Full Paper

J. Agric. Meteorol. (農業気象) 65 (4): 349 –355, 2009

MODIS vegetation and water indices for drought assessment insemi-arid ecosystems of IranParinaz RAHIMZADEH BAJGIRAN, Yo SHIMIZU,
Fumiki HOSOI, and Kenji OMASA
Department of Biological and Environmental Engineering, Graduate
School of Agricultural and Life Sciences, The University of Tokyo,
1–1–1 Yayoi, Bunkyo-ku, Tokyo, 113–8657, Japan
Abstract
In order to evaluate how vegetation indices are affected by climatic patterns and to assess the relationships between them in semi-arid ecosystems of Iran, a six year (2000-2005) time series Moderate
Resolution Image Spectrometer (MODIS) data over a six month growing season was used for retrieving Normalized Difference Vegetation Index (NDVI) and two Normalized Difference Infrared Indices(NDII6 and NDII7 using band 6 and 7 MODIS data, respectively) used as Vegetation Water Index(VWI). The study was carried out in dry farming and rangeland areas located in northwestern partof the country. Vegetation indices dynamics and relationships as well as their response to precipitation
were studied. The results revealed the high dependence of temporal dynamics of MODIS vegetation
indices on precipitation, promising the capability to differentiate drought and normal conditions indifferent land cover types. Among all indices, NDVI was found to be the best index to be used for
drought detection for it had better relationship with precipitation and close relationship with the VWIs.
The results of this research can be used as the basis to develop a region-speciﬁc drought index forsemi-arid regions of Iran.
Key words: Drought, NDVI, Precipitation, Vegetation dynamics, Vegetation water index.

consecutive drought years left negative consequences indifferent parts of the country. Because of the scarcity of
meteorological stations and short length of the available
data, traditional drought monitoring methods have both
temporal and spatial shortages. Since 1980s, using
satellite data, many researches have been carried out
for detailed drought detection and vegetation phenology
and changes studies.
Normalized Difference Vegetation Index (NDVI)
using the reﬂectance of near infrared and red bands
is one of the most popular indices used for vegetation
monitoring (Tucker et al., 1981) and drought detection(Kogan, 1990; Liu and Kogan, 1996; Anyamba andTucker, 2005). NDVI reﬂects changes in chlorophyll
content and vegetation amount and has proved to bea useful tool for studying vegetation cover change andamount in semi-arid environments. Weiss et al. (2004)
concluded that NDVI is a good tool for characterizing
vegetation variability in arid and semi-arid regions andallows long time-scale analysis of vegetation behavior

1. Introduction
Arid and semi-arid regions are characterized bylow erratic rainfall between 250-500 mm y–1, periodic
droughts and different associations of vegetative cover
generally short-grass or scrub vegetation. Therefore,
these ecosystems are susceptible to rapid land coverchange resulting in reduction in productivity and moisture conditions.
Semi-arid regions comprise about 61％ of Iranland surfaces having a rainy period generally from
November to May followed by a dry period between
June and October. Therefore, drought is one of thecommon natural disasters in the country. According
to current literature (Pirdashti et al., 2003; Abbaspour
and Sabetraftar, 2005) Iran has experienced the driest
years during 1999-2001 over the last 50 years. Three
Received; March 25, 2009.
Accepted; June 26, 2009.
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J. Agric. Meteorol. (農業気象) 65 (4), 2009
Long term analysis of the vegetation indices over wet,normal and dry years is an indispensable requirement
to provide better insight into vegetation response
to climate variations. In Iran, very limited studies
have focused on the evaluation of climate effects on
ecosystem and drought detection using remotely sensed
data. To the best knowledge of the authors, no literatureon the application of VWIs in Iran is available. The
present research is the ﬁrst attempt to develop six
year time series MODIS data and analysis of NDVI
and VWIs in the ecosystems of Iran. The objectivesof this research were to study: 1) the NDVI and
two Vegetation Water Indices (using band 6 (NDII6)
and band 7 (NDII7)) inter and intra growing season
temporal variations in the semi-arid dry farming andrangeland areas located in northwestern part of Iran,
2) the relationship between NDVI and each VWI
in studied land cover types and 3) the response ofvegetation indices to precipitation as the main climatic
variable to evaluate their capability to be used as atool for drought detection. This information will be ofhigh signiﬁcance to study the vegetation dynamics andunderstand the relationships between these vegetation
and water indices and/or precipitation in semi-arid
regions such as Iran where climatic and ﬁeld data forvegetation condition monitoring are highly limited.

in response to climate variability. Climatic control ofthe spatial patterns of NDVI has been observed in othersemi-arid regions (Malo and Nicholson, 1990; Farrar
et al., 1994; Liu and Kogan, 1996; Vicente-Serrano
et al., 2006).
Recently, Vegetation Water Indices (VWIs) using
wavelengths around 1240 nm, 1640 nm or 2100 nm
in combination with near infrared have been applied
as independent vegetation measure for retrieving
canopy water contents instead of chlorophyll amount.
Because in semi-arid environments, water is usuallyconsidered the limiting factor in biomass production,
VWIs can provide us with valuable information onvegetation and/or soil water stress and detecting
drought status. Gao (1996) introduced the Normalized
Difference Water Index (NDWI) using 1240 nm and
860 nm wavelengths to retrieve vegetation watercontent. Fensholt and Sandholt (2003) developed the
Shortwave Infrared Water Stress Index (SIWSI) using
1640 nm and 860 nm wavelengths and found a strong
relationship between the index and soil moisture insemi-arid ecosystems of Sahelian zone in Africa. Also
studies have shown that the combination of both NDVI
and VWIs can provide great information on vegetation
phenology and types, water availability and drought(Maki et al. 2004).
High-frequency temporal data of MODIS instrument
having appropriate water absorption bands for retrieving canopy water contents offer a great opportunity to
study regional and global environmental changes. Xiao
et al. (2005) applied MODIS band 6 (1628-1652 nm)
and band 2 (841-876 nm) with NDVI and Enhanced
Vegetation Index (EVI) as a surface moisture index
to monitor paddy rice agriculture dynamics. Gu etal. (2007) presented the strong relationship between
MODIS NDVI and NDWI (derived from MODIS band
7 (2105-2155 nm) and band 2) and drought conditions
in grasslands in Finit Hills ecoregion-USA. Cheng et al.
(2008) indicated the capability of MODIS NDWI and
NDII (Normalized Difference Infrared Index) using
MODIS band 2 and 6 for estimation of canopy watercontent in a semi-arid site in Arizona.
Although the application of vegetation indices suchas NDVI and VWIs has proved to be a great toolfor detecting vegetation amount, soil/vegetation watercontent and drought, due to the complexity of eachecosystem, relationship between vegetation activity andwater content in various land cover types in relation
to climate variation is different and must be studied.

2. Materials and Methods
2.1 Data and methodology
MODIS-Terra 8-day 500-meter atmospheric corrected reﬂectance products (MOD09A1) for six years(2000 through 2005) April through September (6months in growing season) were obtained for the
purpose of this research. The images were composites
generally located in the middle of each month andwere labeled by their DOY starting from the firstday of each 8 day period. Cloud pixels and ﬁll value
pixels were masked out using MODIS quality ﬂags
data. Then NDVI and VWIs (NDII6 and NDII7)
were calculated for the entire study area from April to
September 2000-2005 to investigate temporal variationof vegetation indices during growing season and during
dry to wet years and in relation to inter and intra
annual rainfall using the following equations:

NDVI = _ tNIR - tREDi / _ tNIR + tREDi(1)

NDII6, NDII7 = _ tNIR - tSWIRi / _ tNIR + tSWIRi(2)350

P. Rahimzadeh et al. : Drought assessment in Iranrare to almost no precipitation from July to September.
Around 50 percent of the total precipitation falls during
March to May. The dominant land cover types in the
study area are plain rangeland, mountainous rangeland,dry farming, scattered dry farming and irrigated farming along rivers, respectively. For the purpose of thisresearch, three land cover types including dry farming
areas, plain rangeland and mountainous rangeland wereanalyzed. The dry farming land cover was dominatedby wheat and barley ﬁelds. The plain rangeland was
located at 1400-1700 m elevation having mostly annuals
and perennial short grasses including Agropyron spp.,
Brumus spp., Stipa sp., Holtemia persica., Artemisiasp., and Astragalus sp. The mountainous rangelandcomprised both grasses including Festuca spp., Stipa
spp., Brumus spp., Astragalus spp., and woody shrubs
including Juniperus excelsa, Crataegus spp., Amygdalus spp., and Cotoneaster sp. at higher elevations(1900-2200 m). The location of the case study area
is shown in Fig. 1. Plain rangeland and mountainous
rangeland information is not differentiated in thisﬁgure because this information was not included in
the used vector map. These two rangeland types wereseparated according to the ﬁeld survey conducted inAugust 2008 and also available topographic maps ofthe study area.

where tNIR and tRED are the surface reﬂectance valuesof MODIS band 2 (841-876 nm) and band 1 (620-670nm), respectively, tSWIR is the surface reﬂectance valueof MODIS band 6 (1628-1652 nm) for NDII6 or band
7 (2105-2155 nm) for NDII7.
Also daily and monthly precipitation data for 30
meteorological stations (supplied by Iran Meteorological Organization and Water Resources Research
Organization (TAMAB)) in the study area, covering
the years 2000-2005 were collected. For the purpose ofthis research, weekly and monthly precipitation maps
for all six years were produced using Inverse Distance
Weighted (IDW) interpolation method. Precipitation
maps were produced using concurrent rainfall data,
the week before the composite image date, cumulative
two-week, cumulative three-week, and cumulative
one-month, two-month and three-month data.
Three different land cover types were selected using
the National Plan for Land Use Map of Iran vectormap (Ministry of Agriculture, 2005) and ﬁeld survey.
Pixel values for these three land cover types including
dry farming (350 km2), plain rangeland (250 km2) andmountainous rangeland (rangeland and shrubs) (200km2) were extracted from NDVI, NDII6, NDII7 andprecipitation maps for the entire six year period.
2.2 Study area
The study area in this research covers parts of semiarid regions of northwestern Iran located in Zanjan
province. This region has cold semi-arid climate with
330 mm average annual precipitation. Mean annual
temperature is 11℃ with the mean maximum andminimum of 18 and 4.5℃, respectively. Rainy period
is generally from November to May whereas there is

3. Results and Discussion
3.1 Inter and intra annual vegetation dynamics
In this part, vegetation amount and land canopy
water condition anomalies during six month growing
season in six years were studied. Fig. 2 presents
NDVI, NDII6 and NDII7 dynamics over the entire

Fig. 1. Location of the study area.
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J. Agric. Meteorol. (農業気象) 65 (4), 2009

Fig. 2. NDVI, NDII6 and NDII7 dynamics over the entire study period averaged for all vegetation cover types(DOY indicates the beginning of the 8 day period as it is originally labeled by MODIS).
observed. From this ﬁgure, years 2000 and 2001 canbe identiﬁed as the driest years whereas the years
2002-2005 had better water availability. Meteorological
data and research literatures conﬁrm the occurrenceof droughts in years 2000 and 2001 (Pirdashti et al.,
2003; Abbaspour and Sabetraftar, 2005).
In order to provide better comparison between
vegetation indices in drought and normal years
in each land cover type, only vegetation indicesanomaly during 2001 (a drought year) and 2003 (awet year) are presented in Fig. 3 for each land cover
separately. Mountainous rangeland generally exhibits
the highest NDVI value as compared with two otherland cover types. This can be attributed to the denser
vegetation cover (both range and woody species) andless degradation due to the human activity. The plain
rangeland is degraded and overgrazed whereas thedry farming has rotations in a scattered manner. Allvegetation indices show lower values in 2001 in all
three land cover types except at DOY 97. In this year,
the mean annual precipitation in the study area was
205 mm in comparison with 380 mm in 2003 (normal
precipitation 340 mm). Therefore in 2001, reductionin precipitation was around 40 percent. Comparing
different land cover types, some differences in theanomaly patterns between the wet and the dry year
can be easily distinguished. In all land cover types
almost all indices reach their maximum between May
and June (DOY 129-161) in the wet year whereas indry year the patterns are different among different land
covers. In mountainous rangeland, the trends for the
dry and wet years are more or less the same. Howeverin plain rangeland and dry farming land covers, thepeaks in May and June are not observed. The fact

Fig. 3. Comparison of vegetation indices in dry year
(2001) and wet year (2003) in the dry farming, plain
rangeland and mountainous rangeland.
study period averaged for all vegetation cover types.
All indices exhibited similar time-series inter-annual
and intra-annual patterns. Similar observations havebeen made elsewhere (Cheng et al., 2008; Gu et al.,2007). In all cases, increases in vegetation indices from
April to May (DOY 97-129), followed by decreases
from June to September (DOY 161-249) can be easily
352

P. Rahimzadeh et al. : Drought assessment in Iranall three land cover types decreased similarly around20 percent whereas NDII7 values for dry farming andplain rangeland decreased around 50 percent and thatof mountainous rangeland around 30 percent. Thisshows that NDII7 might be more sensitive to drought
conditions than NDVI (Gu et al., 2007).
3.2 Relationship between NDVI and VWIs
This part of the study deals with the evaluation of
the relationships between NDVI and VWIs. These
relationships during six years for individual pixels
for the three studied land cover types are presented
in Fig. 4. Chlorophyll amount (as depicted by NDVI)
and vegetation water content (as depicted by VWIs)
were found to be strongly related. In all cases, therelationships between NDII7 and NDVI were bothmore signiﬁcant and steeper than those between NDII6
and NDVI. This means that NDII7 is more inﬂuenced
by vegetation amount than NDII6 and will probably
be a better index than NDII6 to reﬂect vegetationwater content in semi-arid regions. Among different
land cover types, dry farming (R2: 0.79), mountainous
rangeland (R2: 0.76) and plain rangeland (R2: 0.73)
exhibited the highest coefﬁcients of determination,
respectively. The coefﬁcient of determination was thebest in dry farming. This can be attributed to the factthat dry farming land cover is more uniform in termsof vegetation species variability.
An interesting point observed in Fig. 4 is that thedependence of vegetation water index on NDVI (seenas the slope of the regression line) is different in thethree land cover types. The highest slope is seen in dry
farming followed by plain rangeland and mountainous
rangeland, respectively. While the difference in slope
between the two rangeland types is negligible, thehigher slope in the case of dry farming is considerable.

Fig. 4. Relationships between NDVI and VWIs
during six years for the three land cover types.
that perennials and woody species in high lands areless susceptible to dry conditions can explain thisobservation. In addition, calculating the changes inindex values in percents in the dry year as comparedwith the values in the wet year, it is observed that in the
drought year, VWI values decreased more than NDVI
values especially during summer time (DOY 193-249
in Fig. 3). For example, summer time NDVI values forFig. 5. Anomaly of the three indices and two-month cumulative precipitation averaged for all land covertypes.
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J. Agric. Meteorol. (農業気象) 65 (4), 2009
2003, peaks of precipitation and those of the indicescoincide whereas in years 2004 and 2005, they donot. This is partly due to the distribution of rainfall
over the entire growing season. As reported earlier,precipitation in late growing season has been unable
to induce changes in vegetation condition (Rahimzadeh
Bajgiran et al., 2008).
Coefﬁcients of determination between NDVI, NDII6
and NDII7 and two-month cumulative precipitation arepresented in Table 1. MODIS NDVI, NDII7 and NDII6
from mountainous rangeland showed good correlation
with two-month cumulative precipitation (R 2: 0.65,
0.60 and 0.55, respectively). For dry farming andplain rangeland, pretty good correlations were obtained
between NDVI and two-month cumulative precipitation. On the contrary, the relationship between NDII7
and NDII6 and precipitation was not very good in both
dry farming and plain rangeland. This can be attributed
to the lack of response to summer precipitation andpossible disturbance by human activities.
As clearly seen, NDVI values exhibited higher
R2 values with precipitation than those of NDII7 or
NDII6 in all land cover types and also in entire study
area, indicating that this index is a better indicatorof drought conditions. NDII7 and NDII6 have shown
almost similar relationships with two-month cumulative
precipitation in all land cover types. All correlations
have been found to be statistically signiﬁcant at 1％
error regardless of their coefﬁcients of determination
values.

Huete (2005) reported higher slope and coefﬁcient ofdetermination between MODIS EVI and NDII7 indegraded open forests as compared with protectedareas. It can be suggested that in the present study,
human induced land cover manipulation can have asimilar effect on the relationship between vegetation
amount and water content.
3.3 Relationship of NDVI and VWIs with rainfall
In this part, different precipitation scheme maps (asdeﬁned under Materials and Methods) were produced
to study the VWIs and NDVI response to rainfall in
all land cover types in the study area. We expecteddifferences between NDVI/VWIs and rainfall relationships and also in each land cover type. As VWIs
are expected to reﬂect both vegetation and soil watercontents, it could be expected that these indices beaffected by more recent precipitation events. However,such an observation was not made in the present study
indicating that both NDVI and VWIs are inﬂuenced
similarly by longer term precipitations. Among all
precipitation schemes, monthly and two-month cumulative precipitation patterns had better relationships
with vegetation indices where two-month cumulative
precipitation was found to have the best relationshipwith vegetation indices. Little work has been doneon the relationships between VWIs and precipitationin semi-arid environments. However, the results ofthe present study are in agreement with the available
data (Cheng et al. 2008). Previous studies on NDVI
had shown that in arid and semi-arid regions with drymonths during growing season, it is the cumulative
precipitation which determines vegetation condition
especially during summer (Vicente-serano et al., 2006;
Rahimzadeh Bajgiran et al., 2008).
Fig. 5 presents the anomaly of the three indices andtwo-month cumulative precipitation averaged for allland cover types. Generally the trends of NDVI and
VWIs are similar to that of precipitation indicatingthat the vegetation condition is strongly dependent
on cumulative precipitation. Over the years 2000 to

4. Conclusion
The six year evaluation of the study area using
MODIS NDVI and VWIs showed that temporal
dynamics of vegetation was highly dependent on
precipitation so that it is possible to distinguish drought
conditions from normal ones in different land covertypes. Strong relationships found between NDVI and
VWIs (especially NDII7) suggest that vegetation watercontent is highly dependent on vegetation amount.
Also between vegetation indices, NDVI had better
relationship with precipitation in all land cover types.
Therefore, should only one index be opted for drought
detection in the study area, it would be NDVI becauseof the better relationship between precipitation andclose relationship with the VWIs. However, considering
the different nature of the information provided by
NDVI and VWIs, information from VWIs should
not be overlooked. In general, temporal response of

Table 1. Coefﬁcients of determination (R2) between
NDVI, NDII6 and NDII7 and two-month cumulative
precipitation.
Land use
NDVI NDII7 NDII6
Mountainous rangeland
0.65
0.60
0.55
Plain rangeland
0.48
0.38
0.39
Dry farming
0.53
0.37
0.41
354

P. Rahimzadeh et al. : Drought assessment in Iran
MODIS vegetation indices to precipitation suggests
the potential of these data for long term environmental
monitoring and changes in semi-arid regions of Iran.

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