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Remote Sensing of Environment 92 (2004) 233–246
Multi-scale analysis of intrinsic soil factors from SAR-based
mapping of drying rates
Tal Svoraya,*, Maxim Shoshanyb
aDepartment of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva 84105, IsraelbDepartment of Transportation and Geo-Information Engineering, Faculty of Civil and Environmental Engineering,
Technion-Israel Institute of Technology, Haifa 32000, Israel
Received 3 March 2003; received in revised form 1 April 2004; accepted 8 June 2004
Abstract
Intrinsic soil factors affect and are affected by the spatial variation of soil properties. Therefore, intrinsic soil factors may both characterize
and serve as an indicator for soil taxonomy. Difficulties in inferring intrinsic soil properties hamper attempts to assess their variability, on
both local and regional/broad scales. Radar remote sensing might facilitate a breakthrough in this field, due to its sensitivity to the soil water
content. In this research, a raster Geographic Information System (GIS) methodology is developed for combining multi-temporal ERS-2 SAR
and Landsat TM data, which allows the estimation of drying rate patterns in bare soil surfaces. The drying rates provide further indication
about intrinsic soil properties. The multi-scale behaviour of soil-drying rates is described using the richness–area curves and characteristic
curves are determined to four soil formations typical to a climatic gradient between Mediterranean and semi-arid environments in Israel. To
the best of our knowledge, this is one of the first attempts to document the effect of intrinsic soil factors on the soil system at the regional
scale. The results achieved here demonstrate the connection between drying rates, richness–area variation and soil hydraulic conductivity of
the four soil formations.
D 2004 Elsevier Inc. All rights reserved.
Keywords: SAR; Soil Moisture; Geographical Information Systems; Intrinsic Factors
1. Introduction
Soil properties vary with place and time (Heuvelink &
Webster, 2001). The magnitude and type of variation in soil
properties affect the interpretation of observations regarding
the evolution, diversity and dynamics of the soil system.
An integration of various sources of information and
synthesis of diverse approaches is required to study the
coexistence of order, complexity, chaotic/nonchaotic, sta-
ble/unstable and self-organization/non-self-organization in
soil systems, as well as relationships between bio-, pedo-,
topo-, hydro- and climodiversity (Phillips, 1999). It is the
emergence of the science of complexity—contending with
‘‘how simple, fundamental processes, derived from reduc-
tionism, can combine to produce complex holistic systems’’
(Malanson, 1999)—that might facilitate a better under-
standing of soil processes and patterns. Within this frame
0034-4257/$ - see front matter D 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2004.06.011
* Corresponding author. Tel.: +97-2-864-7379; fax: +97-2-864-2821.
E-mail address: [email protected] (T. Svoray).
of thought, there is a growing interest in examining Earth
systems with a multi-scale approach (Coops & Waring,
2001; Zhang et al., 2002). It is anticipated that this will
identify typologies of system behavior, from more complex
or chaotic characteristics at a fine scale of examination to
more regulated or ordered and stable characteristics at a
broader scale.
Water content is one of the most important soil
properties that determine vegetation productivity in
semi-arid regions (Kumar et al., 2002; Svoray et al.,
2004). While it is difficult and sometimes even imprac-
tical to map soil moisture in the field over large regions
(Mahmood, 1996), remote sensing approaches, such as
radar and thermal techniques are most adequate for this
purpose. The advantage of Synthetic Aperture Radar
(SAR) data over thermal data in determining soil mois-
ture is its ability to penetrate the upper soil strata (Ulaby
et al., 1986). This potential, however, is not fully
exploited and spatial patterns of intrinsic soil properties
derived from SAR data have not been presented yet in
the scientific literature. In areas of large soil variability,
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246234
the study of soil distribution is particularly important for
global and understanding global processes, such as cli-
mate change and response of Earth systems to distur-
bance (Thornes et al., 1996).
The aim of this research is to assess the potential of
integrating remotely sensed SAR data with multi-scale
modeling of intrinsic soil factors and to identify soil
moisture patterns that represent characteristic spatial soil
complexity of four Mediterranean soil formations.
2. Extrinsic and intrinsic soil factors
Extrinsic factors are environmental controls on the soil
that relate mainly to the components of the clorpt model
(Jenny, 1941). The model’s name is an acronym where: cl
refers to the climatic conditions, o to the effect of biotic
factors (organisms), r is the relief, p is the parent material
and t refers to the time effect. Jenny (1961) argues that the
clorpt model variables define the state of the soil system
within which pedogenetic processes occur. The effect of
pedogenetic processes was acknowledged to operate at the
fine scale, while the clorpt variables have more influence at
broader spatio-temporal scales (Paton, 1978). The studies by
Birkeland (1984) and Hoosbeek and Bryant (1992) provide
a theoretical view and empirical evidence to the operation of
soil processes within the framework of the environmental
state factors.
Factors in clorpt model are inferred indirectly (exclud-
ing the relief that can be mapped directly), thus limiting
its accuracy for soil predictions. Despite this limitation,
many studies have used Jenny’s function to map soil
(e.g., Burrough et al., 1992; Zhu et al., 2001). These
studies have usually combined field knowledge, ancillary
and remotely sensed data in a GIS framework using fuzzy
logic. However, other studies show that the extrinsic
factors are insufficient to explain fully the spatial varia-
tion of soil (Ibanez et al., 1998; Phillips, 1998; Webster,
2000).
Intrinsic factors provide complementary information that
can be related to variation in the initial conditions and their
perturbations that increase over time and make the soil
system dynamic and variable (e.g., Seydel, 1988). Intrinsic
factors are also related to soil attributes which might be the
outcome of long-term pedogenetic processes (Phillips,
2001). The importance of intrinsic factors to understand
soil variation has already been acknowledged and was
linked to patterns of physical and chemical soil properties
(Phillips, 1999; Skidmore & Layton, 1992). In a recent
work for downscaling remotely sensed soil moisture with a
fractal interpolation method, Kim and Barros (2002b) claim
that a calibration of their algorithm reflects the need for
modeling soil intrinsic factors for which there are no
ancillary data.
A general model to quantify the scale of variation of
intrinsic factors is the ‘‘richness–area’’ curve, which has
the following form, as proposed by Phillips (2001) for
use on soil:
Si ¼ ciAbii ; ð1Þ
where S determines the number of soil groups and A is
the area covered by the groups. The coefficients c and b
are determined empirically; where c represents the inher-
ent diversity associated with any unit i; and b represents
the tendency of large soil areas to increase diversity
independently of environmental heterogeneity. Until re-
cently, the lack of tools to estimate the variation of soil
properties over wide regions has made it difficult to
examine the validity of the ‘‘richness–area’’ curves using
Eq. (1) at the regional scale. Assuming that the necessary
information is available, there is still a fundamental
question concerning the range of scales valid for repre-
senting ‘‘richness–area’’ models: Are these relationships
valid at the scale broader than the plot scale?
In this study, we focus on the intrinsic factors that affect
soil moisture content. Soil moisture is a critical factor in the
functioning of semi-arid ecosystems, due to low rainfall and
long dry periods. The recurring soil-drying processes have
attracted widespread attention in soil studies in such regions
of the world (Kumar et al., 2002). The frequency and
magnitude of soil-drying processes depend on climate and
micro-climate, as well as on the physical and chemical
properties of the soil; together, they determine the soil
water-holding capacity.
Recent studies by Kim and Barros (2002a, 2002b) had
stressed that it is not the momentary soil moisture but
rather the soil texture which determines the soil hydraulic
conductivity. However, both soil texture and hydraulic
conductivity cannot be directly estimated or mapped at
the regional scale, while soil moisture can be inferred
from radar remote sensing. Soil drying rates as estimated
according to temporal changes in the soil moisture
following the end of external supply of water (rainfall,
runoff, flooding) may function as indicators of soil
texture and hydraulic conductivity. This avenue had been
exploited earlier by Barros et al. (2000) using a spatial
resolution of 1000 m. However, it was not yet examined
for a finer scale. Increasing the spatial resolution allows
better expression of soil properties variation which then
may be linked to ‘‘richness–area’’ curves. Soil at the
scale of meters is composed by patches of different
texture/hydraulic conductivity properties. Classifications
at this scale assume characteristic compositions and
heterogeneity. We hypothesize here that wide regional
soil formations are characterized by spatial variation of
temporal change in soil moisture which follows the
‘‘richness–area’’ curves. In other words, wide regional
soil formations can be differentiated according to values
of coefficients c and b determined for Eq. (1) based on
spatio-temporal soil moisture data.
Fig. 1. The relationship between the measured VSM concentration and
corresponding NBMI calculated for the study area at all dates.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246 235
3. Monitoring soil moisture with radar remote sensing
The intensity of radar backscatter is determined mainly
by three characteristics of a target: dielectric constant,
topography and roughness (Ulaby et al., 1986). The dielec-
tric constant (eV) is a complex number of two parts (Eq. (2)),
whereby the real part (eV) is the relative permittivity and the
imagery part (eU) refers to the dielectric loss factor:
e ¼ eV� jeU: ð2Þ
To determine the dielectric constant of soil (esoil), Ulabyet al. (1986) suggest a model (Eq. (3)) based on the density
of solid material (qss), bulk density (qb), the dielectric
constant of the solid materials (ess), the volumetric soil
moisture (VSM) content (mv) and the dielectric constant
of free water (efw):
easoil ¼ 1þ qb
qss
ðeass � 1Þ þ mb
vðeafw � 1Þ: ð3Þ
Ulaby et al. (1986) show that the change in the soil
dielectric constant as a result of variation in soil moisture
content has more influence than other characteristics on both
imagery and real components. Thus, dry soil of any kind
results in smaller (tenfold) dielectric constants than humid
soil. Since soil moisture depends on other soil character-
istics, the dielectric constant of different soils has been
studied widely. For instance, Dobson et al. (1985) show
that the dielectric constant can be related to bulk density and
soil texture through the content of clay and sand in the soil.
This methodology could be further used to derive unique
soil moisture backscatter to each soil formation. Later, Wang
et al. (1995) show that there is an increase in the dielectric
constant with the increase in soil moisture at three radar
bands: C, L and P (with wavelength ranges of 5.21–7.69,
19.4–76.9 and 76.9–133 cm, respectively). Wang et al. also
show that differences in the dielectric constants of soil types
for each of these bands, within the range of moisture
relevant to the environment studied in our research (10–
30%), are negligible.
These findings have encouraged researchers to use the
empirical approach (and semi-empirical, Bindlish & Barros,
2001) for soil moisture monitoring with radar systems.
Studies such as Prevot et al. (1993) describe the relationship
between VSM and soil radar backscatter (r0 [dB]) as linear,positive and strong (Eq. (4)).
r0 ¼ a VSMþ b: ð4Þ
The slope coefficient, a, is related mainly to the signal’s
sensitivity to change in soil moisture concentration. The
intercept coefficient, b, represents the backscatter of dry
soil. The soil depth examined in most of the studies that
have used C-band radar is 0–5 cm, which is assumed to be
the penetration depth at this wavelength (Ulaby et al., 1986).
Local factors appear to have a relatively small effect on
backscatter for the ERS-2 SAR configuration, but in ex-
treme cases, they might lead to considerable error in soil
moisture predictions. Most of the attempts to modify the
linear model have used multi-angular approaches that are
not feasible in the case of the ERS-2 SAR data. As an
alternative, Shoshany et al. (2000) proposed the Normalized
Backscatter Soil Moisture Index (NBMI). This index is
based on a multi-temporal approach for generalising the
soil moisture model using a ratio technique that enables
common multiplicative effects on backscatter mainly due to
differences in soil type and surface roughness to be reduced.
The model is based on the assumptions that: (i) the effect of
roughness on backscatter is independent of soil moisture
conditions and does not vary in natural, undisturbed areas
along the time scale of one season; and (ii) seasonal change
within 1 year does not have any effect on soil type.
The NBMI is used in the current research in the form of
Eq. (5), which Svoray (2000) found to be more suitable to
the study area than the original form:
NBMI ¼ dBt1 � dBt2
dBt1 þ dBt2
; ð5Þ
where dBt1;2 are backscatters [dB] at different times (t1, t2,
etc.).
The empirical relationship between NBMI and soil
moisture was strongest in the form of Eq. (6):
VSM ¼ aðNBMIÞ þ b; ð6Þ
where VSM is the volumetric soil moisture concentration
[%] and a and b are empirical coefficients.
Fig. 1 shows a strong relationship between NBMI and
VSM of 0–40% concentration. Lower (0–10%) and upper
(25–40%) parts of this range occur at widely scattered
points. This range corresponds with the study of Griffiths
and Wooding (1996), who reported that there is a significant
positive correlation between VSM in the 10–40% range and
the ERS-1 SAR backscatter. Shoshany et al. (2000) and
Svoray (2000) had further implemented Eq. (6) for an area
of wide variation of soil properties along a climatic gradient.
This data will further facilitate the assessment of ‘‘richness–
areas’’ curves.
Fig. 2. The study area.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246236
4. Typifying soil-drying rates
Intrinsic soil factors and soil drying rates have attracted
the attention of the scientific community. For example,
Barros et al. (2000) have classified soil texture based on
temporal analysis of soil moisture levels using L-band
ESTAR data at the resolution of 200 and 1000 m per pixel.
In another research, an overlay technique has been proven
useful for identifying soil drying typologies over large areas
(Shoshany et al., 1995). Shoshany et al. used the GIS
MATRIX technique to create a unique class value for each
coincidence of two class values of the input layers by
combining the spatial co-occurrence of herbaceous vegeta-
tion changes from Landsat TM images of two dates.
In the present study, we use the MATRIX technique to
examine the temporal change in five soil moisture levels
derived from SAR remotely sensed data (see Section 5.3).
The five levels were derived for each of the soil moisture
values determined at the pixel level for February, April and
May 1997 images, using the standard deviation classifica-
tion procedure (uses the mean value and locates class breaks
above and below the mean at an interval of one standard
deviation).
The application of the MATRIX algorithm to the transi-
tion between February and April results in 25 moisture
combinations for that time interval. These combinations,
together with the five moisture levels determined for May
1997, result in 125 combinations. The output class of the
two MATRIX calculations represents the change of soil
moisture concentration from winter to summer (through
spring); in other words, it forms drying rate groups from a
temporal analysis of the change in soil moisture between the
three seasonal key dates. The 125 groups of drying rates
were then used as data for the analysis of the richness–area
curves of Eq. (1).
The richness–area curves were calculated to four soil
formations that were delineated using the soil map of Israel
(Dan & Raz, 1970). The application of the research ap-
proach to a climatic gradient with high pedodiversity
(Yaalon, 1997) may contribute to the understanding of
spatial patterns of soil variation observed in such regions.
In that sense, our work illustrates a way in bridging the gap
between remote sensing, GIS and soil science.
5. Materials and methods
5.1. Study area
The study area (Fig. 2) is located in the intermediate part
of a long and topographically gentle north–south climatic
gradient. It covers approximately 400 km 2 in a semi-humid
(Csa class according to the Koppen climatic regions) to
semi-arid (BSh according to Koppen) transition zone. Most
of the study area comprises chalky layers from the Eocene,
with white globigerinal chalks, covered by a thick Calcar-
eous (nari) crust. On the western side of the area, the
dominant formation is Quaternary (Recent) alluvium, with
patches of Kurkar ridges from the upper Pleistocene. On the
eastern border, much older layers are exposed: in the
northern parts, limestone and dolomite from the upper and
lower Cenomanian; in the southern areas, chalky rocks from
the Turonian and undivided layers from the Senonian–
Paleocene, which dominate the lower margins of the Judean
anticline. According to Dan (1988), four major soil forma-
tions cover the study area (Fig 3; Table 1):
(i) Brown lithosol and colluvial alluvial loess (M1), in the
southeastern part of the area. The terrain is hilly with
narrow valleys where colluvial alluvial loess has
accumulated. The slopes are used primarily as
rangeland, dominated by herbaceous formations and
dwarf shrubs such as Sarcopoterium spinosum, while
the valleys are intensively cultivated, mainly with
wheat and barley.
(ii) Brown and light rendzina on steep slopes (B3), mainly
in the northeastern part of the area. The soil is usually
shallow on the steep slopes and deeper in the valleys
and on foothills. Most of these areas might be used for
rangeland or forestry, while the deep valleys are
suitable for orchards. The vegetation includes shrubs
and patches of herbaceous vegetation and dwarf
shrubs.
Fig. 3. The soil formation map (from Dan & Raz, 1970)—a subset of the
study area.
T. Svoray, M. Shoshany / Remote Sensing
(iii) Loessial light brown soils and brown grumusolic soils
(N4), in the southwestern part of the area. The soil
formation N4 is located on gentle topography. Crops,
including wheat and barley, cover almost the entire
area.
(iv) Dark brown grumusolic soils and residual dark brown
soils (K3) in the northwestern part of the area, typical of
gentle slopes. In lower areas, cumulative dark brown
soils are found with mainly dark brown clay syan.
Irrigated fields, including orchards and field crops,
cover the entire area.
Table 1
Attributes of the four dominant soil formations in the study area
Soil formation Initials Location Climate Vegetation
Brown rendzina B3 Northeast Semi-humid Shrublands
Dark brown
gromusols
K3 Northwest Semi-humid
to semi-arid
Mainly agricultural
crops
Brown lithosol
and loess
M1 Southeast Semi arid Dwarf shrubs
and herbs
Brown loess N4 Southwest Semi-arid to arid Agricultural crops
The hydraulic conductivity of fine earth data was provided by Ravikovitch (1992)
(2002).
Shoshany et al. (1995, 2000) assessed the variation in soil
properties along a climatic gradient and claimed that soil
moisture content can be attributed to differences in soil
physical and chemical properties which vary according to
the bedrock and evolve through cycles of soil wetting and
drying, typifying the pedogenic processes as leaching in the
winter and redeposition in the summer. In the long run, the
soil formations were determined by the bi-directional elec-
trolyte movement together with the climatic variation of
temperature and rainfall amounts along the sharp climatic
gradient of this region. This is achieved by controlling the
rate of decalcification, cation exchange capacity, colloid
movement in the soil profile, clay mineralogy and gypsum
accumulation. The outcome is a spatial heterogeneous pat-
tern of the soil intrinsic properties and primarily to hydraulic
properties leading to large spatial variation in soil attributes
such as field capacity, soil texture and hydraulic conductiv-
ity. It is hypothesized here that each of the above-mentioned
soil formation have a characteristic spatial variation which
might be depicted using the richness–area curves. The
richness–area curves were applied to the study area using
an increasing area of sampling window. The area and the
shape of the sampling window were determined based on the
size and the shape of each of the soil formations.
5.2. Field survey
During field surveys in February, April and May 1997
and in April and June 1999, soil moisture content was
measured in 16 plots (8 at the semi-humid Avisur site and
8 at the semi-arid Lehavim site—see Fig. 4) to determine the
validity of the remote sensing soil moisture model. Another
57 plots distributed randomly in the study area were
sampled only during April and June 1999. Soil moisture
content in all of these plots was measured from bare terrain.
Fig. 4 shows the location of all plots in the study area and
the position of the soil samples within each plot. The
location of each plot was measured by differential Global
Positioning Systems (GPS) to link the field measurements
with the ERS-2 SAR pixels. The procedure was imple-
mented as follows: For each plot, samples of soil were taken
from ten randomly distributed sites. Each soil sample
of Environment 92 (2004) 233–246 237
Parent material Rock fraction (Rm) Hydraulic conductivity (Kfe)
Mean Standard
deviation
High Low
Chalk and nari 0.42 0.30 0.21 0.13
Sandy sediments 0.51 0.36 0.48 0.3
Loess nari and
limestone
0.69 0.18 0.34 0.19
Loess and clay 0.68 0.30 0.81 0.4
and rock fragments fraction values were provided by Shoshany and Svoray
Fig. 4. Distribution of sampling plots of soil moisture within the climatic gradient of the study area. The plots are located using DGPS measurements.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246238
weighted about 200 g and was taken from the 0–5 cm depth
and sealed in thermal-resistant plastic bags. The bags were
weighed before and after 24 h drying at 105 jC in a standard
oven. Gravimetric soil moisture was calculated using the
well-known ‘‘double weight’’ method. Previous studies
(e.g., Prevot et al., 1993) have shown empirically that the
radar backscatter is more sensitive to volume scattering than
to changes in mass. To correspond to the linear soil moisture
model, the gravimetric soil moisture measurements were
converted into volumetric measures, based on bulk density
measurements for the specific soils of the study area
(Svoray, 2000).
Table 2 shows that the soil moisture values at both the
Avisur and Lehavim sites for February 1997 are the high-
est. The reason for this is illustrated in the accumulated
rainfall graph of 1997 (Fig. 5). Just before the field
campaign and satellite overpass of February 1997, a sig-
nificant rainfall event was recorded at both sites. In
contrast, the last rainfall with relevance to the field cam-
paign and satellite overpass of April 1997 was recorded on
Table 2
Field data of soil moisture concentration (%) in three habitats at the Avisur and L
Site Avisur
Habitat Height Slope Wadi
Measure Average Standard
deviation
Average Standard
deviation
Average Stand
devia
February 1997 27 – 26 – 28 –
April 1997 10 – 8 – 21 –
May 1997 12 1.64 12 2.03 13 0.34
23 March 1997—17 days before the campaign. The de-
crease over time of soil moisture concentration in soil under
similar climatic conditions in Israel’s east–west climatic
gradient was also determined by Svoray (1994). Water loss
of about 3% per day was measured in wet soil (25% of
gravimetric soil moisture) and about 0.5% in dry soil of 5%
soil moisture concentration. Thus, during the 17 days
before the April sampling, soil moisture had decreased
due to evaporation and infiltration, and was particularly
low on the slopes and heights. However, the soil moisture
concentration values in the valleys remained relatively
high, due to drainage accumulation and vegetation cover,
which reduces the sun’s intensity. During the May cam-
paign, soil moisture content values were relatively low and
more constant within the sites (it seems that the wadi had
dried and a moisture increase on slopes and heights might
have been due to an unrecorded small rainfall event). Table
2 gives the differences between the sites: Avisur soil
maintains a moderate moisture level while Lehavim soil
remains much drier.
ehavim sites
Lehavim
Height Slope Wadi
ard
tion
Average Standard
deviation
Average Standard
deviation
Average Standard
deviation
17 – 13 – 19 –
5 – 4 – 7 –
2 0.1 2 0.2 2 0.4
Fig. 5. The cumulative rainfall in the study area, represented by the meteorological stations of Beit-Gimal and Beer Sheva (the closest meteorological stations
with daily data available on the Avisur and Lehavim sites, respectively). Based on data from the Israel Meteorological Service.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246 239
5.3. Satellite data
Five ERS-2 SAR images (Cvv band at mean incidence
angle of 230) were acquired from the study area in two sets of
measurements: (i) February, April and May 1997, represent-
ing the environment during the winter, spring and summer
seasons; and (ii) April and June 1999, representing the spring
and summer seasons of that year. The backscatter (r0) was
derived from the Digital Number (DN) of the ERS-2 SAR
PRI image, using the method by Laur et al. (1997). Since part
of the study area is hilly, backscatter was adjusted for the
variation in the local angle of incidence, based on a digital
elevation model (Svoray & Shoshany, 2003). The geometric
correction of the images used 250 ground control points and a
first-order algorithm with a root mean square error of less
than one pixel.
Implementations of the NBMI method on bare terrain
within the study area were achieved using the synergy of
ERS-2 SAR and Landsat TM images. The assessments were
applied on three dates: February, April and May 1997. The
ERS-2 SAR backscatter images were used to calculate
NBMI layers and the Landsat TM data to identify and mask
the vegetated surfaces, with the Normalised Difference
Vegetation Index (NDVI—the normalised ratio between
the red and infrared bands). The NDVI has shown a strong
correlation with vegetation cover in other environments
around the world and in the study region (Svoray et al.,
2003). The procedure was implemented by the ERDAS/
IMAGINES Model Maker in four stages (Fig. 6). At first,
NBMI layers were calculated from the ERS-2 SAR back-
scatter using Eq. (5). The value of dBt1 in this case is the
backscatter from the calibrated image pixel of each date; dBt2
is the average value of the intercept of soil moisture models
from various environments around the world (Shoshany et
al., 2000) and the soil moisture models calculated for the
present study area. At the second stage, the transformation of
the empirically derived relationship between the VSM con-
centration and NBMI was used as a model for calculating the
VSM layers from NBMI layers (Eq. (7)):
VSM ¼ NBMI þ 0:0204
0:0105: ð7Þ
At the third stage, NDVI was calculated for the study
area using Landsat TM images from February, April and
May 1997 (Eq. (8)):
NDVI ¼ band4� band3
band4þ band3; ð8Þ
where bands 4 and 3 denote the data recorded in the infrared
and red bands of the Landsat TM, respectively. A threshold
value of DN is selected to distinguish vegetated and non-
vegetated surfaces. The threshold value is set at 0.2, based
on spectral signatures of soil and vegetation in the study
area that had been measured previously (Svoray, 2000;
Svoray et al., 2003). The latter shows that under similar
ecological conditions the NDVI value of 0.2 is related to a
vegetation cover of less than 5%. Additional support for the
selected value is a random sampling of 90 plots with bare
soil and low herbaceous vegetation cover in the study area
(Fig. 7).
Use of this NDVI threshold value to distinguish between
vegetated and non-vegetated surfaces is described at the
fourth stage. The NDVI layer for each date represents the
status of vegetation cover at different phenological phases—
which is important given the wide cover of annual vegeta-
tion in the study area.
At this last stage, layers of VSM concentration and
NDVI were merged using a simple conditional model.
Fig. 7. The relationship between total vegetation cover and NDVI values in
the study area.
Fig. 6. The use of NDVI derived from Landsat TM images and the relationship between VSM and NBMI—for the assessment of moisture concentration in bare
soil regions.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246240
The model traced pixels in the VSM layer with cor
responding NDVI values greater than 0.2 to be masked
at zero, and pixels with NDVI values smaller than 0.2 to
give their VSM value in the final layer. This stage
produced a VSM concentration layer that covered only
the bare soil surface of the study area for each of the
above-mentioned dates.
6. Results and discussion
6.1. Results
Fig. 8 shows the soil moisture images for February, April
and May 1997. In general, the February image presents the
largest VSM values (with mean values of 29% and 17% for
Fig. 8. VSM layers of bare soil plots along the climatic gradient of the study area at the three dates in the growing season of 1997. Reddish colors represent
lower VSM values and bluish colors higher VSM values.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246 241
the northern and southern parts of the study area, respec-
tively), due to its relative proximity to the last rainfall event
(Fig. 5). The April image shows lower VSM values (with
mean values of 18% and 12% for the northern and southern
parts, respectively), while the May VSM values are the
smallest (with mean values of 13% and 9% for the northern
and southern parts, respectively). However, the spatial
variability in the VSM values in the three images is high
and a visual analysis shows that there is no continuous
decrease in VSM from the semi-humid area in the north to
the semi-arid area in the south.
The large degree of spatial variation is also observed in
the analysis of the drying rate patterns. The patchy pattern is
generalized by grouping the 125 optional combinations of
soil-drying rate into nine classes of similar temporal change
(see legend in Fig. 9). Thus, for example, the first category
(cyan) includes all of the groups that represent a decrease in
VSM from February to April and then an increase from
April to May; the second category (green) includes all
groups in which VSM increases from February to April
and also increases from April to May. As discussed earlier,
the output categories of this classification infer types of
potential water holding capacity in the cells. For example,
areas of unchanged low VSM levels (depicted in black)
suggest that there is no rainfall infiltration of the soil in these
cells and that they are runoff contributing areas. Identifica-
tion of such group is most important for modeling the
hydrology and erosion in these regions.
A frequency analysis of the nine categories in the entire
study area shows that in 73% of the area, there is a decrease
of VSM from February to April and then either another
decrease of VSM in May or the VSM level remains the
same between April and May. The categories of higher
VSM values in May (shown in Fig. 9 in shades of blue) are
mainly in the agricultural fields of the northern part of the
study area. These fields are cultivated with summer crops,
such as cotton, water melon and sunflower (Cohen &
Shoshany, 2002). The fields are irrigated in May, but the
vegetation cover is low at this date; thereby for these areas
represent an increase in VSM in May, in comparison with
April. Apart from this category which represents a distinc-
tive type of landuse, the other parts of the study area are
characterized by soil types dominated by natural hydrolog-
ical processes of infiltration and runoff.
The resulting heterogeneous and patchy pattern of soil-
drying rate groups can be attributed to the effect of intrinsic
Fig. 9. The four soil formations with 125 soil drying rate transition groups joined into nine categories.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246242
factors on the soil. The coefficients of the richness–area
curves (Eq. (1)) applied to the four main soil formations of
the study area are given in Table 3. There are very strong
correlations (0.88 <R2 < 0.94) between the number of drying
groups and the areas they occupy (Fig. 10). This result
supports previous assertions that the power function of
richness–area curves represents intrinsic variation in soil
(Phillips, 2001) and further strengthens both the information
and functional aspects this multi-scale behavior at the
regional scale, where the validity of the richness–area
relationship has been questioned. The exponents (b coef-
ficients) of the four curves range from 0.25 to 0.37 with a
mean of 0.3, corresponding to values reported previously
(Phillips, 2001; Rosenzweig, 1995).
A bi/bt ratio was calculated between the mean exponent
of the four groups (bi) and the exponent of the richness–
area curve of the total study area (bt). The exponent of the
total area was calculated using bare soil pixels sampled from
the four soil formations that cover the study area. The
selected pixels were examined in the field to assure that
Fig. 10. Richness–area curves calculated to four soil formations of the
study area.
Table 3
Results of the coefficients achieved for the richness–area curves of the four
soil formations in the study area
Soil formation Polynomial model R2
c b
B3 5.99 0.37 0.91
M1 7.83 0.33 0.93
K3 12.32 0.25 0.88
N4 8.61 0.29 0.94
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246 243
areas covered by water sources and artificial structures are
excluded from the analysis.
The result (2.68) has an important pedologic implication
that will be discussed later. The largest exponent is for the
B3 soil formation and the second largest is for M1. The c
coefficients (with a range of 5.99–12.32 and a mean of
8.69) are much larger than observed in previous studies.
Another observation that stems from the application of the
richness–area curves in the current research is that there is a
strong correlation between the coefficients c and d of the
four soil formations examined for the power (R2 = 0.92)
model (Fig. 11). To our knowledge, this relationship has not
been reported previously.
6.2. Discussion
Differences in soil drying rates occur due to the com-
bined effect of intrinsic and extrinsic factors. A useful
method to determine which of the factors have more
influence is the analysis of the bi/bt ratio. The analysis is
based on the assumption that bt represents the overall
variance of four different soil formations and therefore
incorporates variation caused by extrinsic factors (since
the four soil formations vary in their extrinsic characters).
In contrast, bi represents the mean value of the exponent
coefficients of the four formations and is not likely to
represent variation associated with the extrinsic factors.
Therefore, if the bi/bt ratio is greater than 1, then the
variation within the unit and the effect of intrinsic factors
are greater than the variation between units that are caused
by extrinsic factors. As shown in Section 6.1, the bi/bt ratio
calculated here is much larger than one which implies that
the intrinsic factors have more influence on the spatial
pattern of soil-drying rate groups than extrinsic factors.
Furthermore, the large coefficients of determination for
the richness–area curves suggest that the heterogeneity of
the factors is involved with drying processes increase with
scale. This had already been observed by Seyfried (1998),
who, however, attributed the change in scale to extrinsic
factors: ‘‘. . .the increase of spatial variability with scale was
controlled by deterministic ‘sources’ such as soil series and
elevation induced climatic effects.’’ Similarly, a strong
relationship was found between the variation in soil mois-
ture and primary and secondary topographic attributes
(Sulebak et al., 2000). The two studies show increasing
variation in soil moisture with scale as a result of the effect
of environmental factors. However, it is important to note
that the two previous studies and the one reported here differ
in physiography and climate. Seyfried’s study was carried
out in a mountainous area between 1097 and 2237 m, with
snow precipitation. Under such severe topographic condi-
tions, extrinsic factors have a strong impact. Sulebak et al.
surveyed a wet area with large soil moisture values. In this
area, the water movement within (and upon) the soil plays a
much more important role than in our Mediterranean to
semi-arid climate. In the latter areas, water loss is mainly
Fig. 11. The relationship between c and b coefficients, determined to the
four soil formations of the study area.
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246244
due to evaporation and therefore the soil water holding
capacity (strongly dependent on the soil hydraulic proper-
ties) has more influence on changes in soil moisture storage.
The fact that the curves correspond well with the data not
only verifies the validity of the richness–area concept
beyond the plot scale but it also conform with previous
analyses of soil drying rates at much coarse scale. For
example studies such as Barros et al. (2000) who have
found that multi-temporal analysis of SAR images at the
Fig. 12. The relationship between c coefficients and the hydraulic conductivity o
conductivity was calculated based on the formula Ksoil/Kfe = 1�Rm where Rm is
Cousin et al., 2003).
scale of kilometers can be used to analyze spatial patterns of
intrinsic soil factors such as soil texture. In our study, the
link between soil moisture change, soil texture, hydraulic
conductivity and characteristics of the ‘‘richness–area’’
curves was examined and showed high correlation (Fig.
12) limited at this stage by the number of soil groups
presented here. Further insight would require more detailed
analysis with more detailed data of soil texture and hydrau-
lic conductivity.
As discussed earlier, the richness–area relationship
depends on the local or regional environmental conditions
and on the process studied. Nevertheless, it seems that the
soil variation due to long-term processes increases the
heterogeneity of soil moisture patterns with scale. Based
on these findings, we can conclude that intrinsic factors
affect the intra-unit variability. The changes in relationships
between the units (using exponents and, to some extent,
coefficients of determination) may be caused by differences
in extrinsic factors, such as parent material and topography,
between the units. For example, the exponents of the natural
areas (M1 and B3 soil formations) are larger than those of
agricultural areas (K3 and N4 soil formations). A surprising
result is the strong relation between the c and b coefficients
(Fig. 11). This implies that the two coefficients could be
integrated, as each exponent could have a predicted multi-
f the soils (Ksoil) and the fine earth hydraulic conductivity (Kfe). Hydraulic
the mass fraction of the rock fragments (based on Brakesniek et al., 1986;
T. Svoray, M. Shoshany / Remote Sensing of Environment 92 (2004) 233–246 245
plier coefficient. The present study has focused on four
soil types at it provides convincing results, as illustrated in
Fig. 10. The final result, however, provides an indication
for further empirical study.
7. Summary and conclusions
Monitoring spatial patterns of soil attributes in general,
and the estimation of areal soil moisture in particular, is an
important task of environmental remote sensing (Schmugge
et al., 2002). The spatio-temporal dynamics of soil moisture
is especially needed in semi-arid regions, where water stress
determines the productivity of woody and herbaceous veg-
etation (Kumar et al., 2002; Tansey & Millington, 2001).
In the current research, we analysed spatio-temporal
patterns of soil moisture and examined the quantitative
effects of intrinsic soil factors on recurring soil-drying
processes at the scale of 12.5 m per pixel. The progress
achieved here with remote sensing and environmental
monitoring was facilitated by the multi-scale approach
(Walsh et al., 1998). Our results show strong relationship
between the size of the plot measured and the richness of the
drying rate groups within four Mediterranean soil forma-
tions. The richness–area curves enable an extension of the
remote sensing methodology beyond per-pixel estimates of
volumetric soil moisture concentration and thus offer an
alternative perspective to describe the multiscaling behavior
of soils. Furthermore, it may help to disaggregate general-
ized soil map polygons into more detailed landscape com-
ponents (Bui & Moran, 2001).
The relationship between the richness–area coefficients
and soil hydraulic conductivity at the 12.5 me scale as
presented in this study provides an important link between
the studies conducted at the broad scale and the multi-scale
behaviour of soils. Kim and Barros (2002a) show that in a
scale of 10 km, topography dominate soil moisture only
during and immediately after rain storm, while during the
interstorm periods in non-vegetated surfaces, soil moisture
is closely associated with the spatial variability of soil
hydraulic properties. Our study further supports this hy-
pothesis as it shows, in a much finer scale, that soil water
holding capacity has an important role in the determination
of scaling behavior of soil moisture fields.
Linking the local scale with the regional strengthens the
phenomenological basis of the remote sensing interpreta-
tion. It allows differentiation between areas that conform to
the intrinsic scale change and other areas that are represent-
ing local anomalies due to disturbance or error in the remote
sensing interpretation. Thus, the effect of the soil’s intrinsic
factors can be considered in soil (moisture) modeling,
thereby contributing to a gap in our knowledge.
The transition maps of soil-drying rates provided here
can serve as an important database for future work by
providing information on contributing areas, sources and
sinks in hydrological models.
Acknowledgements
This study was carried out under a JNF (Jewish National
Fund) research grant no. 190/9/328/8. We wish to
acknowledge Haim Katz for his help in the field campaign
and we thank the anonymous reviewers and Prof. Jonathan
D. Phillips who contributed significantly to the depth of the
soil information analysis.
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