Journal of Environmental Protection, 2010, 1, 448-455
doi:10.4236/jep.2010.14052 Published Online December 2010 (
Copyright © 2010 SciRes. JEP
Spatial Variation of Vegetation Moisture Mapping Using
Advanced Spaceborne Thermal Emission & Reflection
Radiometer (ASTER) Data
Vivek Kumar Singh1, Rajat Satpathy2, Reshma Parveen1, Ayyem Perumal Thillai Jeyaseelan1
1Jharkhand Space Application Center, Ranchi, Department of Information Technology, Government of Jharkhand, Ranchi, India;
2Department of Remote Sensing & GIS, Vidyasagar University, Midnapore, India.
Received August 20th, 2010; revised September 29th, 2010; accepted October 2nd, 2010.
Drought is a recurrent phenomenon in Jharkhand. It affects the livelihoods of the majority of its people, particularly
tribals and dalits living in rural areas. Twelve of the 24 districts of the state, covering 43% of the total land area, are
covered under the Drought Prone Areas Programme (DPAP). Hunger and starvation deaths are reported almost every
year. Vegetation moisture content is one of the key parameters in drought monitoring, agricultural modelling and forest
health mapping. In this paper the three different approaches is described using Advanced Spaceborne Thermal Emis-
sion & Reflection Radiometer (ASTER) data for measuring the vegetation moisture content in a part of Palamu Com-
missionaire of Jharkhand state, which is prone to severe drought. ASTER thermal data was used to calculate land sur-
face temperature using Normalized Differential Vegetation Index (NDVI) emissivity correction method. Reflective
bands are used to determine NDVI, Modified Soil Adjustment Vegetation Index (MSAVI) & Normalised Differential
Water Index (NDWI). The three different vegetation moisture estimation methods namely MSAVI – LST (land surface
temperature) feature space identifica tion, NDWI & Vegetation Dryness Ind ex (VDI) is applied to determine the vegeta -
tion moisture level. The results of three methods were classified and final moisture content map was produced. The re-
sult was validated using rainfall data of study area. This study indicates that by proper pre-processing of ASTER data,
it can be used to estimate the land surface temperature and vegetation moisture content and can be used for drought
Keywords: Vegetation Moisture, ASTER, LST, NDWI, VDI
1. Introduction
Periods of persistent abnormally dry weather known as
droughts, can produce a serious agricultural, ecological
and hydrological imbalance. Drought harshness depends
upon the degree of moisture deficiency, duration and the
size of the affected area [1]. Remote sensing is now
widely used to monitor and predict vegetation character-
istics for sustainable development. Imaging spectrometry
has great potential for monitoring vegetation type and
biophysical characteristics [2]. Vegetation reflectance
spectra are often quite informative, containing informa-
tion on the vegetation chlorophyll absorption bands in
the visible region, the sustained high reflectance in the
near infrared band, and the effects of plant water absorp-
tion in the middle infrared region.
Using that absorption and reflection characteristics of
vegetation researchers have defined many vegetation
indices for monitoring vegetation parameters. Normal-
ized Differential Vegetation Index (NDVI) is used to
measure the forest health. It measures the chlorophyll
content within the vegetation [3]. Normalized Differen-
tial Water Index.
(NDWI) was used to measure the moisture content
within the vegetation field. The surface temperature re-
sponse is a function of v arying vegetation cover and sur-
face soil water content. Lambin and Ehrlich (1996) ex-
plained the Vegetation Index (VI) and Land Surface
Temperature (LST) space in terms of evaporation, tran-
spiration and fractional vegetation coverage [4]. Accord-
ing to Lambin and Ehrlich (1996), the variations in sur-
face brightness temperature are highly correlated with
variations in surface water content over base soil [4].
Advanced Spaceborne Thermal Emission & Reflection
Radiometer (ASTER) data have a distinct vegetation
Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &
Reflection Radiometer (ASTER) Data
Copyright © 2010 SciRes. JEP
absorption and reflection bands. So, it is used to measure
the moisture content within the vegetation field of
Jharkhand state, which is regularly affected by drought.
Almost every year some part of that state is affected by
drought. The main objective of this study is to evaluate
the potential of ASTER imagery to determine the varia-
tion of vegetation moisture within the study area. Mean-
while the area suffer drought year after year. So, for
identifying the situation of drought, this study was car-
ried out. Three approaches are used to estimate the vege-
tation moisture levels and then the result is compared
with the rainfall status of the study area in the same time.
2. Study Area
The present study area is the part of Latehar, Garhwa,
Palamu & Gumla district of Jharkhand state (Figure 1).
Geographically, the area is located on southern part of
the Chotanagpur plateau. Maximum height of the area is
about 1120 meter above mean sea level. The area drained
by North Koel, Burha River and their tributaries. The
area enjoys a tropical climate. The maximum temperature
in summ er rise s to abo ve 32 and the minimum in winter
falls to 03-04. The average annual rainfall is about 1600
mm. The latitudinal and longitudinal extent of the study
area is as follows – Latitudinal extent –23°15’N-23°50’N
and Longitudi nal extent - 83°24’E-83°38’E.
3. Methods
3.1. Data Used
Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER) Level-1A satellite data (Table 1)
is used for this study. The imagery was georectified in
UTM, WGS 84, and Zone-45N. Apart from satellite data
ASTER DEM, SOI Toposheet on 1:50000 scale and
Figure 1. Location of the study area & selected test sites.
Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &
Reflection Radiometer (ASTER) Data
Copyright © 2010 SciRes. JEP
Table 1. ASTER satellite data specifications used for the
study [5].
Product Spectral
Bands Spectral range
(µm) Spatial
Resolution (M)
1 0.5-0.60 15
2 0.63-0.69 15
3N 0.78-0.86 15
3B 0.78-0.86 15
4 1.60-1.70 30
5 2.145-2.185 30
6 2.185-2.225 30
7 2.235-2.285 30
8 2.95-2.365 30
9 2.360-2.430 30
10 8.125-8.475 90
11 8.475-8.825 90
12 8.925-9.275 90
13 10.25-10.95 90
14 10.95-11.65 90
other published map and literature were used for the
The flow chart shown in Figure 2 illustrates the pro-
cedures followed to generate the crop moisture map.
3.1.1. Method of LST Calcul ation
Thermal atmospheric correction is must to calculate the
most accurate temperature using ASTER thermal data.
Here, the In-Scene Atmospheric Compensation algorithm
(ISAC) [6], is applied to remove atmospheric noises from
the thermal bands.
The algorithm first determines the wavelength that
most often exhibits the maximum brightness temperature.
This wavelength is then used as the reference wavelength.
Only spectra that have their brightest temperature at this
wavelength are used to calculate the atmospheric com-
pensation. At this point, for each wavelength, the refer-
ence blackbody radiance values are plotted against the
measured radiances. A line is fitted to the highest points
in these plotted data and the fit is weighted to assign
more weight to regions with denser sampling. The com-
pensation for this band is then applied as the slope and
offset derived from the linear regression of these data
with their computed blackbody radiances at the reference
wavelength. Upwelling atmospheric radiance and at-
mospheric transmission are approximated using the fol-
Figure 2. Processing flow for mapping vegetation moisture.
lowing method. First, the surface temperature of every
pixel is estimated from the data and used to approximate
the brightness temperature using the Planck function and
assuming an emissivity of 1. Next, a line is fitted (using
one of two methods) to a scatter plot of radiance vs.
brightness temperature. The atmospheric upwelling and
transm ission are then de r ived from the slope and offset of
this line.
Satellite thermal infrared sensors measure radiances at
the top of the atmosphere, from which brightness tem-
peratures TB (also known as blackbody temperatures)
can be derived by using Plank’s law [7].
TkIn hc
 
where h is Planck’s constant (6.62 × 10-34 J-sec), c – ve-
locity of light (2.998 × 108 m sec-1), λ – wavelength of
emitted radiance (m), Bλ – blackbody radiance (Wm-2
μm-1). With the known LSE, the emissivity-corrected
LST (TS) can be calculated by the Stefan Boltzmann law
where σ is the Stefan Boltzmann constant (5.67 × 10-8
Wm-2 K-4), B – total amount of radiation emitted (Wm2-),
Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &
Reflection Radiometer (ASTER) Data
Copyright © 2010 SciRes. JEP
TS – surface temperature (K), TB – brightness temperature
(K), ε – land surface emissivity.
In order to determine an actual surface temperature it
is necessary to determine the emissivity of the land sur-
face features.
3.1.2. NDVI Method for Emissivity Correction
Retrieval of LST from multispectral TIR data requires an
accurate measurement of emissivity values of the surface
[9]. The emissivity of a surface is controlled by such
factors as water content, chemical composition, structure,
and roughness [10]. For vegetated surfaces, emissivity
can vary significantly with plant species, areal density,
and growth stage [10]. In the mean time, emissivity is a
function of wavelength, commonly referred to as spectral
emissivity [11]. Pixels representing the land surface are
usually mixed pixels of surfaces-types such as vegetation
and soil. The effective emissivity of a pixel can be esti-
mated by summing up the contributions from its sur-
face-types. Van de Griend and Owe (1993) [12] found a
high correlation between measured emissivity and NDVI,
which measured from visible and near-infrared spectral
They gave the following relation:
1.0094 0.047*ln NDVI
 (5)
But this is only valid for areas with a large patches
covered by vegetation or soil. Valor and Caselles (1996)
[13] proposed an operational model of determining the
effective emissivity that is applicable to areas with sev-
eral soil and vegetation types and changing vegetation
 
 (6)
where εv, εs are the emissivity of the full vegetation and
bare soil, and Pv is the vegetation cover fraction. εv, εs
can be obtained from the Formula (4) where the patches
of vegetation and soil are selected so that they are a lot
bigger than a pixel. Pv is derived according to Carlson
and Ripley (1997) [14 ], from:
3.1.3. Modified Soil Adjustment Vegetation Index
A Modified Soil Adjusted Vegetation Index (MSAVI),
proposed by [15] was created using ASTER VNIR image.
The following equation was used to calculate the index
Figure 3. Distribution of land surface temperature calcu-
lated from ASTER image.
0.50.5 2
 (8)
where, n
is reflectance at Near Infra Red (NIR) (AS-
TER Band 3n) band and r
is reflectance at Red (R)
band (ASTER Band 2).
4. Results & Discussion
4.1. Estimation of Vegetation Moisture
4.1.1. First Approach: VI-LST Triangular Space
The Vegetation Index (VI) – Land Surface Temperature
(LST) Triangular Space Method proposed by Lambin
and Ehrlich (1996) was used to map the relative variation
of vegetation moisture within Baresanr reserved forest.
The MSAVI created from ASTER VNIR image was used
as Vegetation Index and Land surface kinetic tempera-
ture (in degree centigrade) calculated from ASTER TIR
image was used as LST [16].
The MSAVI image and LST image were stacked to
create a two layer image that was then was classified
using unsupervised classification method. A feature
space image was created using the two layer image. The
MSAVI image layer was used along X axis and the LST
image layer was used along Y axis. The feature space
Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &
Reflection Radiometer (ASTER) Data
Copyright © 2010 SciRes. JEP
Figure 4. Feature space interpretation after Lambin and
Ehrlich’s (1996).
Figure 5. The feature space image created using the ASTER
imagery and the comparison with Lambin and Ehrlich’s
(1996) interpretation. Figure 6 shows the feature identifica-
tion procedure using the created feature space image.
image was used to identify the signatures of the classified
image on the basis of Lambin and Eh rlich’s (1996) inter-
pretation (Figure 4).
Figure 5 shows the feature space image created using
the ASTER imagery and the comparison with Lambin
and Ehrlich’s (1996) interpretation. Figure 6 shows the
feature identification procedure using the created feature
space image.
The initial classified image was recorded into three
classes: Moderate Vegetation Moisture, Low Vegetation
Moisture and Very Low Vegetation Moisture. Figure 7
shows the vegetation moisture classification using fea-
Figure 6. Procedure of moisture level identification using
feature space image.
ture space image interpretation.
4.1.2. Second Approach: NDWI Based Moisture Index
The Normalized Differential Water Index (NDWI) pro-
posed by [17,18] was calculated using the VNIR and
SWIR ASTER imagery. The equation of NDWI is as
The Figure 8 shows the classified vegetation moisture
4.1.3. Third Approach: Vegetation Dryness Index
Vegetation Dryness Index (VDI) estimates vegetation
water deficit at canopy level [19]. NDVI and NDWI are
used to calculate th e maximal and minimal water content
lines. VDI is then calculated by the position of the point
falling in between the maximal and minimal water con-
tent lines. VDI is a ratio between NDVI and NDWI and
gives us the vegetation water stress. The Figure 9 shows
the vegetation moisture variations of the test sites derived
using VDI method.
All the classified map shows that the moisture level of
the two test sites are mainly very low to low. So, the ve-
getations are unhealthy or stressed due to unavailability
of water in this time. The percentage of the area covered
by each class in the classified VDI, NDWI and MSAVI-
Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &
Reflection Radiometer (ASTER) Data
Copyright © 2010 SciRes. JEP
Figure 7. Vegetation moisture classification using feature space image interpretation.
Figure 8. Classified NDWI derived vegetation moisture map.
Figure 9. Classified VDI derived vegetation moisture map.
Spatial Variation of Vegetation Moisture Mapping Using Advanced Spaceborne Thermal Emission &
Reflection Radiometer (ASTER) Data
Copyright © 2010 SciRes. JEP
Table 2. Variation of vegetation moisture estimated by three different methods.
Percentage of area coverage
VDI method NDWI method MSAVI+LST Feature space
interpretation method
Moisture Level
Site - 1 Site - 2 Site - 1 Site - 2 Site - 1 Site - 2
Moderate Vegetation Moistu re 1.05 0.69 0.61 1.24 2.20 1.27
Low Vegetation Moisture 78.90 66.42 68.32 75.21 71.26 75.18
Very low Vegetation Moisture 20.04 32.89 31.06 23.54 26.53 23.54
LST images were calculated and tabulated to compare
the values (Table 2).
5. Conclusions
This paper explores the relationship between LST and
different vegetation index to measure the moisture con-
tent within the vegetations in drought prone area of
Jharkhand state during the early summer season. The
results of the analysis of the current research indicate that
crop moisture can be estimated using ASTER imagery.
The land surface temperature calculated from ASTER
TIR image shows high temperature within the entire
study area which also indicates the drought. Vegetation
moisture estimated using the NDWI method does not
agrees with the VDI derived vegetation moisture map but
agrees with land surface temperature and precipitation
Vegetation moisture estimated using MSAVI-LST
Triangular method does not correlate with the v egetation
moisture map generated by NDWI (site-1), but does cor-
relate with NDWI derived vegetation moisture map
(site-2) and precipitation map. Therefore, it can be con-
cluded that NDWI and MSAVI-LST Triangular method
has potential to estimate vegetation moisture variation
more accurately than VDI method.
Field data related to vegetation moisture may help to
calibrate the method fo r more precise result, and suitable
microwave imagery can be used to compare the results
obtained from optical imagery.
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Reflection Radiometer (ASTER) Data
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