首页 > , Vol. , Issue () : 1993-2002
The multispectral camera carried by the gaofen4(GF-4) satellite was featured by high spatial resolution and high frequency observations. It played an important role in atmospheric aerosol monitoring. The most two mature satellite aerosol optical depth(AOD) retrieval algorithms were dark target algorithm(DT) and deep blue algorithm(DB).While the former one was limited in the low reflectivity area and 2.1m wavelength band. An AOD inversion algorithm was developed in this work based on the enhanced surface reflectance library support algorithm by using GF-4 data. The key question of AOD inversion were the estimate of surface reflectivity and the assumption of aerosol types. The MOD09-CMA data was applied to perform atmospheric correction with Second Simulation of Satellite Signal in the Solar Spectrum Vector(6SV) model for GF-4 data. To make more accurate, it was specified that no treatment be performed in the condition of cloud and high aerosol load. A quarter-period reflectance library for the GF-4 data was synthesized using the percentage minimum mean method. The surface reflectance library was reanalyzed to obtain the relationship model between NDVI and red, blue reflectivity. We used NDVI to determine surface reflectance when NDVI was greater than 0.2 and used static surface reflectance library to determine surface reflectance when NDVI was less than 0.2.The aerosol type parameters were determined by the MODIS global spatial-temporal distribution map in terms of aerosol types. The algorithm was validated against two datasets: Aeronet dataset and MOD04 products. Statistical analysis of the validation results was based on the linear regression model using the goodness-of-fit indicators: correlation coefficient(R), root mean squared error(),and expected error(). The accurate values in this works were derived with R:0.93, RMSE:0.21, and the percentage of falling within was more than 79%.The was better than the MODIS-DT product, which was slightly worse than the MODIS-DB product. Compared with the MODIS-DB algorithm, our algorithm fell more in the expected error line. The 6SV model was used to simulate the error.It was found that the pixel-by-pixel imaging angle can effectively reduce the error. The surface reflectivity library error was minimum in summer. The change in solar angle was suggested to be considered to build the surface reflectivity library in other seasons. In the mean time, the single aerosol model assumption was one cause of errors as well.