Design of Air Quality Remote Sensing Monitoring System and Parallel Dense Dark Vegetation Algorithm

Abstract

During Beijing Olympic Games, air quality had been extensively concerned by home and abroad. Remote Sensing satellite can provide greater monitoring scale compared to ground equipment. Based on NASA MODerate resolution Imaging Spectrometer (MODIS) data, using .NET multi-layer architecture, combined with MatlabCOM, ArcEngine and other components, we establish Air Quality Remote Sensing Monitoring System and discuss the roles and design criteria of each component layer. To solve Aerosol Optical Thickness (AOT) retrieval time consuming problem, we use a Master-Worker strategy to parallelize the Dense Dark Vegetation (DDV) algorithm , it is running on a common platform of computer clusters and based on cloud mask to distribute task . Through the acceleration performance analysis, it has shown preferable speedup radio and load balance. Finally, this paper verifies some results from Beijing Olympic atmosphere monitoring project.