The U.S. Environmental Protection Agency (EPA) is responsible for protecting public health through development and enforcement of environmental standards. EPA's strategic plan defines 'Clean Air and Global Climate Change' as one of their five primary goals. It is pursuing this goal with the help of an air quality monitoring network and an array of analytical and physical models, organized within a computational framework referred to here as the Air Quality Management Decision Support System (AQMDSS). Realizing the central role that the AQMDSS plays in air quality management decisions of great socio-economic importance, EPA, in partnership with other federal agencies, maintains an aggressive program towards continued improvement of the DSS. The characterization of the dynamic properties of the land surface plays an important role in determining the accuracy of air quality models. In the current AQMDSS, many dynamic features of the land surface are represented in very simplistic ways, with little seasonal variability or observational data. Data sets with improved spatial and temporal resolutions, such as those from NASA Earth Science mission sensors, have the potential to improve AQMDSS performance. The overarching goal of the proposed project, which addresses goals of NASA's Air Quality application of national priority, is to demonstrate the feasibility and to facilitate and broaden the use of remotely-sensed observations as an input to the AQMDSS, thereby supporting NASA's and EPA's goals. The expected improvements in the AQMDSS will lead to better air quality forecasts and facilitate decision-making for regulatory agencies and policy makers. This study also addresses two Air Quality national priority objectives: support EPA-developed tools for states and locals on regional haze, and improve land cover characterization in air quality models. We envision this project to demonstrate the usefulness of satellite observations, both as input to atmospheric models and for validation of their results.