[Effects of aerosol optical thickness on the optical remote sensing imaging quality].

In recent years, due to changes in atmospheric environment, atmospheric aerosol affection on optical sensor imaging quality is increasingly considered by the load developed departments. Space-based remote sensing system imaging process, atmospheric aerosol makes optical sensor imaging quality deterioration. Atmospheric medium causing image degradation is mainly forward light scattering effect caused by the aerosol turbid medium. Based on the turbid medium radiation transfer equation, the point spread function models were derived contained aerosol optical properties of atmosphere in order to analyze and evaluate the atmospheric blurring effect on optical sensor imaging system. It was found that atmospheric aerosol medium have effect on not only energy decay of atmospheric transmittance, but also the degradation of image quality due to the scattering effect. Increase of atmospheric aerosol optical thickness makes aerosol scattering intensity enhanced, variation of aerosol optical thickness is also strongly influences the point spread function of the spatial distribution. it is because the degradation of aerosol in spatial domain, which reduces the quality of remote sensing image, in particularly reduction of the sharpness of image. Meanwhile, it would provide a method to optimize and improve simulation of atmospheric chain.

[Modeling continuous scaling of NDVI based on fractal theory].

Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals' relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters' variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI (computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.