In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
located in arid areas of Iran. Average annual rainfall in the 
study area is 325.38 mm. The significant amount of rainfall 
happens during the winter in which agriculture fields and 
rangeland vegetation depends. Drought which usually occurs in 
this area causes many problems for its ecosystem. 
2.2 Image and Metrological Data 
The satellite data used in this research consist of ETM+ images 
belonging to 19th August 1999 and 10th July 2002. The 
meteorological data (monthly rainfall) were collected from 18 
rain-gauge stations which have 21 years common statistical 
basis. Table 1 present the annual rainfall of the study area for 
the considered years. As can be observed from the table, the 
rainfall increased considerably in 2002 compared to previous 
years. 
Year 
Annual (mm) 
1999 
405.5 
2000 
134 
2001 
242 
2002 
458.97 
Table 1. The annual rainfall of the study area. 
3. METHODOLOGY 
3.1 Image Pre-processing 
Since the selected images had different acquisition date, sun 
angle correction was applied to remove the differences caused 
by sun. Atmospheric correction was then performed using 
FLASH algorithm in order to obtain the correct reflectance. 
After the radiometric pre-processing, the images were geo- 
referenced using topographical maps in scale of 1/50000 with 
RMSEs equal to 0.47 and 0.2, respectively. 
Since the main objective of this study was to assess drought 
using different indices, digital numbers recorded by the sensor 
were converted to the spectral radiance and reflectance using 
gain and offset parameters provided in the header file of the 
images. 
3.2 Drought Severity by Meteorological Data 
Several drought indices based on meteorological data have been 
introduced by researchers. This includes Palmer Drought 
Severity Index (PDSI), Standardized Precipitation Index (SPI), 
Crop Moisture Index (CMI), Reclamation Drought Index (RDI) 
and etc. each having advantages and disadvantages. In the 
present research, Run-Test method was used because of its 
simplicity and also because it only requires annual rainfall. It 
can be expressed by the equation (1): 
h 
p-Jf 0 <0 => Dry 
(X-X 0 >0=> Wet 
Where X = annual rainfall 
P" = average of annual rainfalls 
The following parameters can be calculating using this method: 
1. Drought duration: the number of consecutive years which 
drought occurs. 
2. Drought magnitude: the total of X- X 0 amounts in each 
period. 
3. Drought intensity: the average of X- X 0 amounts in each 
period. 
4. Drought severity: the maximum amount of X- X 0 in each 
period. 
3.3 PDI and MPDI Indices 
Ghulam et al, in 2006, offered a new index based on spectral 
characteristics of surface in red and near infrared spectral space. 
As it can be seen from Figure 1, the AD line represents the 
changes in surface vegetation from full cover (A) to partial 
cover (E) to bare soil (D), while BC refers to an area with a soil 
moisture status described as wet (B), drier (D) and extremely 
dry (C) (Ghulam, 2006). 
Figure 1. NIR-Red space and PDI (Ghulam, 2006) 
The soil line is a linear relationship between NIR and Red 
reflectance of bare soil (Richardson, 1977). In this paper, in 
order to obtain the soil line parameters (slope and intercept), 
about 500 pixels of different types of the bare soils were 
extracted and were plotted in the feature space of Red-NIR. 
Here are the soil line parameters for each image: 
R nir = 1.19 R Red + 0.001 (1999 image) 
/?nir = 1-19 R Red + 0.003 (2002 image) 
PDI can be calculating using the following equation: 
PDI = —— (%d+K R m ) (2) 
V oc 2 +l 
where oc = slope of soil line 
R Red and R NIR refer to the atmospherically corrected 
reflectance of the Red and NIR bands, respectively (Ghulam, 
2006). 
Crop growth is directly related to the soil moisture. Where soil 
moisture is below a certain level, crops cannot absorb enough 
water from the soil and are exposed to drought. Consequently, 
the soil moisture is the main factor in remote monitoring of 
drought. Soil spectral reflectance decreases with increasing soil 
moisture, Therefore, the severity of a drought can be estimated 
by the close relationship between soil moisture and soil spectral 
reflectance. However, the spectrum received by the sensor is a 
mixture reflected or emitted information from different surface 
targets. Therefore, both the soil moisture status and the 
vegetation status are very important in drought monitoring.