ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012) 1–12

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ISPRS Journal of Photogrammetry and Remote Sensing
journal homepage: www.elsevier.com/locate/isprsjprs

Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature
Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stressdetection in semi-arid regions of IranParinaz Rahimzadeh-Bajgiran, Kenji Omasa ⇑, Yo Shimizu
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, Japana r t i c l e

i n f oArticle history:
Received 2 July 2010
Received in revised form 29 October 2011
Accepted 30 October 2011

Keywords:
NDVI
NDII7
Land surface temperatureSemi-arid regions
Water statusa b s t r a c t
This study aims at developing appropriate methods to detect water stress in the semi-arid regions of Iran.
To do this, the Vegetation Dryness Index (VDI) concept, originally developed for forest ﬁre detection, wasapplied to detect vegetation/soil water stress. A modiﬁed approach towards the Temperature Vegetation
Dryness Index (TVDI) concept, incorporating air temperature and a Digital Elevation Model (DEM) todevelop the improved TVDI (iTVDI) is also introduced and the results are compared with the original TVDI
and VDI through veriﬁcation by precipitation and soil moisture data. Evaluation of the VDI in the study
area showed that there was no signiﬁcant relationship between the VDI values and precipitation or soil
moisture indicating its inappropriateness to be used for water stress detection. Compared with the TVDI,
results indicated that there were more statistically signiﬁcant relationships between the iTVDI and recent
precipitation and soil moisture in the four land cover types in the study area. This indicates that the iTVDI
is highly inﬂuenced by recent precipitation during the summer and can therefore estimate water status. It
is concluded that the iTVDI can be successfully used for vegetation/soil water stress monitoring in thesemi-arid regions of Iran.
Ó 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier
B.V. All rights reserved.

1. Introduction
Semi-arid regions, which are characterized by low and erratic
annual precipitation, comprise 61% of Iran. Therefore, vegetationand soil water stress is a major widespread problem in the country.
Drought is a common natural disaster, requiring action to monitor
and detect water stress, to mitigate its negative impacts on human
life, and wildlife and plant communities. Water content in soil andvegetation can be estimated using three different methods: (1)ﬁeld measurements; (2) meteorological data; and (3) remote sensing. Although ﬁeld measurements are the most accurate methods
for estimating soil and vegetation water content, they are expensive and time consuming especially for mountainous and remoteareas. There exist several climatic and hydrological drought indices(point-based) which are based on meteorological data. However,point-based data are often poorly distributed and are insufﬁcientand not available for timely water stress/drought detection. In contrast, remote sensing methods enable us to monitor water stress at
higher temporal and spatial resolutions at lower cost and time.

⇑ Corresponding author. Tel.: +81 3 5841 5340; fax: +81 3 5841 8175.
E-mail address: aomasa@mail.ecc.u-tokyo.ac.jp (K. Omasa).

Since the 1980s a number of remote sensing indices have beendeveloped to investigate vegetation condition and amount or soil
moisture for drought monitoring. Advanced High Resolution Radiometer (AVHRR) data, supplied by National Oceanic and Atmospheric Administration (NOAA), were the ﬁrst remotely sensed
data used for vegetation condition and changes monitoring providing the ability to generate the NDVI (Normalized Difference Vegetation Index), and the land surface temperature (LST). The NDVI is acommon vegetation index reﬂecting vegetation amount and chlorophyll content and one of the ﬁrst remotely sensed indices successfully used for monitoring vegetation condition and drought
detection at regional scale (Anyamba and Tucker, 2005; Kogan,
1990; Liu et al., 1994; Malo and Nicholson, 1990; Tucker et al.,
1981; Vicente-Serrano et al., 2006). However, the NDVI has two
main limitations for drought monitoring; ﬁrst the apparent timelag between rainfall and the NDVI response (Rahimzadeh-Bajgiran
et al., 2008, 2009; Rundquist and Harrington, 2000; Wang et al.,2001) and second, little inﬂuence of signiﬁcant precipitation events
later in the growing season (plant seed production period) on
the NDVI (Rahimzadeh-Bajgiran et al., 2008, 2009; Wang et al.,2001). The NDVI is often referred to as a greenness index which
represents the vegetation amount and chlorophyll content of thevegetation rather than water status of the region. Therefore, there

0924-2716/$ - see front matter Ó 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.isprsjprs.2011.10.009

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is a need for a more sensitive indicator than the NDVI for water
stress monitoring.
To overcome these problems, approaches combining vegetation
indices and the LST were proposed for determining canopy water
status. The LST is a good indicator of energy partitioning at the surface. In the case of drought, the LST can be used as a proxy for soil
and vegetation water stress estimation for the reason that in dry
condition due to the lack of soil moisture, both leaf temperature(a result of stomata closure) and surface temperature will increase.
Application of the LST for detecting canopy water stress and crop
evapotranspiration rate dates back to the 1970s using aerial thermal scanners (Bartholic et al., 1972; Heilman et al., 1976). The earliest applications of thermal scanning techniques and energybalance analysis for plant physiology study on stomata response
and transpiration activities at the leaf level were reported in theearly 1980s (Hashimoto et al., 1984; Omasa et al., 1981a,b; Omasa
and Croxdale, 1991). Later, using remote thermal sensing devices,
indices such as the Crop Water Stress Index (CWSI) were developed for irrigation scheduling (Idso et al., 1981; Jackson et al.,1981).
Using concepts such as the CWSI at larger scales (using remotely sensed data) was problematic due to the soil backgroundand mixed pixels. Other approaches were necessary to solve theabove mentioned problem (Jones, 2004). One approach, used by
Nemani et al. (1993), made use of the fact that the NDVI is an indicator of green vegetation cover within the pixels, and therefore theslope of LST vs. NDVI plot gives a measure of stomata conductanceand evapotranspiration. Goetz (1997) reported that there is a negative correlation between the LST and the NDVI and indicted that
surface temperature can rise rapidly with water stress. Other
researchers developed the concept of the LST vs. NDVI relationshipand described the triangular shape of the data falling between the
LST and the NDVI axes (Gillies and Carlson, 1995; Gillies et al.,
1997; Price, 1990). On the basis of the triangular space, the Temperature Vegetation Dryness Index (TVDI) was developed for soil
moisture detection in Senegal (Sandholt et al., 2002). Moran et al.
(1994) described the data in the LST vs. vegetation index space
as a trapezoid and developed the Water Deﬁcit Index (WDI) basedon the CWSI concept for soil and vegetation water deﬁcit. Incorporation of the LST data into drought monitoring procedures for naturally vegetated areas is still a subject for further improvements. A
comprehensive review on the application of remotely sensed LST/
vegetation indices for the estimation of soil surface moisture andevapotranspiration can be found in Petropoulos et al. (2009) andLi et al. (2009).
Vegetation Water Indices (VWIs) using near infrared wavelengths around 1240, 1640 or 2100 nm in combination with thataround 860 nm have been applied as independent vegetation
measures for retrieving canopy water content (Tucker, 1980).
VWIs are considered to provide us with valuable information onvegetation and/or soil water status and detecting drought.
According to these concepts, the NDII (Normalized Difference
Infrared Index) using 2100 and 860 nm wavelengths (Hardisky
et al., 1983) or using 1600 and 860 nm wavelengths (Hunt andRock, 1989) was proposed to retrieve land surface moisture condition. Gao (1996) introduced the Normalized Difference Water
Index (NDWI) using 1240 and 860 nm wavelengths to retrieve
vegetation water content. A similar formula to that of the NDWI
to retrieve the EWT (Equivalent Water Thickness) was applied to
shrub steppes, shrub savannahs, tree savannah, and savannah
woodlands (Ceccato et al., 2002). Fensholt and Sandholt (2003)
developed the Shortwave Infrared Water Stress Index (SIWSI)
using 1640 and 860 nm wavelengths and found a strong relationship between the index and soil moisture in semi-arid environment of Sahelian zone in Africa. Cheng et al. (2008) indicatedthe capability of the MODIS NDWI and NDII (using MODIS band

2 (841–876 nm) and 6 (1628–1652 nm)) to estimate canopy
water content in a semi-arid site in Arizona. It seems that the
NDWI is more suitable for detecting severe vegetation water
stress, whereas the NDII can be used to detect low/moderatewater stress more accurately as bands with higher water absorption are used in its calculation.
Many studies have shown that the combination of both the
NDVI and VWIs can provide great information on vegetation phenology and types as well as water availability. The main reason
for these combinations is that high chlorophyll contents do notnecessarily indicate high water contents and vice versa (Huete,2005). Rather, vegetation indices such as the NDVI and VWIs eachprovide information on two different aspects of vegetation condition, being chlorophyll amount and water content, respectively.
Maki et al. (2004) adopted the concept used by Moran et al.
(1994) and developed the Vegetation Dryness Index (VDI) using
the SPOT/VEGETATION NDVI and NDWI to estimate ﬁre risk in aforest in Russia. Also MODIS band 2 and band 6 (NDWI) with the
NDVI and the Enhanced Vegetation Index (EVI) as a surface moisture index were applied to monitor paddy rice agriculture dynamics (Xiao et al., 2005). Gu et al. (2007) developed the Normalized
Difference Drought Index (NDDI) using MODIS NDVI and NDWI
(derived from MODIS band 2 and band 7 (2105–2155 nm)) for
drought detection in grasslands in Finit Hills ecoregion-USA andreported that the NDDI can detect drought in the study area. However, very limited studies have been conducted and well documented for vegetation and soil water stress detection in arid andsemi-arid regions using these indices in combination with vegetation indices such as the NDVI.
In Iran, the AVHRR–NDVI has been employed for drought monitoring and the results have shown its capabilities for drought
detection in the studied regions (Rahimzadeh-Bajgiran et al.,2008). Also studies on the MODIS NDVI and NDII6 (using band 2
and band 6) and NDII7 (using band 2 and band 7) anomalies for
six years have shown the capability of the NDVI and VWIs for differentiating dry years from normal years in the northwest region of
the country (Rahimzadeh-Bajgiran et al., 2009). However, as mentioned earlier, the indices were unable to timely detect changes inwater status.
Combined application of the NDVI with the LST or VWIs has not
yet been studied in Iran. As both VWIs and the LST contain information on soil and vegetation water status, the application ofthese indices together with vegetation indices such as the NDVI
in drought-prone regions of the country is assumed to be considerably beneﬁcial. High-frequency temporal data of the MODIS
instrument provide three appropriate water absorption channels
for retrieving canopy water content, and suitable thermal andreﬂective bands. This offers a great opportunity to apply
vegetation indices, VWIs and LST at regional and global scale
simultaneously.
In this paper, we try to apply VWIs and the LST together with
the NDVI for soil/vegetation water stress detection in a semi-arid
region in northwest Iran using MODIS products. Three different approaches will be presented and the results will be compared. First
the VDI, originally developed for forest ﬁre detection, will be evaluated for vegetation/soil water stress estimation at a larger scale
for rangeland and dry farming land covers. A modiﬁed approach towards the TVDI incorporating air temperature and a Digital Elevation Model (DEM) to develop the improved TVDI (iTVDI) will be
introduced and the results will be compared with those for the
TVDI. This modiﬁcation is meant to improve the performanceof the TVDI in the study area where variation in elevation increases
the TVDI uncertainty. Temporal and spatial variations of the
VDI, the TVDI and the iTVDI will be studied and ﬁnally the resultsof all indices will be validated by using precipitation and surface
soil moisture data.

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2. Materials and methods
2.1. Deﬁnition of the indices2.1.1. The Vegetation Dryness Index (VDI)
Maki et al. (2004) interpreted the NDVI vs. NDWI scatter plot toestimate vegetation water deﬁcits. The scatter plots indicate thatmaximum and minimum water content per unit area (NDWI) areassociated with leaf quantity per unit area, approximated by the
NDVI. Maki et al. (2004) illustrated that this feature is similar to
the WDI presented by Moran et al. (1994). The WDI indirectly
estimates Fuel Moisture Content (FMC) per leaf quantity with thesurface minus air temperature difference (Ts  Ta). The VDI, in contrast, directly estimates FMC per NDVI using the NDWI. Fig. 1aillustrates the theoretical trapezoidal shape and the deﬁnition of
the limits, i.e. dry and wet edges, used to derive the VDI:

VDI ¼ 1  ðBC=ABÞð1Þ

where AB and BC are the distances represented on Fig. 1a, between
the dry edge (lower limits of the scatter plot) and wet edge of the
trapezoid (upper limits in the scatter plot). Vertices (n) in Fig. 1aare poor vegetation with rich water content (n = 1), poor vegetation
with poor water content (n = 2), rich vegetation with rich watercontent (n = 3) and rich vegetation with poor water content(n = 4). The VDI is lower for wet and higher for dry conditions andvaries between 0 and 1. Note that to be comparable with the twoother indices studied here (the TVDI and the iTVDI), the NDVI isthe x axis in Fig. 1a. In this study, the NDII7 was used instead of
the NDWI as it uses a more appropriate water absorption bandfor semi-arid regions (Rahimzadeh-Bajgiran et al., 2009).
2.1.2. The Temperature Vegetation Dryness Index (TVDI)
Evapotranspiration rate has been shown to be a good indicatorof the land surface water status and can be theoretically estimatedthrough the surface energy balance equation:

Rn ¼ G þ H þ LE

ð2Þ

where Rn is the radiation ﬂux, G is the soil heat ﬂux, H is the sensible heat ﬂux, L is the latent heat of vaporization and E is the evapotranspiration. Substituting factors in Eq. (2), the latent heat ﬂux LE
can be estimated by the following equation:

LE ¼ Rn  G  H ¼ aRsw þ eðRlw  rTs4 Þ  G  qcp ðTs  TaÞ=r a

ð3Þ

where Rsw is the shortwave radiation ﬂux from the environment, ais the absorption coefﬁcient of shortwave radiation of the surface,
Rlw is the longwave radiation ﬂux from the environment, e is theemissivity of the longwave radiation of the surface, r is the Ste-

fan–Boltzmann constant, qcp is the volumetric heat capacity, andra the aerodynamic resistance to heat transfer (Jones, 1992). Ts
and Ta are the surface temperature and the air temperature, respectively. Idso et al. (1981) showed that Ts  Ta was linearly related to
Vapour Pressure Deﬁcit (VPD) for well-watered crops.
The key step in the development of methods for estimating
evapotranspiration rate was developed by Jackson et al. (1981)
who introduced the Crop Water Stress Index (CWSI). The CWSI
has been shown to be only applicable for fully vegetated covers,
and as mentioned before, its application was problematic for remote sensing data. Later, by using the Jackson et al. (1981) concept,
the WDI was developed by Moran et al. (1994). The WDI estimates
the ratio of LE/LEp by using land surface temperature and ambient
air temperature and can be used for partially vegetated covers. LEp
is the latent heat ﬂux for potential evapotranspiration rate. The
WDI is calculated using Eq. (4):

WDI ¼ 1  LE=LEp 
¼ 1  ðTs  TaÞ  ðTs  TaÞmax = ðTs  TaÞmin  ðTs  TaÞmax
ð4Þ
where (Ts  Ta) is the surface (soil and canopy) temperature minus
air temperature at measuring time, and (Ts  Ta)min and (Ts Ta)max are the minimum and maximum (Ts  Ta), respectively, forthe same vegetation index value (for example, the NDVI) in the
trapezoid space of the vegetation index vs. (Ts  Ta). WDI needs calibration using Penman–Monteith combined with energy balance
equation.
In the WDI model, some inputs required to estimate the verticesof the trapezoid are difﬁcult to assess, for example, the aerodynamic resistance (highly affected by wind speed and plant structure), maximum and minimum canopy resistances (both closely
related to stomata closure) and soil heat ﬂux (Mendez-Barroso
et al., 2008). Providing these data especially for natural vegetation
covers is difﬁcult. Therefore, Sandholt et al. (2002) interpreted the
AVHRR LST vs. NDVI relationship in terms of soil moisture and proposed the Temperature Vegetation Dryness Index (TVDI) which is a
simpliﬁcation of the WDI model. In the TVDI method, the study
area should be large enough to provide a wide range of NDVI andsurface soil moisture conditions. The TVDI is estimated using thefollowing equation:

TVDI ¼ ðTsobs  Tsmin Þ=ða þ bNDVI  Tsmin Þ

ð5Þ

where Tsobs is the observed LST at a given pixel, Tsmin is the minimum LST in the NDVI vs. LST space, deﬁning the wet edge, NDVI
is the observed Normalized Difference Vegetation Index and a andb are parameters deﬁning the dry edge, modeled as a linear ﬁt todata (Tsmax = a + bNDVI), where Tsmax is the maximum LST observa-

Fig. 1. (a) The trapezoidal shape resulting from the NDII7 vs. NDVI plot to derive the VDI using Method 1, (b) the theoretical trapezoidal shape resulting from DT vs. NDVI toderive the iTVDI (location of wet edge and dry edge in scatter plot of DT vs. NDVI for DOY 2003-193).

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tion for a given NDVI. Parameters a and b are estimated from anarea large enough to represent the entire range of surface moisture
content. The TVDI is lower for wet and higher for dry conditions andvaries between 0 and 1. Further information on the TVDI calculation
can be found in Sandholt et al. (2002).

effects of topography on net radiation as described by VincenteSerrano et al. (2004) or daily solar zenith angle variation while
using 8-day composite satellite data has been made. Effects of soil
and vegetation types on Ts and NDVI as mentioned by Lambin andEhrlich (1996) can also be additional sources of error.

2.1.3. The improved Temperature Vegetation Dryness Index (iTVDI)
As it is assumed that the main source of variation in the TVDI issoil moisture, air temperature is not considered in the model
which may increase the uncertainty of the TVDI for larger areasand higher NDVI values (Sandholt et al., 2002). An inherent
assumption when applying the TVDI is that Ta is constant for thesubset or window over which the index is estimated. A typical error in studies based on the TVDI, is that this assumption is violated
in selecting too large an area. On the other hand, when using the
TVDI to estimate soil moisture status, heterogeneity of the earth
surfaces increases the uncertainty of the TVDI to estimate soil
moisture. Therefore, the TVDI should ideally only be applied in regions with little topography. To correct for the effect of topography, Ran et al. (2005) used an approach to correct Ts with a DEM
before constructing the AVHRR Ts/NDVI space. Hassan et al.
(2007) proposed a correction method to use a DEM to infer local
pressure from altitude and then transform surface temperature
to potential temperature.
In this research, we wanted to use the TVDI concept to estimate
soil and vegetation water statuses in a mountainous area. Therefore, it seems essential to incorporate air temperature (Ta) in the
TVDI equation (Idso et al., 1981; Moran et al., 1994) to improve
its performance and address the inconsistency in retrieving evaporation and transpiration status (water content status of both low
and highly vegetated areas). Using Ts  Ta gradient on the y-axis
is more physically correct, as it makes the scatter plot of the data
more similar to what is described by Moran et al. (1994). The same
concept has also been used by Nieto et al. (2010a) for air temperature estimation using remotely sensed data and Nieto et al.
(2010b) for dead fuel moisture estimation.
Air temperature decreases as altitude increases, a phenomenon
known as the environmental lapse rate. Therefore, a Digital Elevation Model (DEM) of the study area has also been used to calculate
the lapse rate to improve the performance of the index to estimate
evapotranspiration at different altitudes in each speciﬁc month.
The new index hereafter called the improved TVDI (iTVDI) is calculated using Eq. (6) and is schematically presented in Fig. 1b.

2.2. The study area

iTVDI ¼ ðDT obs  DT min Þ=ðDT max  DT min Þ ¼ BC=AB

ð6Þ

where DTobs is observed Ts  Ta and Ta is observed air temperature
calibrated using DEM. DTmin and DTmax are the minimum and maximum DT, respectively, for the same vegetation index value (herethe NDVI). AB and BC are the distances represented on Fig. 1b, between the dry edge and wet edge in the DT vs. NDVI scatter plot
as described by Sandholt et al. (2002). Vertices (n) in Fig. 1b are baresoil with no evaporation (n = 1), bare soil with max evaporation(n = 2), rich vegetation cover with no transpiration (n = 3) and richvegetation with max transpiration (n = 4). The iTVDI is lower for
wet and higher for dry conditions, and similar to the TVDI, varies
between 0 and 1.
In the estimation of the iTVDI from satellite data it is assumed
that the trapezoidal scatter plot should be built with thermal datacollected over areas experiencing the same thermal inputs, particularly the same net radiation and wind speed. A normalization procedure was suggested by Gillies and Carlson (1995) to correct forvariation of net radiation and wind speed. On the other hand, tobe determined correctly, the trapezoid of the study area should
contain full range of variability in land surface conditions fromdry bare soil to saturated bare soil and from water stressed vegetation to well watered vegetation. In this research no correction for

The study area covers cold semi-arid regions between 35°N and
37°N latitudes and 47°E and 49°E longitudes in the north-westernpart of Iran. This area is agriculturally important and contains two
protected areas. The area is generally high lands having an altitudeof 300–3000 m (Fig. 3a). Mean annual precipitation in this region isaround 350 mm, where mean annual temperature is 10.8 °C withthe mean maximum and minimum of 18 and 4.5 °C, respectively.
The rainy period is generally from November to May, whereas
there is almost no precipitation from July to September. The major
land cover types in the study area are rangeland, dry farming, andirrigated farming along rivers. The study area also has low to moderate vegetation cover except for irrigated farmlands, and occasional forest and shrubs located in the northern part of theregion. Four land cover types including dry farming, plain rangeland, mountainous rangeland, and rangeland and shrubs were selected for analysis in this research (Fig. 2). Detailed informationon the characteristics of these land cover types will be presented
in Section 3.4.
2.3. Meteorological data
Ambient air temperature from 17 meteorological stations (supplied by Iran Meteorological Organization) for the years of 2000–
2005 was used for this research for retrieving Ts  Ta (DT) maps.
Temperature data were interpolated using the smart interpolation
method (Willmott and Matsuura, 1995). For using this method, sixteen Radar Topography Mission (SRTM) DEM images (90 m) of thestudy area were obtained from Global Land Cover Facilities (GLCF).
As considering the general value of the lapse rate (0.6 °C/100 m) is
inaccurate and may differ in different places and different months,
in this research, environmental lapse rates were calculated using
linear regression between air temperature and elevation in eachmonth for available meteorological stations. Lapse rate values calculated for different months in the growing season decreased from
0.48 °C/100 m in April to 0.33 °C/100 m in September. To evaluate
interpolation accuracy, a linear regression was performed between
estimated and observed temperatures at two stations not includedin the interpolation (R2:0.99). Finally, DT maps were produced foreach date during summer time for years 2000–2005. Fig. 3 presents
the DEM of the study area (a), an example of the interpolated Ta
map without the DEM incorporation (b), interpolated Ta map after
the DEM incorporation (c), and corresponding Ts map of day 193 of
the year 2001 (DOY 2001-193) (d).
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. Precipitation maps
were produced using Inverse Distance Weighted (IDW) interpolation method.
2.4. Remote sensing data
For the purpose of this research, MODIS-Terra 8-day, 500-m
atmospheric corrected reﬂectance products (MOD09A1) and 8day 1000-m land surface temperature products (MOD11A2) for2000 through 2005, April through September, were obtained toretrieve time series of the VDI, TVDI and iTVDI maps. The images
were composites generally located in the middle of each month

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5

Fig. 2. The location of the study area and the land cover classiﬁcation map derived from Landsat7/ETM+ images.

Fig. 3. The DEM of the study area (a), an example of the interpolated Ta map without the DEM incorporation (b), interpolated Ta map after a DEM incorporation (c), andcorresponding Ts map (d) of DOY 2001-193.

and were labeled by their DOY starting from the ﬁrst day of the 8day period. Daily 500-m atmospheric corrected reﬂectance product(MOD09GA) and daily 1000-m land surface temperature product(MOD11A1) for 14 August 2008 were used to verify the VDI, TVDI
and iTVDI results with measured volumetric soil moisture data. All
MODIS products were re-projected using MODIS-MRT tool. Cloudand ﬁll value pixels were masked out using MODIS quality ﬂags
data. Pixels with viewing geometry (view/solar zenith angles) values 645° were used. The 500 m 8-day composite MODIS reﬂectance product (MOD09A1) was used to derive the NDVI andNDII7 for all six-year data in six months using the following
equations:6

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NDVI ¼ ðqNIR  qRED Þ=ðqNIR þ qRED Þ

ð7Þ

NDII7 ¼ ðqNIR  qSWIR Þ=ðqNIR þ qSWIR Þ

ð8Þ

where qNIR and qRED are the surface reﬂectance values of MODIS
band 2 (841–876 nm) and band 1 (620–670 nm), respectively, andqSWIR is the surface reﬂectance value of MODIS band 7 (2105–
2155 nm) for NDII7. MOD11A2 data contain LST values generated
from MODIS data using the generalized split-window LST algorithm(Wan and Dozier, 1996; Wan and Li, 1997). For the purpose of thisresearch, MOD11A2 LST data were resized to 500-m for all six years
study period.
To calculate the VDI, wet and dry edges were determined fromthe scatter plot of the NDII7 and NDVI. Because there was no clear
method for the estimation of wet and dry edges, a method similar
to that of Verbesselt et al. (2007) was used in this research in two
ways. For the ﬁrst method (herein referred to as Method 1), forty
two samples from all natural vegetation covers and bare soil for
6 years during growing season (April through September) were extracted from the NDVI and NDII7 maps. Equations were determined using the NDII7 vs. NDVI plot of the extracted data for thewhole study area. For the second method (herein referred to as
Method 2), forty two samples were divided into four land covercategories (dry farming, plain rangeland, mountainous rangeland,
and rangeland and shrubs) and for each category wet and dryedges were calculated separately. The location of the dry edge
and wet edge in the NDII7 vs. NDVI scatter plot for all land covers
in the study area (Method 1) is presented in Fig. 1a. The VDI resultspresented throughout this paper are all derived from Method 1.
The reason will be explained in Section 3.1.
The method presented in Sandholt et al. (2002) was used to calculate wet and dry edges for the TVDI using Ts vs. NDVI scatterplot. Coefﬁcients of dry edge and wet edge equations were calculated by means of linear regression using the feature space Ts vs.
NDVI data for each date during the summer time (June–August)in the 6 year study period. These periods are considered water limited periods during growing season. Lack of available moisture content during this period reduces the length of the growing season
and decreases plant biomass. For retrieving the iTVDI, the same
method as the TVDI was used with the difference that dry edge
and wet edge equations were calculated from DT (Ts  Ta) vs. NDVI
space (Fig. 1b). As the estimation of dry and wet edges can be affected by cloud and noise, care was taken to remove these sourcesof error.
As the land cover classiﬁcation map of the study area was available at the coarse resolution, for the purpose of this research, anaccurate land cover map was produced using ETM+ data. Four
frames of Geocover-orthorectiﬁed Landsat 7 products (path andraw: 167/034, 167/035, 166/035 and 166/034) during May andJune 2000 were used. As the study area was located in four framesof Landsat7/ETM+ images, in order to have a radiometrically consistent mosaiced image for classiﬁcation, digital numbers (DN) of
all ETM+ imagery were converted to atmospheric corrected reﬂectance. First, digital numbers of all ETM+ imagery were converted tospectral radiance. Then, using Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric correction softwaremodule, spectral radiances in all ETM+ images were converted toatmospherically corrected reﬂectance. After atmospheric correction, all images were mosaiced. Different band combinations werestudied and ﬁnally all bands (except band 6) of ETM+ data wereused for classiﬁcation. Using all bands, differentiation from dry
farming and rangelands was more accurate. Training data werecollected using ﬁeld data, Google Earth, ETM+/NDVI map, andETM+ spectral scatter plots. Maximum likelihood classiﬁcations
method was used to classify the data. Maximum overall accuracy
obtained was 85.05% with a Kappa Coefﬁcient of 0.76 (Fig. 2).

2.5. Fieldwork
A ﬁeld survey was conducted during 13–15 August 2008 to collect information on vegetation cover types and soil moisture data.
Measurements were made in rangelands and harvested dry farming. In each sampling site, four volumetric soil moisture content
samples at two depths (5 and 15 cm) were taken using an SM200 sensor (Delta-T Devices, UK). The soil moisture measuring device is able to measure volumetric soil moisture content with 3%
accuracy. Soil samples were also taken for soil physical properties
studies and sensor calibration. Field data were used for land coverclassiﬁcation and indices veriﬁcation.

3. Results and discussion
3.1. Spatial and temporal variation of the VDI, the TVDI and the iTVDI
Spatial and temporal variations of the VDI (calculated using
Method 1), the TVDI and the iTVDI for three dates in summer(DOY 161, 193 and 217) during 2001 and 2003 are shown in
Fig. 4 through Fig. 6, respectively. During the 2000–2005 study period, year 2001 represents a dry year with around 240 mm annual
precipitation whereas year 2003 is considered a normal to slightly
wet year having around 350 mm annual precipitation. DOY 161,
193 and 217 are dates during water limited period of the year.
Trends for the VDI images presented in Fig. 4 show that in general, in 2001 and 2003 the VDI maps do not have any particular inter and intra annual dynamics in any of the four studied land covertypes. Although years 2001 and 2003 are different regarding water
status, the VDI cannot indicate these differences. Even in DOY
2001-161, the VDI values show better water availability (average
VDI value 0.53) than DOY 2003-161 (average VDI value 0.57) (average index values are means for the entire study area). In 2003, despite the lack of any precipitation from DOY 161 to DOY 217, theVDI values exhibit an opposite trend, reducing from an averageof 0.57 to 0.50. Furthermore, some areas such as mountainous
rangeland in the west of the study area (yellow pixels) and dry
farming in the north (blue pixels) (see Fig. 2, land cover classiﬁcation map) show unusual dryness and wetness, respectively in DOY
161, 193 and 217 in both years.
Spatial and temporal variations of the TVDI in the study area
(Fig. 5) show that in both 2001 and 2003, DOY 161 had the lowest
TVDI values (average values 0.54 and 0.55, respectively) due toApril and May rainfalls. In general, DOY 161 in all other years
has shown to have lower TVDI values. For DOY 2001-193, the TVDI
values (average value 0.62) are higher than DOY 2001-161 due tolack of precipitation. Also for DOY 2003-193, the TVDI values (average value 0.58) are increased because of low rainfall. The TVDI values for DOY 2001-217 decrease (average value 0.56) due to theoccurrence of precipitation (between 10 and 50 mm) in July, but
for DOY 2003-217, the TVDI values keep rising (average values of0.60) because of dryness in July. The study of the TVDI in summer
time shows that the index values will increase from June to August
especially in poor rangeland and dry farming, if there is no
precipitation.
Fig. 6 depicts variations in the iTVDI where a similar trend tothat of the TVDI can be observed. In both 2001 and 2003, DOY
161 exhibits the lowest iTVDI values (average value 0.56 and
0.54, respectively) as a result of April and May rainfalls. DOY
2003-161 is wetter than 2001-161. In DOY 2001-193 the iTVDI values (average value 0.69) are increased due to lack of precipitationas compared with DOY 2001-161. In DOY 2003-193, because of low
rainfall, the iTVDI values (average value 0.64) are higher than DOY
2003-161. In DOY 2001-217, the iTVDI values decrease (averagevalue 0.60) due to the occurrence of precipitation in July, whereas

P. Rahimzadeh-Bajgiran et al. / ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012) 1–12

7

Fig. 4. Spatial and temporal variations of the VDI in DOY 161, DOY 193 and DOY 217 in 2001 and 2003 (black pixels are masked areas due to noise and clouds).

Fig. 5. Spatial and temporal variations of the TVDI in DOY 161, DOY 193 and DOY 217 in 2001 and 2003 (black pixels are masked areas due to noise and clouds).

for DOY 2003-217, the iTVDI values continue to increase (averagevalue 0.67) because of dryness in July. Similar to the TVDI, the iTVDI values will increase from June to August especially in poor
rangeland and dry farming, if there is no precipitation. Generally,
similar temporal and spatial dynamics can be seen in the TVDI
and iTVDI maps. The main difference between the TVDI and iTVDI
maps is that the TVDI shows mountainous areas (including mountainous rangeland and rangelands and shrubs) wetter than the iTVDI. Non-mountainous areas on the contrary are shown to have
similar index values in both the TVDI and iTVDI maps. Other
researchers have also reported similar problems for the TVDI in
mountainous areas (Hassan et al., 2007; Ran et al., 2005).
The VDI calculated in this research is expected to show thewater deﬁcit for the region from low (bare soil) to high (full vegetation cover) NDVI values (Fig. 1a). Thus, it is expected to provide
similar results to those of the TVDI or iTVDI. The VDI quantiﬁes
the water content (NDII7) of land surface at a given NDVI value.
Studies have shown that the NDII variation is similar to that of

the NDVI (Cheng et al., 2008; Rahimzadeh-Bajgiran et al., 2009)
and therefore its variation is different from that of Ts or DT. In fact,
DT is an indicator of evapotranspiration rate under constant thermal environments (see Eq. (2)). All land cover types (different NDVI
values) can have the same DT values according to their water status whereas the NDII7 has seasonal variations making it lessresponsive to precipitation in the late growing season. On the other
hand, the NDII7 is characteristic to each region, and each land cover type has its own range of NDII7 values (Huete, 2005). As a result,
the VDI classiﬁes the land covers according to the availability ofwater content at each NDVI value, and because the ratio of the
NDVI to NDII7 in each land cover type stays constant during growing season, the VDI variations cannot be observed. Therefore, blue
colored areas in the northern part of the study area (which are dry
farming and poor rangeland with better water status than similarareas with the same NDVI values) are always classiﬁed as wet areas(VDI = 0.1–0.3) in the VDI maps despite changes during growing
season. Also, mountainous rangelands (yellow colored areas in8

P. Rahimzadeh-Bajgiran et al. / ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012) 1–12

Fig. 6. Spatial and temporal variations of the iTVDI in DOY 161, DOY 193 and DOY217 in 2001 and 2003 (black pixels are masked areas due to noise and clouds).

western part of the study area) which have higher NDVI values arealways classiﬁed as very dry areas (VDI = 0.7–1). This is due to thecharacteristics of these areas where the NDII7 values are lower
than other areas with the same NDVI values.
To cope with the problem of different NDII7 values in each land
cover type in the VDI retrieval, dry and wet edges were also calculated for each land cover type, separately (Method 2). Then, the VDI
values were re-calculated for each land cover. Although this resulted in both dry and wet areas in each land cover, on the average,
the VDI values were still higher in mountainous areas and lower indry farming which can be due to the heterogeneity in each landuse. Therefore, the problem of unusual wetness or dryness previously observed in Method 1 still persisted in all images. For thisreason the results of Method 2 were not presented in this paper.
At this stage, working on the improvement of the VDI index was
stopped because although ﬁnding very homogenous areas would
probably improve the performance of the VDI, this would also
make the application of the VDI very difﬁcult when applying it atregional scale. On the other hand, application of this index for
homogenous areas may be limited to only dense vegetation cover
due to the difﬁculties in using the NDII7 in lower vegetation covers(Fensholt and Sandholt, 2003). As mentioned earlier, considerable
ﬂuctuations in the NDII7 values in different land cover types especially those with low to moderate vegetation covers, make it more
difﬁcult to use the VDI for such regions.
3.2. Relationships between the VDI, the TVDI, the iTVDI and
precipitation
Linear correlation coefﬁcients established between the VDI, theTVDI, the iTVDI and three different precipitation schemes in thefour main land cover classes are presented in Table 1. Due to thelack of precipitation in the summer (there was no precipitation
within 20 days before each image) the ﬁrst 30-day cumulative precipitation before each image was used as the ﬁrst precipitation
scheme. The two other precipitation schemes were 60-day and90-day cumulative precipitations. As seen in Table 1, better correlations were established for the iTVDI than the other two indicesfor all three of the precipitation schemes. No signiﬁcant relationship was found between the VDI and any of the three precipitation
schemes in the study area. Missing values in Table 1 are low posi-tive correlation coefﬁcients between the VDI and precipitation.
Compared with the VDI, generally higher coefﬁcients are obtained
between the TVDI and precipitation. However, the TVDI was foundto have statistically signiﬁcant relationships with precipitation
only in plain rangeland where better correlations were observed
when a longer precipitation scheme was used. The coefﬁcients obtained between the iTVDI and precipitation indicate that there are
signiﬁcant relationships between this index and 30-day cumulative precipitation in all land cover types. The relationship between
the iTVDI and 30-day cumulative precipitation was strongest fordry farming than for any of the other land cover types.
Fig. 7 presents the anomaly of the VDI, the TVDI and the iTVDI
with 30-day cumulative precipitation for the studied land covertypes during the summer in 2000–2005. The VDI and precipitation
trends are not similar in any of the four land cover types indicatingthat the VDI is not inﬂuenced by recent precipitation. Althoughtrends of the TVDI and the iTVDI are similar to that of precipitation,better agreements are observed for the iTVDI indicating that thisindex is strongly dependent on rainfall. The TVDI shows less sensitivity to precipitation changes; for example, while in the rangelandand shrubs the iTVDI values change considerably when precipitation occurs, such changes are not reﬂected in the TVDI values.
Comparison of the TVDI and iTVDI values in the studied land covertypes shows that the TVDI generally has lower values except forplain rangeland where the values are close to the iTVDI. Also, compared with indices such as the NDVI and VWIs (Cheng et al., 2008;
Rahimzadeh-Bajgiran et al., 2008, 2009; Wang et al., 2001), the
iTVDI responds to more recent precipitation even in late growing
season.
3.3. Relationship between the VDI, the TVDI, the iTVDI and soil
moisture
Relationships between the VDI, the TVDI, the iTVDI and precipitation were analyzed in Section 3.2, which implied relationships
between the indices and soil moisture or evapotranspiration. As
soil moisture database in Iran was not complete, data collectedduring the August 2008 ﬁeld survey was used to study relationships with the indices.
As mentioned in Section 2.4, MODIS data for dates of 14 and 15
August 2008 were collected. Due to the severe noise in data on 15

9

P. Rahimzadeh-Bajgiran et al. / ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012) 1–12

Table 1
Linear correlation results between the VDI, the TVDI, the iTVDI, and three different cumulative precipitation schemes in the four land cover classes during summer 2000–2005.
Land cover types/precipitation

VDI

TVDI

iTVDI

R value

P value

R value

P value

R value

P valueDry farming
30-day precipitation
60-day precipitation
90-day precipitation

0.18
–
–

0.5396
–
–

0.24
0.03
0.22

0.41420.91200.4560

0.84
0.72
0.66

0.00020.00370.0144

Plain rangeland
30-day precipitation
60-day precipitation
90-day precipitation

0.10
0.03
0.20

0.71670.91790.5106

0.60
0.64
0.74

0.02360.01380.0024

0.77
0.78
0.75

0.00130.00080.0029

Mountainous rangeland
30-day precipitation
60-day precipitation
90-day precipitation

–
–
–

–
–
–

0.45
0.21
0.09

0.10490.46440.7677

0.70
0.40
0.17

0.00600.15970.5761

Rangeland and shrubs
30-day precipitation
60-day precipitation
90-day precipitation

0.06
0.09
0.04

0.83500.75510.8793

0.08
0.02
0.00

0.79800.94180.9933

0.57
0.32
0.13

0.03980.26380.6709Fig. 7. Anomaly of the VDI, the TVDI, and the iTVDI with 30-day cumulative precipitation in the studied land cover types during the summer in 2000–2005.

August, data for 14 August were used for retrieving the VDI, theTVDI and the iTVDI. Fortunately, air temperature and meteorolog-

ical conditions were similar on both dates. Year 2008 is considereda dry year with no precipitation occurring during the last 30 days

10

P. Rahimzadeh-Bajgiran et al. / ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012) 1–12

before the time the images were taken. Soil moisture data werecollected from rangelands and dry farming areas at 1200–2200 m
elevation. Linear correlations were established between the VDI,
the TVDI or the iTVDI, and soil moisture content measured at
depths 0–5 and 0–15 cm. The results of the correlations at thedepth of 0–5 cm are presented in Fig. 8.
Similar to relationships with precipitation (Section 3.2), the iTVDI was found to have better relationships than the other two indices with soil moisture. No signiﬁcant relationship was foundbetween soil moisture and the VDI at either depth. Furthermore,as shown in Fig. 8, the relationship between soil moisture andthe VDI was found to be positive, whereas the relationships between soil moisture and the TVDI and iTVDI were found to be negative. The relationship between the TVDI and soil moisture showedthat there is a signiﬁcant relationship between them at the depthof 0–5 cm (R = 0.54, P value = 0.038) but no signiﬁcant relationship was found between soil moisture and the TVDI at the depthof 0–15 cm (R = 0.30, P value = 0.284). Results show that there
were signiﬁcant relationships between soil moisture and the iTVDI
values at both depths, with a stronger relationship at 0–5 cm(R = 0.80, P value = 0.0003) than 0–15 cm (R = 0.67, P value = 0.006). Comparing the iTVDI values at different elevations
showed that using air temperature and environmental lapse rate
information can improve the iTVDI performance because areas
having similar soil moisture values but at different elevations have
similar iTVDI values.
Results for the VDI in this study may not be directly comparablewith those from Maki et al. (2004) because, in that study, they veriﬁed the VDI results based on SWIR measurement at 1580–
1750 nm with pixels of burnt areas in the Russian Far East forestsand found that these pixels had comparatively higher VDI values
before ignition. However, no veriﬁcation with soil moisture or fuelmoisture content (FMC) was made. On the other hand, Verbesselt
et al. (2007) found weak positive relationships while correlating
the VDI values based on SWIR measurement at 1580–1750 nmwith FMC data in savannah ecosystems and concluded that the
NDVI and NDWI still performed better than the VDI. Using a differ-ent SWIR wavelength (2100 nm) in this study showed similar weak
positive relationships between soil moisture and the VDI values. By
modifying the methodology to calculate the VDI, we tried to improve the VDI performance but the results were still unsatisfactory.
The VDI cannot be used as a proxy for water stress detection in the
study area. Although absorption at 2100 nm might be too strong
which can result in saturation, it seems that the VDI failure in
the present study can be attributed to other reasons as mentioned
in Section 3.1.
As we expected, when using the TVDI to estimate soil moisturestatus, heterogeneity of the earth surface increases the uncertaintyof the TVDI to estimate soil moisture and higher elevated areas will
exhibit higher soil moisture content. Although samples taken in
the northern part of the study area from rangeland and shrubs
had low soil moisture content in the TVDI map, a large part of thisarea exhibited low TVDI values and appeared to be a no stress area.
This has caused weaker relationships between soil moisture andthe TVDI in this research. Soil moisture is related to the evapotranspiration rate which is in turn related to (Ts  Ta), not solely
Ts. Therefore, if Ta is not considered, the estimation will not be
accurate. As Ta is not accounted for in the TVDI, this index is lessappropriate to indicate water status in the study area, especiallywhen the area is vast and elevation changes greatly.
3.4. Variations of the VDI, the TVDI and the iTVDI in different land
cover types
According to the classiﬁcation map produced using Landsat7/
ETM+ data (Fig. 2) and results of the ﬁeld survey, the major land
cover types were rangeland, dry farming, irrigated farming andbare soil. Rangelands in the study area were classiﬁed into three
categories including plain rangeland, mountainous rangeland,
and rangeland and shrubs. Plain rangeland is characterized by
low and degraded vegetation covers mostly annual and perennial
short grasses including Agropyron spp., Brumus spp., Stipa sp., Holtemia persica, Artemisia sp., and Astragalus sp. located between 1200
and 1700 m in elevation. Mountainous rangeland is less degraded

Fig. 8. Linear correlation between the VDI, the TVDI, the iTVDI, and soil moisture content measured at depth of 0–5 cm in August 2008.

P. Rahimzadeh-Bajgiran et al. / ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012) 1–12

and comprised of annual and perennial short grasses and perennials such as Artemisia spp., Festoca spp., Bromus spp., and Acantholimon spp. at higher elevations (1700–3000 m). Rangeland andshrubs are also located at higher elevations but vegetation covercompositions are different from plain and mountainous rangeland.
This land cover is mostly located in the northern part of the study
area and comprised of both grasses, including Festuca spp., Stipa
spp., Brumus spp., Astragalus spp., and woody shrubs, Juniperus excelsa, Crataegus spp., Amygdalus spp., and Cotoneaster sp. at elevations between 1400 and 2700 m.
Plain rangeland and dry farming generally show higher VDI values whereas rangeland and shrubs exhibit lower values but showing no seasonal dynamics (Fig. 4). Three regions display different
results; mountainous rangelands which have very high VDI values
and poor rangeland and dry farming in the north having low VDI
values. As the VDI showed no correlations with precipitation or soil
moisture and its spatial and temporal variations were also abnormal, it can be inferred that changes in the VDI values are notdependent on water availability, but rather they are mainly inﬂuenced by land cover type.
Comparing the TVDI values and land cover classiﬁcation map(Figs. 5 and 2) show that the TVDI values are lower in mountainous
rangeland and rangeland and shrubs. Although this is a logicaltrend, because of the poor relationships with precipitation and soil
moisture discussed earlier, it can be concluded that TVDI ﬂuctuations are less inﬂuenced by water availability.
On the contrary, the iTVDI values were found to be in close
agreement with precipitation and soil moisture, therefore it canbe inferred that changes in the iTVDI are dependent on the water
status in the study area. Comparing the iTVDI values and land cover classiﬁcation map (Figs. 6 and 2) shows that bare soil, plain
rangeland and dry framing always have higher iTVDI values incomparison with mountainous rangeland and rangeland andshrubs in the study area. This can also be easily seen in Fig. 7 where
anomalies of the iTVDI in different years are presented. As cumulative precipitation for the growing season was higher in mountainous rangeland and rangeland and shrubs than other land covers, it
can be concluded that rangeland and shrubs, mountainous rangeland and dry farming have better water availability, in a descending order, whereas plain rangeland generally suffers from water
stress during the summer.

4. Conclusions
In this study, the application of the VDI, the TVDI and the iTVDI
for water stress detection in the semi-arid regions of Iran was evaluated and their seasonal and annual variations were studied. The
relationships between these indices and precipitation, soil moisture, and land cover types were also analyzed.
Evaluation of the VDI in the study area revealed that this index
was more correlated with land cover types, than precipitation orsoil moisture. The VDI does not seem to be an appropriate index
to detect water stress in the study area. On the other hand,
although the TVDI anomalies were in agreement with those of precipitation, it was found to have only signiﬁcant relationships with
precipitation in plain rangeland, whereas, no signiﬁcant relationships were observed in the three other land covers. The relationship between the TVDI and soil moisture was only signiﬁcant at
the 0–5 cm depth. The iTVDI outperformed the VDI and the TVDI
having stronger relationships with precipitation and soil moisture.
The iTVDI relationship with recent precipitation in the four studied
land cover types was statistically signiﬁcant and its anomaly wasin agreement with precipitation. In addition, strong relationships
were observed between the iTVDI values and soil moisture data
at both studied depths indicating that this index is a better indica-11

tor of instantaneous water status than the two other indices. The
application of iTVDI to estimate evapotranspiration and determine
water stress in the study area can be recommended.
Theoretically, the iTVDI, the TVDI and the VDI are all expected
to estimate water status. The iTVDI was shown to provide better
results as compared with the original TVDI when Ta and a DEM
were introduced into the model. Efforts to obtain satisfactory results for the VDI, even after modifying the approach were in vain.
Although absorption at 2100 nm might be too strong, which can
result in saturation, it seems that the VDI failure in this study
can be attributed to other reasons. In both the VDI and the iTVDI
deﬁnitions, the NDVI is coupled with the other variable (either
NDII7 or DT), and therefore, the main reason for their different response to actual water status can be attributed to the y-axis response to precipitation or soil moisture. Both DT and the NDII7
represent water content of the land cover, but their behaviors are
not identical. DT variations are a function of irradiation and climatic conditions and are mainly inﬂuenced by recent precipitation
whereas the NDII7, similar to the NDVI, has seasonal variations
making it less responsive to precipitation in the late growing season. Although the NDII7 values are less variable in higher vegetation covers, in lower vegetation covers (NDVI < 0.4), NDII7 values
are highly variable in each land cover type with the same NDVI values at any given time. Thus, in the two dimensional space of the
NDVI and NDII7 as presented in the VDI deﬁnition, the NDII7 value
may not be a good representative of water status in low to moderate heterogeneous vegetation covers. Therefore, the VDI does notseem to be applicable to semi-arid heterogeneous regions.
Using DT and the NDII7 for water stress detection will provide
different information on water status. While DT offers information
on instantaneous water status, the NDII7 provides information on
annual and seasonal dynamics of water status. The NDII7 can differentiate wet and dry years in semi-arid regions and its application in these areas would be beneﬁcial (Rahimzadeh-Bajgiran
et al., 2009). However, should the NDII7 be used to determine
water status over a time period, it should be incorporated into anindex in which its values can be compared with the best conditions
at the same pixel level.

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