Total Precipitable Water Vapor (TPW) has an impact on many atmospheric and hydrological processes which can be calculated by the spatial and temporal resolution of weather conditions. Moreover, precipitable water vapor plays a significant role in predicting the weather so that climate change can be constantly monitored by spatial and temporal variations. Water vapor is one of the most abundant greenhouse gases that has an increasing effect on the heat of the earth. Therefore, zonation of precipitable water vapor map in global scale improves the understanding of hydrologists from the hydrological cycle, Earth and atmosphere reactions, the energy cost, and climate change through greenhouse gas emissions. The complex reactions between water vapor, aeroes and clouds, and difficulties in estimating their true amounts make it impossible to evaluate the effect of water vapor on heightening the heat generated by greenhouse gases. One of the most common methods for estimating the precipitable water vapor is the use of remote sensing technique since satellite images are captured continuously within a spatial area. The most crucial advantage of estimating precipitable water vapor by using microwave data over other methods such as optical data is its application and availability on cloudy days. Since microwaves are capable of crossing the clouds, algorithms developed based on them remain functional, whereas optical-based algorithms do not show appropriate performance on the cloudy days. In this study, the efficiency of the remote sensing microwave data in estimating precipitable water vapor parameter has been evaluated in different areas of Iran in order to achieve an algorithm which can predict the desired parameter precisely at spatial resolution and within extreme weather conditions as well as drought.