Estimation of Atmospheric Column and near Surface Water Vapor Content Using the Radiance Values of Modis

One of the most important parameters in all surface-atmosphere interactions (e.g. energy fluxes between the ground and the atmosphere) is atmospheric water vapor. It is also an indicator among others to modeling the energy balance at the Earth’s surface. Total atmospheric water vapor content is an important parameter in some remote sensing applications especially land surface temperature (LST) estimation. As such, total atmospheric water vapor content and LST are used as key parameters for a variety of environmental studies and agricultural ecological applications. Estimation of an accurate LST requires the atmospheric water vapor content estimation. This study is concerned with retrieving total atmospheric water vapor content (W) using Moderate Resolution Imaging Spectrometer (MODIS). We have used a ratio technique to estimate the column water vapor based on MODIS data. However Atmospheric Infrared Sounder (AIRS) column water vapor and AIRS MMR near surface water vapor have been taken into account to calculate coefficients of the equation in the ratio technique. Then the accuracy of the results was examined using independent data set. It is concluded in this study that MODIS data is appropriate in mapping water vapor content as a suitable alternative to meteorological stations measurement data. In this paper, an existing operational algorithm is used to retrieve total atmospheric water vapor content from MODIS data using an LST independent approach. This paper offers a radiance based algorithm for retrieving total atmospheric water vapor content (W) using MODIS radiance data. As a new approach, AIRS data are taken as the reference data to calculate the coefficients of the equation of the method. AIRS is a facility instrument whose goal is to support climate research and improved weather forecasting. The AIRS instrument measures the distribution of water vapor in the atmosphere in three dimensional, globally, every day.