Aerosol optical depth retrieval based on land surface spectra modeling

The radiation from the sun to satellites in the sky is always modulated twice by atmosphere. Aerosol is one of the most active components in atmosphere and it usually contaminates the remotely sensed imagery severely so that most remotely sensed imagery cannot be used without atmospheric effect correction. However, the remotely sensed imagery is always the coupling of atmosphere and land surface information, which makes it very difficult to decouple the remotely sensed information to retrieve accurate atmospheric information and land surface information respectively from remotely sensed imagery alone. Based on the physical mechanism of radiative transfer model, many researchers assumed the specific surface condition, such as dark objects and invariant objects, so the atmospheric information like aerosol optical depth (AOD) can be decoupled from remotely sensed information. Since these assumptions are just for some specific surface conditions, the atmospheric information of many other surface conditions, such as sparsely vegetated areas and snow covered areas, are not available. Therefore, we propose an algorithm of aerosol information retrieval for agricultural areas based on land surface modeling. By analyzing the soil, the leaf and the canopy spectra, we selected a set of models to calculate the surface reflectance of different land surfaces, which include bare soil areas, sparsely vegetated areas and densely vegetated areas. In addition, the water vapor effect of 2.1μm spectrum band has been considered in this paper for retrieving more accurate canopy and soil reflectance. Finally, North China Plain is selected as experimental area for aerosol information retrieval through the proposed algorithm, and the AOD value measured by sunphotometer is taken as true value to evaluate the algorithm´s accuracy.