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This proposal addresses the ROSES-2008 A.19 call for the Earth Science Applications Feasibility Study. We propose an 18-month investigation for 1.3.1 Agriculture and 1.3.2 Air Quality applications. This proposal capitalizes on our progress on the MODIS data processing algorithms developed at NASA?s Terrestrial Ecology Program under funding of Dr. D. Wickland. We have developed a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS. Using a time series of measurements and an image-based rather than pixel-based processing, it performs simultaneous retrievals of atmospheric aerosols and surface spectral bi-directional reflectance/albedo without empirical assumptions typical of current operational algorithms. The new algorithm is generic and works over different surface types including bright deserts. The aerosol retrievals are performed at high resolution of 1km (compared to 10 km for MODIS or 17.6 km for MISR). This p roduct offers a more precise identification and characterization of aerosol sources at local and regional scales. MAIAC has an internal cloud mask (CM) and a land-water-snow dynamic classification which allows MAIAC aerosol retrievals and atmospheric correction to flexibly adapt processing path as a function of a current surface state which may change due to flooding, snowfall/ablation, seasonality etc. At present, MAIAC is undergoing extensive regional and global testing. Validation against AERONET measurements shows a good quality of aerosol retrievals in difficult cases, such as large urban areas with high and variable pollution levels. This project proposes to evaluate the feasibility of MAIAC products to augment current operational atmosphere and land data and applications of the USDA Forest Service and US Navy. The USDA Forest Service can potentially leverage MAIAC products to enhance current MODIS-based decision support systems for broadscale resource monitoring and assessment. Independently, MAIAC aerosol retrievals over desert regions will be evaluated for use in the Navy Variational Analysis Data Assimilation System (NAVDAS).