| Atmospheric aerosol particles play a complex role in optical remote sensing. Their absorption and diffusion characteristics alter the radiation reaching the sensor. Atmospheric aerosol optical depth is an indicator of air pollution. Aerosol retrieval using remotely sensed dada over land remains a difficult task because the solar light reflected by the Earth-Atmospheric system mainly comes from ground surface. By using MODIS data and ASTER, we study the methods of Multi-spatial Scale Aerosol Distribution Monitoring for Beijing Area.On the basis of the SYNTAM model, we proposed the advance SYNTAM model-SYNTAMII. SYNTAMII model considers the atmosphere absorption, includingozone absorption, mix-air absorption and water vapor absorption. Comparison to the AERONET 2.0 data, the derived aerosol optical depth by SYNTAMII model is more accurate than SYNTAM model. By using MODIS data, a new dark target algorithm is proposed. The algorithm doesn't need assume the aerosol type. It showed excellent competence at the aerosol distribution and aerosol properties retrieval, which is, however, restrictedly used for lower reflectance ground surface.According to the characteristic of ASTER data, we proposed the aerosol optical retrieval methods for high spatial resolution remote sensing data. The methods marked with "ASYNTAM". By ASNYTAM, surface reflectance and aerosol optical thickness can be simultaneously retrieved over various ground types including higher reflective surface such as urban area. The preliminary aerosol retrievals using ASTER data by ASYNTAM are carried out over Beijing city. Preliminary validation result comparing with AERONET data shows good accuracy and promising potential, further validations work is ongoing.We have developed a grid-based remote sensing data processing system for Aerosol Remote Sensing. In the system, the end users submit their application requirements to the Grid resource broker which then discovers suitable resources by...
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