Drought is a complex and multi-causal environmental problem, which can have serious socio-economic consequences. Recently, the IPCC (Intergovernmental Panel on Climate Change) concluded in its Fourth Assessment Report (AR4) that South Asia and the Middle East will experience severe and prolonged droughts due to climate change. global climate, particularly the increase in greenhouse gases in the atmosphere (IPCC, 2007). Drought is a natural disaster of meteorological origin whose effects are aggravated by human activities. Sometimes drought affects large areas, even several countries, for a long period of time. Drought has serious consequences on the food productivity of a land, and even on the life expectancy of its inhabitants. The consequences of drought lead to socio-economic and ecological problems (WGA, 1996) (Jeyaseelan, 2005; Pongracza et al., 1996). Iran, which encompasses drylands, has been periodically threatened by drought episodes, which have had devastating consequences on society and the environment (Shamsipour et al., 2008). Therefore, studying drought requires multiple sources of datasets. In other words, when designing a regional planning project for sustainable development, acquiring up-to-date data is crucial, especially for countries with arid to semi-arid climates. Recent innovations in remote sensing methods have provided new solutions for the study of environmental problems in geosciences. In the assessment of natural risks such as drought, remote sensing provides rapid and instantaneous spatial data on natural phenomena; they are useful in decision making as well as in weather forecasting (Sunyurp et al., 2004). Drought monitoring by remote sensing depends on the factors that cause drought (Jeyaseelan, 2005). Drought indicators and variables, obtained using remote sensing data, may include certain uncertainties, induced by the sensitivity of the factors or their dependence on meteorological and environmental conditions. Additionally, some non-standard algorithms may lead to an erroneous estimate of drought intensity. More effective methods for increasing the accuracy of evaluation and analysis of remote sensing data are to apply models that can combine into data layers. Geographic information systems (GIS) are used to combine data layers in drought modeling. Recently, spatial technologies, such as SR and GIS, and digital modeling techniques have been developed as powerful tools for ecological environmental assessment (Krivtsov, 2004; MacMillan et al., 2004; Store and Jokimäki, 2003 ). The use of these technologies not only provides a platform to support multi-level, hierarchically integrated analysis of resources and the environment, but also integrates the obtained information into a comparative theoretical analysis of the ecosystem. Meanwhile, Plummer (2000) argued that prospects for combining ecological models and remote sensing data would focus on precision estimation, issues of spatial and temporal scale, and long-term comprehensive datasets...