MAPPING SOIL MOISTURE IN THE CENTRAL EBRO RIVER
VALLEY (NE SPAIN) WITH LANDSAT AND NOAA SATELLITE
IMAGERY: A COMPARISON WITH METEOROLOGICAL DATA.
Sergio M. Vicente Serrano*, Xavier Pons Fernández** and José Mª Cuadrat Prats*
* Department of Geography. University of Zaragoza. C/ Pedro Cerbuna 12. Zaragoza.
50009. Spain. svicen@posta.unizar.es, jmcuadra@posta.unizar.es
** Department of Geography and Centre for Ecological Research and Forestry
Applications (CREAF), Autonomous University of Barcelona, Bellaterra 08193, Spain.
Xavier.Pons@uab.es
ABSTRACT:
This paper analyses the spatial distribution of soil moisture using remote
sensing: NOAA-AVHRR and Landsat-ETM+ images. The study was carried out in the
central Ebro river valley (NE Spain), and examines the spatial relationships between the
distribution of soil moisture and several meteorological and geographical variables
following a long, intense dry period (winter 2000). Soil moisture estimates were
obtained using thermal, visible and near-infrared data and applying the “triangle
method”, which describes relationships between surface temperature (TS) and fractional
vegetation cover (FR). Significant differences were found between the soil moisture
estimates obtained using AVHRR and ETM+ sensors. However, in both cases the
spatial distribution of soil moisture was largely accounted for by meteorological
variables.
KEY-WORDS: Soil moisture, Spatial scales, Climate-soil moisture relationships,
Surface temperature, NDVI, Ebro river valley, Spain.
1- INTRODUCTION:
Natural and agricultural dryland ecosystems depend heavily on soil moisture. In
semi-arid regions, where evapotranspiration rates are high, plant growth is limited by
1
low levels of soil moisture (Downing, 1996; Müller-Edzards et al., 1997; Alexandrov
and Hoogenboom, 2000; Sheperd et al., 2002). In these areas, knowledge of the spatial
distribution of soil moisture is of considerable importance for hydrological, agricultural,
economic and social planning.
In areas marked by high climatic variability, soil moisture and vegetation growth
are determined by the amount and spatial distribution of precipitation. In periods of
abundant rainfall, soil moisture does not vary greatly thereby guaranteeing the normal
growth of natural vegetation and crops. Under these conditions, the topographical,
edaphic and lithological characteristics have little influence (Western et al., 1999).
However, in dry periods, soil moisture becomes a highly limited resource, and this
moisture is heterogeneously distributed according to specific meteorological events, the
topography, soil type, lithology, land uses and green cover (Western et al., 1999).
Therefore, knowledge of the spatial interrelations between soil moisture, climatic
factors and the geographical characteristics of the environment is of great applied
interest, since in dry periods water availability is a determining factor, and crop success
is more likely in those areas that are able to store water for longer periods.
Remote sensing techniques have been extensively used for the analysis of soil
moisture and plant water availability. Estimates of soil moisture using satellite data have
been conducted using various methods (Tucker, 1980; Crist and Cicone, 1984; Levit et
al., 1990; Seguin et al., 1991; Nemani et al., 1993; Dupigny-Giroux and Lewis, 1999;
etc) and can lead to significant time and cost savings in environmental and agricultural
management. Remote sensing allows continuous estimates to be made, which represents
a considerable improvement on the more limited measurements of ground data, and so
ensures a virtually continuous temporal register thanks to the high temporal resolution
of satellite images.
2
Soil moisture estimates obtained by remote sensing typically provide relative
values in that they allow different areas on the image to be compared; they do not
provide physical measurements. Basically, five types of method are available for
estimating soil water content by means of remote sensing.
The first group uses microwave images (Wang et al., 1989; Schumugge et al.,
1988 and 2002; François, 2002) and the method is based upon the high absorption of the
electromagnetic long wave radiation caused by water. The second is based on thermal
inertia models, which make use of thermal, visible and infrared information when
calculating soil moisture (Cracknell and Xue, 1996; Sobrino and Raissounni, 2000). The
radiometric behavior of the different surfaces in the middle infrared has been widely
used as a third method in estimating soil and vegetation moisture levels (Crist and
Cicone, 1984; Musick and Pelletier, 1988; Levit et al., 1990), since the middle infrared
reflectance of the soil and the green cover is highly conditioned by water content
(Tucker, 1980). Various studies have estimated soil moisture using band combinations
and a series of ground measurements to provide ground truth. Such empirical regression
models constitute the fourth approach (Carlson, 1986; Levit et al., 1990; Cocero et al.,
2000). Finally, the relationship between surface temperature (TS) and fractional
vegetation cover (FR) has been widely used for soil moisture estimates (Nemani et al.,
1993; Gillies and Carlson, 1995; Gillies et al., 1997; Dupigny-Giroux and Lewis, 1999).
This last relationship, which is described in further detail below, was used to estimate
soil moisture in this study.
However, although numerous studies have analyzed the spatial distribution of
soil moisture using remote sensing, its relationship with climatic conditions and other
geographical characteristics has not been always been examined in depth. Similarly,
while many studies have compared the parameters recorded using a range of sensors at
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several spatial scales (mostly NDVI), the relationship with soil moisture estimates made
at various spatial platforms has been the subject of little analysis (Carlson et al., 1995;
Moran et al., 2002). This issue is clearly important because together with the spectral,
radiometrical and orbital satellite characteristics, the different spatial resolutions of each
sensor must be taken into consideration since they condition the spatial analysis of soil
moisture. Frequently, in remote sensing the scale is the factor that is most restrictive.
Indeed, processes seen to be operating at certain scales cannot be analyzed at others
(Foody and Curran, 1994), while certain studies performed at a given scale cannot be
conducted at another (Woodcock and Strahler, 1987). In the case of soil moisture
analysis, soil and vegetation moisture usually present a marked spatial variability,
though this might vary from scale to scale. Foody (1991) reported significant changes in
crop moisture in relation with microtopography; this indicates that is more difficult to
find significant soil moisture patterns at the small scale (Choudhury, 1991).
This paper analyzes the spatial distribution of soil moisture estimated indirectly
using thermal, visible and infrared data from satellite images, employing a method
similar to that adopted elsewhere (Nemani et al., 1993; Carlson et al., 1994; Lambin
and Ehrlich, 1996; Sandholt et al., 2002) but revealing some marked differences
between the sensors and in the role ascribed to certain meteorological and geographical
variables. The area selected for the analysis was the central Ebro river valley (NE
Spain), a semi-arid region of considerable climatic, lithological, edaphic and landscape
diversity.
This paper has four objectives:
1- To determine the spatial distribution of soil moisture using satellite images.
The analysis was conducted on 17 March 2000 following a period of severe drought in
which scarce rainfall presented an uneven and anomalous spatial distribution.
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2- To analyze the influence of “static” environmental variables (lithology, soil
type, land use and topography) on the spatial distribution of soil moisture.
3- To determine the influence of meteorological factors (precipitation and
temperature) prior to image acquisition on the spatial distribution of soil moisture, and
to establish whether the severe drought significantly affected the spatial distribution of
the soil moisture predicted by remote sensing.
4- To identify whether there are any differences in the climate-soil moisture
relations obtained with the two satellites (NOAA and Landsat).
2- STUDY AREA
The location of the study area is shown in Figure 1. This section of the Ebro
river basin is an excellent example of a relatively flat topographical area in which the
climatic elements present a considerable spatial complexity. The landscape is dominated
by horizontal structural platforms that overlie tertiary deposits, with altitudes below 800
m. Terraces and quaternary glacis mark the boundary between alluvial basins and plain
bottoms (Pellicer and Echeverría, 1990).
[Insert Figure 1 about here]
The study area is located inside the general circulation of the Temperate Zone,
close to the subtropical domain. The relief features isolate the valley from any maritime
influences. The principal characteristic of the area is its aridity (Ascaso and Casals,
1981; Creus, 2001). Pluviometric variability is high. In some years the precipitation
greatly exceeds (e.g. 646 mm in Zaragoza in 1959) the average (Zaragoza: 322 mm),
whereas in other years the study area receives almost half that quantity (e.g. 182 mm in
5
1995). Furthermore, the lack of rain and the uncertainty of rainfall events combine with
a high potential evaporation (1100 mm in the center of the valley) (Martínez-Cob et al.,
1997).
The vegetation of the area is thermally influenced steppe (Suárez et al., 1992),
determined largely by the lithology, soil-type and, in particular, the aridity. Forests are
scarce in the bottom of the valley due to human activity dating back centuries; only
some small forests of Juniperus thurifera, Quercus ilex and Pinus halepensis remain on
the slopes of the tabular relief. The most common land use is dryland agriculture (wheat
and barley). In this agricultural system the climate plays an especially important role as
harvests are strongly conditioned by the rainfall, so financial losses can be severe in dry
years.
3- METHODS
3.1- PRE-PROCESSING
Two satellite images (Landsat7-ETM+ and NOAA14-AVHRR), taken on 17
March 2000, were used. There was a five-hour lag between the two recordings (Landsat
passed at 10:35 GMT and NOAA14 at 15:25 GMT). This date was selected because the
Landsat image was the last to be taken before the spring rains. The Landsat image was
orthorectified (Palà and Pons, 1995) using a Digital Elevation Model (DEM), while the
NOAA-AVHRR image was geometrically corrected using a second-order polynomial
adjustment (Richards, 1993). The images were radiometrically corrected in order to
account for the atmospheric and solar illumination factors (Pons and Solé, 1994;
Chávez, 1988). NDVI was obtained for both images (Tucker, 1979). The NDVI
obtained from the Landsat image was resampled to 1 km, using a mean criterion, in
order to match the AVHRR resolution.
6
Thermal bands were transformed to brightness temperatures (Markham and
Barker, 1986). NOAA-AVHRR TS was obtained by means of a split-window algorithm
(Sobrino and Raussoni, 2000). The Landsat thermal band was not corrected
atmospherically given the fact that it is difficult to obtain reliable results in standard
cases with only one thermal band without atmospheric data (Vidal et al., 1994; Coll et
al., 1994). This does not mean that the use of the Landsat image is limited because the
same atmospheric perturbation can be assumed for the whole area in the image. Finally,
the effect of surface emissivity, in both images, was also corrected (Sobrino et al.,
2001). The Landsat surface temperature was resampled to 1000m resolution using a
mean criterion. The NDVI and the TS are shown in Figure 2.
[Insert Figure 2 about here]
Figure 3 shows the relationship between the TS and the NDVI obtained with
NOAA-AVHRR and Landsat-ETM+ images at a cell size of 1000m. For NDVI images,
the Pearson correlation coefficient was 0.74. The correlation for TS images was 0.71.
[Insert Figure 3 about here]
There are two principal restrictions to the effective comparison of both images.
The TS map obtained by means of the AVHRR image is, in general, three degrees
higher than that obtained using the ETM+ image. The absence of atmospheric
correction in the latter case, and the different recording times of both satellites (LandsatETM+ at 10:35 GTM and NOAA-AVHRR at 15:25 GTM) might account for these
differences. Gutman (1999) stresses the fact that the time lag between the passing of one
satellite and the other causes major divergences in the TS measured. This has been
observed in temporal series of TS images from NOAA-AVHRR satellites, where the
observations were made progressively later than the launch because of the drift in the
equator-crossing time (Price, 1991).
7
There are also significant differences between NDVI images, with higher values
being obtained with the AVHRR image than with the ETM+ image. These differences
may be produced by the different spectral configurations of both sensors: AVHRR red
and near-infrared bands: 0.58-0.68m and 0.72-1.10m, respectively; ETM+ red and
infrared bands: 0.63-0.69m and 0.76-0.90mrespectively. Nevertheless, a number of
studies demonstrate the comparability of AVHRR and ETM+ NDVI images and point
out that spectral configurations have little influence on results (Teillet et al., 1997).
More important may be the effects of shifts in the photosynthetic vegetal activity during
the day, caused by changes in incoming solar radiation, atmospheric moisture and air
temperature (Justice et al., 1991). Differences between NDVI images obtained at
different times of day have been analyzed in several studies (Holben et al., 1990;
Gutman, 1991; Che and Price, 1992; Schultz and Halpert, 1995), showing that satellite
orbit drift in the NOAA-AVHRR satellites results in considerable differences in the
NDVI results between the moment of launching and the final satellite life because the
time pass significantly changes (Teillet and Holben, 1994; Gutman and Ignatov, 1995).
This implies discontinuities and inhomogeneities in temporal NDVI series (Kogan and
Zhu, 2001) as well as problems in the calibration of images (Rao and Chen, 1996).
These problems illustrate that certain limitations are encountered when comparing the
information obtained by both satellites.
3.2- SOIL MOISTURE ESTIMATION
Soil moisture estimation using thermal data is based on the relationship between
the water content of different surfaces and its temperature. The latent and sensible heat
fluxes are conditioned by the surface water content (Eltahir, 1998). On unvegetated soils
and in full vegetation areas, evaporation and transpiration increase as the water content
rises. When soils are moist, the latent heat fluxes increase because of the greater
8
absorption of water. This process causes sensible heat to decrease. In dry soils the
process is the inverse of this. The radiative energy is not consumed in the evapotranspiration process, and the sensible heat increases, raising the TS. In theory, this is a
simple method of soil moisture estimation. However, the environment is particularly
heterogeneous (soil, lithology, vegetation cover, topography), and it cannot be assumed
that the coldest areas actually coincide with the areas of greatest soil moisture. Indeed,
there are a number of elements that interfere in this relationship, the most significant
being vegetation (Lambin and Ehrlich, 1996). The hydric vegetation conditions are
hardly recognized in the visible region of the electromagnetic spectrum, but changes can
be significant in the surface vegetation temperature (Seiler et al., 2000; Kogan, 2001).
TS and fractional vegetation cover (FR) can provide information about vegetation and
moisture conditions at the surface. Lambin and Ehrlich (1996) summarized the
relationship between both variables in a theoretical space that indicates the moist limits
and the soil moisture status in relation to different vegetation cover percentages (Figure
4).
[Insert Figure 4 about here]
Nemani et al. (1993) pointed out that, in bare vegetation areas, temperature
changes can be assimilated to differences in soil water content. In fully vegetated areas,
thermal changes may be associated with changes in the green cover evapo-transpiration
which is eventually conditioned by soil water content. However, reducing soil moisture
estimates to a relationship between vegetation cover and TS means simplifying the real
radiative models. Indeed, different vegetal species can have distinct transpiration rates
under similar hydric conditions. The rugosity of vegetation cover affects wind
incidence, and subsequently influences evapo-transpiration processes (Smith and
Choudhury, 1990), while the soil color affects surface albedo and heat fluxes. Yet, in
9
the radiative processes, the greatest interference comes from the differences in the
vegetation cover. Smith and Choudhury (1991) demonstrated that vegetation rugosity
has little influence on the relationship between TS and NDVI while Goward et al.
(1985) showed that soil-albedo does not have influence on the exchange of transpiration
heat fluxes.
Gillies and Carlson (1995) and Sandholt et al. (2002) use the NDVI-TS
relationship to map soil moisture. The assumption is that thermal differences in areas
with the same green cover may be the result of changes in their soil moisture. The
principle underlying this method, however, may lead to certain errors (Sandholt et al.,
2002); indeed, the estimate assumes that soil moisture is the main source of variation in
TS, but other causes may condition this variation (view angle effects, errors in TS
estimation, etc.). Nevertheless, Sandholt et al. (2002) obtained a high degree of
similarity in the soil moisture estimated using the NDVI-TS relationship and the soil
moisture predicted by climatic data using a hydrological model.
The relationship that allows us to determine soil moisture using thermal
information needs to consider the FR. Gillies et al. (1997) obtained the FR using a
normalization of the NDVI image. In areas in which the vegetation-cover types are
highly varied (from bare soils to full-vegetated areas), the maximum NDVI value is
associated with 100% vegetation cover. In our study area, we can assume this to be the
case also due to the great diversity of vegetation covers, with some areas in riverside
woods and irrigated lands presenting 100% vegetation cover. The normalization of
NDVI was achieved using the Gillies et al. (1997) approximation that previously
eliminates negative NDVI values.
N
( NDVI  NDVI 0 )
( NDVI S  NDVI 0 )
Where:
10
N is the normalized NDVI;
NDVI is the value of NDVI in a pixel;
NDVIS is the maximum NDVI in the image;
and NDVI0 is the minimum NDVI in the image.
Gillies et al. (1997) and Choudhury et al. (1994) drew attention to the existence
of a clear potential relationship between the normalized values of NDVI and the FR of
each image pixel (on a scale between 0 and 1), where the FR is estimated using FR .
This estimation method was applied to ETM+ and AVHRR NDVI images.
The soil moisture estimation method uses a dispersion diagram between T S and
FR based on the “triangle” that defines the TS/ FR space (Lambin and Ehrlich, 1996).
The division of the FR image into areas with the same vegetation cover enables us to
assume that, in each vegetation interval, the coldest areas correspond with the moistest
soils, and that the hottest areas have the driest soils (Figure 5).
[Insert Figure 5 about here]
In each FR category, the pixel with the lowest TS is considered as the moisture
limit and is given a soil moisture value of 1. The pixel with the highest thermal values
constitutes the dry limit whose moist value is 0. The remaining pixels in each FR are
scaled from 0 to 1 in relation to their TS. Dupigny-Giroux and Lewis (1999) carry out
similar soil moisture estimation, establishing the same 0 and 1 thresholds for dry and
moist soils, respectively. The dispersion diagrams between FR and TS are shown in
Figure 6. In both cases, the dry and moist limits are recognized by the distinct FR
ranges.
[Insert Figure 6 about here]
Various intervals were established in the FR images in accordance with image
histograms. Table 1 indicates the intervals selected from the NOAA-AVHRR image as
well as the percentage of image area in each interval.
11
[Insert Table 1 about here]
Using these intervals, 19 binary masks were created and were, subsequently,
used to divide up the TS image into 19 partial TS images, each of which corresponds to a
certain range of vegetation cover. The most extreme pixels were not used as the dry and
moist limits as they were considered to be outliers (for example, pixels with partial
water cover or errors in the satellite register). We therefore analyzed the histograms of
each TS image in order to select the most suitable dry and moist limits.
Using these limits (dry = 0 and moist = 1), the remaining areas in each T S image
were scaled by making lineal adjustments (Table 2). Using MiraMon software (Pons,
1998), the lineal adjustments were applied to each partial image of TS. The result was
19 partial soil moisture images, one for each FR interval. Finally, these partial images
were combined to reconstruct the two original spaces of the soil moisture images (one
from NOAA-AVHRR and one from Landsat-ETM+). The Landsat soil moisture image
was resampled to 1km pixel size. A final 3 x 3 low-pass filter (mean) was applied to the
two 1km images in order that the results might be generalized.
[Insert Table 2 about here]
6- GEOGRAPHICAL AND METEOROLOGICAL INFORMATION
The spatial distribution of soil moisture depends on various factors (topography,
lithology, land use, climate, etc.). As Western et al. (1999) claim, topography is one of
the dominant factors in the hydrological processes. For example, slope and aspect
influence the contributions to precipitation, flow convergence and incident solar
radiation, factors that each condition evapo-transpiration processes.
We obtained a Digital Elevation Model (DEM) from contour lines at a scale of
1:50,000 using ArcInfo GIS. The DEM was generated at 30 m per pixel size, coinciding
12
with the ETM+ resolution. The DEM was resampled at a resolution of 1000m using a
mean criterion. From the digital elevation model (at 30 and 1000m cell resolution), the
slope was calculated. A solar radiation digital terrain model for the three months prior to
March 2000 was designed from the digital elevation models following the proposal of
Pons (1997), and implemented in the INSOLDIA module of the MiraMon GIS.
Digital lithological cartography supplied by the Confederación Hidrográfica del
Ebro (CHE, 2002), and reclassified into 12 classes, was used. Soil cartography (CSIC,
1970) was digitized and reclassified in nine classes. Land cover information was
obtained through a supervised maximum likelihood classification (Chuvieco, 2003)
from the spectral information of the Landsat-ETM+ image (7 classes). Urban and water
areas were not considered in the following analyses.
Finally, and in order to determine the soil moisture-climate relationships, a
continuous mapping of precipitation and temperature during the autumn and winter
seasons before 17 March 2000 was undertaken. These maps were used to determine the
impact of meteorology on the spatial distribution of soil moisture estimates obtained
from remote sensing. A spline interpolation method (INTERPNT module from
MiraMon) was used to generate these maps (Mitasova and Mitas, 1993; Borrough and
McDonnell, 1998).
Daily temperature and precipitation data were obtained from 61 weather stations
for autumn 1999 (September, October and November) and winter 2000 (December,
January, February, and the first seventeen days of March). The precipitation and
temperature data for the autumn and winter seasons were grouped as different variables;
while the autumn precipitation was normal for this season, the winter precipitation was
very scarce and its distribution was highly anomalous (Cuadrat, 1999). The quantity and
spatial distribution of this winter precipitation is shown in Figure 7. In fact, less than 6%
13
of the mean winter precipitation was recorded at ten weather stations. The central valley
and NE areas received 30% of the mean winter precipitation, but here again the
distribution was anomalous. Autumn temperatures were also anomalously distributed
(the northeastern region reported thermal values that were 15% higher than mean
temperatures). The mean winter precipitation is shown in Figure 8. Note that the spatial
patterns are quite unlike those for winter 2000 precipitation: higher precipitation values
in the northern areas and lower in the South.
[Insert Figure 7 about here]
[Insert Figure 8 about here]
7- STATISTICAL ANALYSIS
Any analysis of soil moisture-climate relationships needs to consider the
influence of other environmental factors, assuming that a simple cause-effect
relationship between an increase in precipitation and a rise in soil moisture cannot be
formulated. For this reason, the analysis here has considered several environmental
variables in order to determine the importance of geographical and meteorological
variables in the explanation of the spatial distribution of soil moisture.
It would seem that the following independent variables are potentially related to
soil moisture: elevation, slope, incoming solar radiation, mean air temperature (winter),
mean air temperature (fall), mean precipitation (winter), mean precipitation (fall),
lithology (12 classes), soil type (9 classes) and land use (6 classes). These variable were
obtained at 30m resolution and were then resampled to 1000m. Continuous variables
were resampled using a mean criterion while the categorical variables were resampled
using a modal criterion. In order to determine the percentage variance in the spatial
distribution of soil moisture accounted for by each independent variable, a multiple
14
stepwise regression was used. Multiple regression analysis enables us to determine the
role of the independent variables, whose values are known, in the explanation of a
dependent variable. This analysis facilitates the interpretation of the influence of the
independent variables on the dependent variable (Hair et al., 1998). Nevertheless,
categorical variables (lithology, land use or soil type) cannot be included in multiple
regression models. This problem was solved by converting these variables into binary
variables (presence, absence), and thus we obtained an independent variable for each
particular category (Hair et al., 1998).
Multiple regression analysis was carried out using random sampling. The sample
in the images at 1000m of pixel size contained 20% of the original pixels, while the
analysis at Landsat-ETM+ resolution (30 m) employed a sample of 1% of pixels.
8- RESULTS
8.1 Soil moisture estimates. Relationships between sensors
The soil moisture images obtained with AVHRR and ETM+ data at 1000 and
30m of resolution are shown in Figure 9. The two images present significant differences
although both were obtained on the same day. The main differences are that in the
AVHRR image the dry areas in the northeast are much more extensive and the soil
moisture values in the west and southwest are higher than their corresponding values in
the ETM+ image. The visual differences between the two soil moisture images are
greater than the differences between the original NDVI and TS images obtained with the
two sensors (Figure 2).
[Insert Figure 9 about here]
The relationships between the estimates obtained with both sensors are shown in
Figure 10. There is a positive and significant correlation (p < 0.001) between soil
15
moisture estimates obtained from AVHRR and ETM+ images at 1000m cell size. This
value is much lower than that calculated between the NDVI and TS images (see Figure
3). The correlation between the TS images is 0.71 (p < 0.001), while the correlation
between the NDVI images is 0.74 (p < 0.001). The correlation between the soil moisture
images, although significant, is lower than that between either the NDVI or TS images.
As the image processing (with the exception of the thermal atmospheric correction) was
largely similar, these large differences can probably be attributed to the fact that the
satellites images were obtained at different times.
[Insert Figure 10 about here]
7.2- Soil moisture-geographical and meteorological relationship patterns
The spatial distribution of soil moisture as revealed by the AVHRR image in
relation to a number of independent variables is shown in Table 3. The table records the
results of various regression models. Model 4, which includes the following variables:
winter precipitation, fall temperature, elevation and fall precipitation, was selected
because the inclusion of a fifth variable did not significantly improve the explanation of
the total variance (only 1 %). Note that the inclusion of just four variables provides a
particularly good explanation and that lithological, edaphic and land-use variables were
not included in the model.
[Insert Table 3 about here]
Partial correlation revealed that the winter precipitation had the greatest weight
in the explanation of soil moisture distribution (0.58, Table 4). A further variable, the
fall temperature, also showed a very high negative partial correlation. This would
account for the negative correlation in the case of the fall precipitation, because
although the rainfall in this period was normal in quantity and spatial distribution, the
16
abnormally high temperatures in the northern areas might have led to higher evapotranspiration rates thereby limiting water soil retention in the northeastern areas.
[Insert Table 4 about here]
Several studies (Nicholson et al., 1990; Davenport and Nicholson, 1993; Liu and
Kogan, 1996; Wang et al., 2001) conclude that the strongest correlation between arid
vegetative ecosystems and precipitation occurs two or three months after the rainfall
event. In the meteorological stations sited in the northeastern area, September and
October accounted for 39.8 and 41.1% of fall precipitation respectively, whereas
November only accounted for 19.1 %. The amount of time elapsed might be the cause
of the absence of a correlation with autumnal precipitation, since the rain fell principally
in the months of September and October, that is some five to six months before data
acquisition.
A positive correlation was shown with elevation, which can be explained by the
presence of wooded communities in the areas of highest altitude (the colder
temperatures in such areas can lead to lower evapo-transpiration rates and higher water
contents for longer periods, independently of the fall/winter period).
However, the soil moisture distribution estimated by means of AVHRR is
conditioned, above all, by the quantity and spatial distribution of the winter
precipitation. The inclusion of the edaphic, lithological and land-use variables in the
model did not improve the results.
The same analysis was conducted in order to determine the influence of
meteorological, edaphic, lithological and land use variables on the spatial distribution of
soil moisture obtained with ETM+ image. The multiple regression analysis results are
shown in Table 5. Seven independent variables are included in the model chosen, given
17
that the inclusion of an eighth variable in the model did not significantly improve the
explanation of the total variance (2 %). The number of variables included in the model
was greater than in the case of the AVHRR analysis. These additional elements were
principally lithological, edaphic and land-use variables, but some meteorological
variables were also included (winter precipitation and fall temperatures). The total
variance explained was not as great as that explained by the AVHRR-soil moisture
regression model. The reason for this lies in the higher spatial diversity recorded with
the Landsat resolution (30m), which hindered the development of general models for
the whole study area.
The higher spatial resolution of Landsat images allows the detection of detailed
characteristics which NOAA-AVHRR images fail to identify. This means that local
geographical factors mainly affect the spatial configuration of soil moisture.
[Insert Table 5 about here]
Pearson and partial correlations between the independent variables selected in
model 7 and the estimated soil moisture obtained using the ETM+ image are shown in
Table 6. Although winter precipitation shows a low correlation (R) with soil moisture
(R = 0.08) when included in the model and when the combined influence of other
variables is eliminated, it is the variable with the greatest influence over the spatial
distribution of soil moisture, with a significant, positive partial correlation (0.31).
Likewise, the partial correlation of the fall temperature is higher (-0.29) than the partial
correlation between soil moisture and lithological, edaphic and land-use variables. This
confirms the results obtained using the AVHRR-soil moisture image, as it is these two
18
meteorological factors that have the greatest influence on the spatial distribution of soil
moisture.
[Insert Table 6 about here]
The regression model that uses the Landsat-ETM+ soil moisture estimation
image resampled at 1km pixel size improved the explanation with regard to the original
image (32% in the case of 30m and 42% at 1km) (Table 7). Despite this, the result is not
as high as the AVHRR estimation with the same pixel size (55%). The meteorological
variables (fall temperature and winter precipitation) are once again an important
influence in the explanation of soil moisture conditions, with high partial correlations of
0.29 for fall temperature and 0.29 for winter precipitation (see Table 8).
[Insert Table 7 about here]
[Insert Table 8 about here]
Therefore, relationships between soil moisture and prior meteorological
conditions are better detected at a broader rather than at a finer resolution, while the
NOAA-AVHRR data also retain these features more clearly since the broad scale
characteristics (primarily land use) do not disturb this relationship owing to the mixture
of NOAA pixels. However, the broader resolution of the NOAA-AVHRR is not free of
drawbacks. The relationship with ETM+ soil moisture is not significant, and this
relationship is not improved with cell size degradation.
The drought that affected the central Ebro valley in the winter of 2000 was
evident in the satellite images. Its effects were apparent in the high correlation between
the quantity and spatial distribution of winter precipitation and the soil moisture
obtained from the NOAA-AVHRR image (r = 0.44). In fact, the spatial distribution of
19
soil moisture after the winter of 2000 did not correlate with the mean winter
precipitation in the study area (a negative spatial correlation was recorded between both
variables: r = -0.17, p<0.01). This confirms the relationship between the soil moisture
conditions and the quantity and spatial distribution of precipitation during that particular
winter season.
9- CONCLUSIONS
Soil moisture estimates from two distinct remote sensing images and their
correlations with climatic data have been analyzed in this study during a period of
severe drought. Various studies have highlighted the advantages of remote sensing
methods for obtaining soil and vegetation moisture estimates (Cocero et al., 2000;
Nemani et al., 1993; Sandholt et al., 2002; Dupigny-Giroux and Lewis, 1999; Gillies
and Carlson, 1995). The approach described here is based on the relationship between
the TS and the FR. The principal conclusions to be drawn are:
- The method allows soil moisture estimates to be made using satellite images
based on visible and infrared (including thermal) spectral information. The method can
be used at a range of spatial scales so as to concentrate the specific, local ground
measurements. While remote sensing is not a good substitute for ground-based methods,
which offer better quality soil moisture data at specific sites, it does offer obvious
advantages when mapping at regional, continental and even global scales, and when
conducting repeated mapping exercises. Although the method has certain limitations
(see Sandholt et al., 2002), it is particularly useful for the continuous monitoring of
temporal soil water conditions, which enables those areas most affected by drought
episodes in areas of marked climatic variability to be identified.
20
- Significant differences were found between the soil moisture estimates
obtained using AVHRR and ETM+ sensors despite the adoption of an identical
methodology in both cases. Indeed Carlson et al. (1995) report that different remote
sensing systems provide different results. The most likely cause of these spatial
differences was the fact that the satellites recorded the data at different times of day
when the incident solar radiation was not the same. This suggests that the results from
this type of analysis must be interpreted carefully. When comparing results at different
spatial scales it is important that the data are recorded at the same time of day .
- There was a marked response in the distribution of soil moisture to
meteorological variables. This response was more apparent in the AVHRR image than
in the ETM+ image, although in the case of the latter we recorded a more marked
influence of meteorological variables than of lithological, edaphic and land-use
variables in the explanation of the spatial distribution of soil moisture. The nearest
precipitation in time (winter precipitation) was the variable that accounted for most of
the soil moisture distribution. The influence of other factors (lithology, soil type or land
use) varied with the resolution of the image. High-resolution soil moisture images were
affected by a high degree of environmental variability, while at a finer scale
geographical features disturbed the meteorological signal. However, remote sensing is
undoubtedly useful for continuous temporal assessment and as a complement to
meteorological stations.
As Lakshmi (2000) recognizes, precipitation is the main factor in accounting for
the dimensions of the soil water reserve, the infiltration processes and runoff. However,
the process is particularly complex, since after a given period of time an increase in
precipitation does not always lead to an increase in soil moisture. An increase in
incident solar radiation in areas of high soil moisture might, for example, cause
21
increased evaporation and greater water losses. What is evident is that it is difficult to
find a direct relationship between precipitation conditions and soil moisture in any
given period of time. Nonetheless, the uneven spatial impact of the drought in the
winter of 2000 was successfully detected with the soil moisture estimates obtained from
the AVHRR image.
ACKNOWLEDGEMENTS:
This work has been supported by the projects: “La sequía en Aragón: tendencias
climáticas
seculares
y
patrones
de
cambio
ambiental”
(CLI99-098)
and
“Caracterización espacio-temporal de las sequías en el valle medio del Ebro e
identificación de sus impactos” (BSO2002-02743), financed by the Spanish Comission
of Science and Technology (CICYT). We want to thank to the National Institute of
Meteorology (INM) for the readiness of the data used in this work. Thanks to Alfredo
Romo and Juan de la Riva for the NOAA and Landsat images.
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31
Figure 1: Study area. Tic figures are UTM coordinates, in km, in zone 30N.
32
Figure 2- Land surface temperature and NDVI estimated with both sensors at different
resolutions. 1- Surface temperature obtained from Landsat-ETM+ at 30 m resolution; 2Surface temperature from Landsat-ETM+ degraded to 1 km; 3- Surface temperature
obtained from NOAA-AVHRR; 4- NDVI obtained from Landsat-ETM+ at 30 m
resolution; 5- NDVI from Landsat-ETM+ degraded to 1 km; 6- NDVI obtained from
NOAA-AVHRR.
33
1
2
3
0
5
0
.
7
r
=
0
.
7
1
r
=
0
.
7
4
0
.
6
3
0
0
0
.
5
0
.
4
2
9
5
atAVNHDRVIfrreosmoluEtTioMn+(1sekmns)or
Temperature(K)afrtoAmVEHTRM+resseonlsuotiron(1km)
0
.
3
0
.
2
2
9
0
0
.
1
2
8
5
2
8
5
2
9
0
2
9
5
3
0
0
3
0
5
T
e
m
p
e
r
a
t
u
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e
(
K
)
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0
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00
.
10
.
20
.
30
.
40
.
50
.
60
.
7
N
D
V
I
f
r
o
m
A
V
H
R
R
s
e
n
s
o
r
Figure 3- Relationship between surface temperature (1) and NDVI (2) obtained from
AVHRR and ETM+ sensors at a resolution of 1 km. Negative NDVI were not
considered (Gillies et al., 1997).
34
Increment of transpiration
resistance
High cover
Medium vegetation
cover
Low vegetation cover
Increment of soil moisture
Surface temperature
Border of minimum soil moisture
and minimum plant transpiration
Fractional vegetation cover
Figure 4- Surface temperature-vegetation cover relationship space. After Lambin and
Ehrlich (1996).
Surface temperature
Equal fractional
vegetation image
Surface temperature
Limit of dry soil
Limit of moist soil
Fractional vegetation cover
35
Figure 5- Vegetation image fragmentation and determination of soil moisture using the
hottest and coldest limits as dryer and moister areas
1
.
0
1
.
0
2
1
0
.
6
0
.
6
0
.
4
0
.
4
Fractionalvegtaioncover
0
.
8
Fractionalvegteioncover
0
.
8
0
.
2
0
.
2
0
.
0
0
.
0
2
8
42
8
62
8
82
9
02
9
22
9
42
9
62
9
83
0
0
T
e
m
p
e
r
a
t
u
r
e
(
K
)
2
8
5
2
9
0
2
9
5
3
0
0
T
e
m
p
e
r
a
t
u
r
e
(
K
)
Figure 6- Fractional vegetation cover-Temperature spaces. (1) Landsat-ETM+ image,
(2) NOAA-AVHRR image.
36
Interval
%
Interval
%
0.000-0.050
0.13
0.300-0.350 8.99
0.050-0.100
0.58
0.350-0.400 8.62
0.100-0.125
3.27
0.400-0.450 5.63
0.125-0.150
6.64
0.450-0.500 4.94
0.150-0.175 11.06 0.500-0.550 2.63
0.175-0.200 12.47 0.550-0.600 1.76
0.200-0.225
8.97
0.600-0.700 1.62
0.225-0.250
8.86
0.700-0.800 0.58
0.250-0.275
6.23
0.275-0.300
6.83
>0.800
0.20
Table 1: Fractional vegetation intervals selected and percentage respect to the study area from NOAAAVHRR image
37
Fractional vegetation cover interval Moist limit Dry limit Intercept
Slope
0.000-0.050
281.5
295.7
20.74
-0.070
0.050-0.100
286.5
302.3
19.16
-0.063
0.100-0.125
288.1
302.8
20.61
-0.068
0.125-0.150
289.7
301.9
24.76
-0.082
0.150-0.175
290.1
301.9
25.69
-0.085
0.175-0.200
290.2
301.6
26.39
-0.087
0.200-0.225
290.0
301.8
25.45
-0.084
0.225-0.250
289.7
301.3
25.78
-0.085
0.250-0.275
286.3
301.5
19.80
-0.065
0.275-0.300
287.0
301.6
20.63
-0.068
0.300-0.350
287.0
301.3
21.04
-0.069
0.350-0.400
286.5
298.5
25.01
-0.083
0.400-0.450
286.0
297.9
25.03
-0.084
0.450-0.500
286.0
297.0
27.00
-0.090
0.500-0.550
286.7
297.3
28.08
-0.094
0.550-0.600
285.0
296.0
26.90
-0.090
0.600-0.700
285.3
296.0
27.76
-0.093
0.700-0.800
288.1
295.0
43.31
-0.146
>0.800
288.5
292.0
85.63
-0.293
Table 2: Selected dry and moist limits, slope and intercept of the fittings in each range of fractional
vegetation cover. AVHRR image.
38
Figure 7- Winter-2000 precipitation quantity and spatial distribution. Points are the
weather stations used in the interpolation process.
39
Figure 8- Mean winter precipitation quantity and spatial distribution. Points are the
weather stations used in the interpolation process.
Figure 9- Soil moisture spatial distribution for AVHRR and ETM+ sensors, scaled to
[0,1]. 1: soil moisture estimation from NOAA-AVHRR image, 2: soil moisture
estimation from Landsat-ETM+ image degraded at AVHRR resolution, 3: soil moisture
estimation from Landsat-ETM+ image at 30 m resolution. White patches are urban
areas and water bodies excluded from the analysis.
40
1
.
0
=
0
.
5
6
0
.
9r
0
.
8
0
.
7
0
.
6
0
.
5
0
.
4
SoilmoisturedrreosmoluEtTioMn+fsAenVsHoRratsensor
0
.
3
0
.
2
0
.
1
0
.
0
0
.
0
0
.
1
0
.
2
0
.
3
0
.
4
0
.
5
0
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6
0
.
7
0
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8
0
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9
1
.
0
S
o
i
lm
o
i
s
t
u
r
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r
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m
A
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H
R
R
s
e
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s
o
r
Figure10- Soil moisture relationship in the estimates from both sensors.
Model
1
R
R2
0.43 0.19
Adjusted R2 Std. Error of the Estimate
0.19
0.10
41
2
0.61 0.38
0.37
0.09
3
0.68 0.46
0.46
0.08
4*
0.74 0.55
0.54
0.08
5
0.75 0.56
0.55
0.08
Table 3: Explanation of the spatial distribution of soil moisture using a stepwise
multiple regression analysis and based on the soil moisture estimation using the NOAAAVHRR image. * Selected model. Variables included in each model are:
1- Winter precipitation;
2- Winter precipitation and fall temperature;
3- Winter precipitation, fall temperature and elevation;
4- Winter precipitation, fall temperature, elevation and fall
precipitation.
Variable
Correlation (R) Partial correlation
Winter precipitation
0.44**
0.58**
Fall temperature
0.04
-0.54**
Elevation
0.23**
0.44**
42
Fall precipitation
-0.35**
-0.40**
Table 4: Pearson and partial correlation between soil moisture estimation from the NOAA AVHRR image
and the variables inserted in the fourth model of the stepwise regression. Significant correlations at 99 %
are labeled as **.
Model
R
R2
Adjusted R2 Std. Error of the Estimate
1
0.27 0.07
0.07
0.12
2
0.36 0.13
0.13
0.11
3
0.42 0.18
0.18
0.11
4
0.46 0.21
0.21
0.11
5
0.50 0.24
0.24
0.10
6
0.51 0.26
0.26
0.10
43
7**
0.57 0.32
0.32
0.10
8
0.58 0.34
0.34
0.10
Table 5: Explanation of the spatial distribution of soil moisture using a stepwise multiple
regression analysis and based on the soil moisture estimation using the Landsat-ETM+ image at 30
m resolution. * Selected model. Variables included in each model are:
1- Lithology 3 (Limestones and marls);
2- Lithology 3 (Limestones and marls) and Land-use 1 (Coniferous forests);
3- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests) and
Soil type 3 (Soils with calcareous top layers);
4- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), Soil
type 3 (Soils with calcareous top layers) and Land-use 4 (Dryland
agriculture and rangelands);
5- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), Soil
type 3 (Soils with calcareous top layers), Land-use 4 (Dryland agriculture
and rangelands) and Lithology 6 (gipsums);
6- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), Soil
type 3 (Soils with calcareous top layers), Land-use 4 (Dryland agriculture
and rangelands), Lithology 6 (gipsums) and winter precipitation;
7- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), Soil
type 3 (Soils with calcareous top layers), Land-use 4 (Dryland agriculture
and rangelands), Lithology 6 (gipsums), winter precipitation and fall
temperature.
Variables
Correlation (R)
Partial correlation
Lithology 3
-0.27
-0.22
Land-use 1
0.25
0.22
Soil type 3
-0.23
-0.16
Land-use 4
-0.27
-0.25
Lithology 6
-0.10
-0.24
Winter precipitation
0.08
0.31
Fall temperature
-0.17
-0.29
44
Table 6: Pearson and partial correlation between soil moisture estimation with ETM+
image and the variables inserted in the fourth model of the stepwise regression. All
correlations are significant (p < 0.001). Included categories of soil type, lithology and
land-use are defined in table 5.
Model
R
R2
Adjusted R2
Std. Error of the Estimate
1
0.34
0.12
0.17
0.10
2
0.44
0.19
0.19
0.10
3
0.48
0.23
0.23
0.10
4
0.52
0.27
0.27
0.10
5
0.56
0.32
0.31
0.09
6
0.58
0.33
0.33
0.09
7
0.62
0.38
0.38
0.09
8
0.63
0.40
0.40
0.09
9**
0.65
0.42
0.42
0.08
10
0.66
0.43
0.43
0.08
45
Table 7: Explanation of the spatial distribution of soil moisture using a stepwise multiple
regression analysis and based on the soil moisture estimation using the Landsat-ETM+ image
resampled at 1 km. * Selected model. Variables included in each model are:
1- Lithology 3 (Limestones and marls)
2- Lithology 3 (Limestones and marls) and Land-use 1 (Coniferous forests)
3- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests) and
soil-type 3 (Soils with calcareous top layers)
4- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), soiltype 3 (Soils with calcareous top layers) and Land-use 2 (Irrigated lands).
5- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), soil-type 3
(Soils with calcareous top layers), Land-use 2 (Irrigated lands) and land-use 6
(Scrublands).
6- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), soiltype 3 (Soils with calcareous top layers), Land-use 2 (Irrigated lands), landuse 6 (Scrublands).
8- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), soil-type 3
(Soils with calcareous top layers), Land-use 2 (Irrigated lands), land-use 6
(Scrublands), fall temperature and winter precipitation.
9- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), soiltype 3 (Soils with calcareous top layers), Land-use 2 (Irrigated lands), landuse 6 (Scrublands), fall temperature, winter precipitation and winter
temperature.
10- Lithology 3 (Limestones and marls), Land-use 1 (Coniferous forests), soiltype 3 (Soils with calcareous top layers), Land-use 2 (Irrigated lands), landuse 6 (Scrublands), fall temperature, winter precipitation, winter
temperature and lithology 6 (gipsums).
Variables
Correlation (R) Partial correlation
Lithology 3
-0.34
-0.28
Land-use 1
0.28
0.33
Soil type 3
-0.24
-0.22
Land-use 2
0.21
0.24
Land-use 6
0.21
0.24
Fall temperature
-0.18
-0.29
Winter precipitation
0.09
0.29
Winter temperature
-0.13
0.18
Lithology 6
-0.10
-0.18
Table 8: Pearson and partial correlation between soil moisture estimation with ETM+
image resampled at 1 km, and the variables inserted in the fourth model of the stepwise
regression. All correlations are significant (p < 0.001). Included categories of soil type,
lithology and land-use are defined in table 7.
46
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