Abstract
The strategy of both the remote sensing of the atmospheric aerosols and atmospheric correction for quantitative retrieval of the earth surface reflectance with space-borne sensor in solar radiation waveband, i.e., from visible, near infrared to short-wave infrared waveband is discussed in this paper. This strategy is based on the quantitative analysis of the relationship between the observed parameters (apparent spectral reflectance) by space-borne sensors and the intervening atmosphere and earth surfaces, by using radiative transfer model calculation. At first, we derived an explicit parameterized expression which relates the apparent spectral reflectance with the atmospheric spectral optical depth and surface spectral reflectance, then, we suggested an iterative algorithm for simultaneous remote sensing of both atmospheric spectral optical depth and surface spectral optical depth and surface spectral reflectance. In this algorithm some empirical relationships for atmospheric spectral optical depths and surface spectral reflectance in different wavelengths, respectively, are assumed. The above algorithm can be applied to the remote sensing of relatively homogeneous surface. Second, for inhomogeneous surface, we developed an algorithm of the atmospheric spread function which is the basis of the estimation of the adjacency effect for high resolution surface remote sensing, such as small pixel size as TM, SPOT images. Based on above algorithms, we may execute the atmospheric correction for both low and high resolution remote sensing and also, in same approximation for spectral relationship of both atmospheric spectral optical depth and surface spectral reflectance respectively, simultaneous remote sensing of the atmospheric aerosols and surface reflectance can be operated.

Key words: atmospheric correction atmospheric aerosol; adjacency effect; optical remote sensing.

1.Introduction
Atmospheric correction plays an important role in quantitative remote sensing of surface parameters from space-borne (satellite) and air-borne optical remote sensing from visible, near infrared, to short-wave infrared waveband. For surfaces with low reflectance such as water surface, atmospheric scattering contributes the major part of the reflected solar radiance to the sensor than the radiance reflected by water surface. Thus atmospheric correction is consisted of the major step for ocean optical remote sensing. The another effect of atmosphere scattering is called adjacency effect, which is the contribution of background reflection to the sensor's field of view in certain target pixel through atmospheric scattering. This adjacency effect plays important role in high resolution remote sensing of inhomogeneous surfaces with big contract within neighbouring pixel.

There have been a lot of research works towards appropriate atmospheric correction. In principal, if one knows the atmospheric condition, in particular atmospheric aerosol information such as optical depth, aerosol type (which characherizes its size distribution and reflective indices), surface can be retrieved by radiative transfer model calculation for horizontal homogeneous atmosphere with homogeneous surface. For inhomogeneous surfaces, one needs to reduce the adjacency effect, which is related to radiative transfer model for horizontally homogeneous atmospheric and inhomogeneous surfaces.

In fact, in many cases, information for atmospheric condition is not available. On the contrary, atmospheric aerosol (optical depth and other parameters) as also an important task of the optical remote sensing as it plays significant role in cooling (in general) the atmosphere which may compensate the world global warning by increasing emission of trace gases. In general, dynamic monitoring of both the atmospheres which may compensate the world global warning by increasing emission of trace gases. In general, dynamic monitoring of both the atmospheric (aerosols) as well as earth surface becomes an urgent task for both earth science research and various applications. In this sense, we need to develop retrieval algorithms for simultaneous remote sensing of atmospheric aerosol's optical depth and surface reflectance.

One of the strategies to implement such as task is to use known empirical (by some other research) spectral dependence of atmospheric scattering and surface reflectance. Some investigators use dark (A(l)-0) pixel (in certain wavelength l) to derived atmospheric aerosol's optical depth (ta(l)), then by using empirical relationship to obtain (ta(l) in other wavelengths, which may be used as the basis of atmospheric correction for other non-dark surface pixels. In this approach, the basic requirement is the existence of dark pixels with enough size within remote sensing image. Inaddition, it is assumed that the distribution of atmospheric aerosols are horizontally homogeneous and empirical relationship of (ta(l) is appropriate. These last two assumptions are generally valid for finite areas. As for the correction of adjacency effect, there have been detailed discussions with preliminary success. [e.g.1,2]

Recently there have been a series of so called imaging spectrometer and multi-wavelength imagery sensor on board different earth observing satellites such as OCTS, AVHRR, POLDER on ADEOS, LANSAT/TM series, SPOT/HRV series. MODIS on EOS AM-1 etc. Data resources of spectral remote sensing are rich enough for us to search for more effective retrieval algorithm(s). In this paper, a strategy is suggested for both atmospheric correction and remote sensing of atmospheric aerosol optical depth. The strategy is based on radiative transfer model calculation. The basic procedure is at first to separate contribution then to deviate the target direct reflection from adjacency effect, procedures for these tow steps are discussed.