Atmospheric Aerosol Optical Depth Remote Sensing Inversion Algorithm And Remote Sensing Image Atmospheric Correction

Aerosol is an obstruction difficult to be overstepped between ground information and remote sensing image. It is not only an important parameter used in global climate research and air pollution, but also the necessary parameter in quantitative remote sensing field. The aim of this thesis is to find a new aerosol optical depth (AOD) inversion algorithm in sparse vegetation area and improve the atmospheric correction to do some contribution to the quantitative remote sensing.The thesis includes five chapters as follows:The background knowledge, researching significance and main researching interests will be introduced in the first chapter.The second chapter will introduce the simulation and analysis of atmospheric radiation transferring model. At first, an short introduction to the atmospheric radiation transferring model is given, then the factors impacting the aerosol optical depth inversion have been analyzed one by one, which include apparent reflectance, surface reflectance, geometry condition, atmospheric water vapor content and aerosol modes.The third one will show you the aerosol optical inversion algorithm in sparse vegetation region. This algorithm enters on surface spectral model in sparse vegetation area, in the help of spectral library, and succeeds in getting the inversion result of aerosol optical depth. Finally, we validate the algorithm by the remote sensing images and aerosol optical depth metered on ground surface. By aggregating this algorithm with the present algorithms, we develop an aerosol optical inversion module, which now is used in the remote sensing inversion system software developed by our research team.The forth one gives a method to correct the atmospheric effects by taking the AOD and water vapor content inversion results into the atmospheric radiation transferring model. In this chapter we will give you the idea to construct the Look-Up-Table(LUT). After the atmospheric correction of the remote sensing images we validate the results by analyzing the MODIS and ETM+ data retrieved during the field experiment in Shunyi district, Beijing.What the fifth chapter talks about is summarizing the whole thesis and putting forward the problems at hand.