 0.28, the offset of the relation being equal to ⫺14.7, and the correlation coefficient equal to 0.72. Finally, the less robust result should be the offset of our relation, which is determined by the global roughness effect of a given agricultural area and which depends strongly on local agricultural practices. CONCLUSION

Figure 6. Robustness of the relations between radar signal and volumetric soil moisture: comparison of the two yearly relations obtained independently in 1995–1996 and 1996–1997, and the relation obtained mixing the two-year data. For each year, ⳵r0/⳵Wv is the slope of the fitted linear relation and r0 (Wv⫽0%) the offset.

sents the linear relations fitted separately on each of the two yearly scatter diagrams. The empirical linear relation obtained mixing the two years (already shown in Fig. 4) was also reported in solid line. The slope of the linear relation remains very close (0.28–0.27) from the first to the second agricultural year, which shows that the radar sensitivity pointed out by the method remains stable from a year to another. However, from Figure 6, we also note a slight change in the offset values [r0(Wv⫽0%)]. As seen before, this parameter characterizes the mean soil roughness effect, which was assumed to be constant during a year. Therefore, the presence of a nonnull difference (0.7 dB) indicates a change from the first to the second year. This may be attributed to the crop rotation. However, since the dispersion is about the same considering only the one or two years (the Wv inaccuracies are respectively equal to ⫾4.2% in 1995– 1996, ⫾4.4% in 1996–1997 and ⫾4.5% in 1995–1997), it suggests that a relation established over the whole agricultural cycle (3 years) could be used in a reliable way for the following years. Finally, in this study, the described methodology was only applied in the case of the Orgeval basin. However, since the empirical results are in good agreement with theoretical models describing soil and vegetation signal, the methodology should be applicable to other watersheds. Furthermore, we think the slope of the empirical linear relation (r0 vs. Wv) should not much vary from a basin to another. This is confirmed by a study done in the case of Britain (France) basins: Cognard (1996) found that, over wheat fields, the slope of the obtained linear relation between ERS/SAR measurements (corrected from the vegetation contributions through a simple water cloud

In this article, we have shown the possibility of retrieving a mean hydric state of small agricultural watershed, from high resolution radar images such as those acquired by ERS1&2. This mean soil water content estimation is performed through the selection of some land cover types called “sensitive” targets, over which we are able to free from both vegetation and roughness effect on the global radar backscattering response. We first focus on a single “sensitive” target: the wheat crop. Indeed, some previous studies (Taconet et al., 1996) showed the possibility of monitoring soil moisture variations from radar measurements carried out on this specific target. Our contribution was then to show that: • Using a first-order radiative transfer model to correct the global radar response for the effect of the canopy during the sparse vegetation period, the radar signal may be also used to estimate soil moisture. Strong vegetation attenuation on radar signal will prevent the accurate retrieval of soil moisture only during the 2 months May and June, corresponding to a dense vegetation period. • At the watershed scale, the roughness effects are negated by the spatial averaging process which takes into account the distribution of wheat fields row direction (which is not true at field scale). Therefore, at watershed scale, the same relation between r0 and Wv may be used from November to August (excepting the months of May and June). The generalization of this soil moisture retrieval method to multiselection of targets permits first the extension of the time period of the retrieval process and second the increase of the characterized surfaces. The selection, image per image, of as much as possible “sensitive” targets is done according to vegetation effect correction possibilities. The validation of the method toward the problem of roughness effect was done by comparison with the reference relation (wheat case). We found that, at the watershed scale, the mean effect induced by the different mixed roughness states is about constant during the whole year, which allows retrieval of soil moisture variations from relative radar measurements performed over the sensitive targets. On the Orgeval test site, the good sensitivity of the radar signal to soil moisture, obtained

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by this multiselective method, leads to an accuracy in the retrieved soil water contents equal to ⫾4–5%. The main advantages of the multiselective target method relative to simpler methods are: • Relatively to a nonselective method, a better sensitivity of r0 to soil moisture and then a reduction of the error bar on soil moisture estimation. In the case of the Orgeval, error bar reduces from ⫾12–13% to ⫾4–5%. • Relatively to the wheat-selection method, i) a better representation of the global watershed hydrological state since soil moisture index is based on larger surfaces, and (ii) an extension of the time period during which radar signal may be used to retrieve soil moisture. On the Orgeval, the characterized surface increases from 30% to more than 50% in winter, and the time period increases from 8 months to 10 months (the whole year except May and June). Moreover, the selection of several crops takes into account the soil moisture heterogeneity induced by the specific soil draining of the different vegetation types. Finally, analyzing the robustness of the proposed method and the fitted relation, we found it is rather satisfying since the same sensitivity was obtained considering each agricultural year separately. The following step of this study will be the assimilation of the retrieved soil moisture values into an hydrological model. The aim of “assimilation” process is to correct the model drifts by taking into account observations (e.g., ground truth data or satellite Earth Observation data). Then, such a process should lead to better stream flow simulations and reservoir monitoring. In this context, the main drawback of our results, which is that the offset relation depends on the mean roughness effect of the particular basin and year considered, may be overcome, provided that the assimilation is based on relative variations of soil water content. Works are underway in this direction. The authors would like to thank ESA for the support of this study through the provision of ERS images in the frame of the AO2.F115 Project. This study was partly supported by the French National Remote Sensing Program (PNTS).The authors thank M. Zribi for helpful discussions.

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