Atmospheric Correction

Seen from space, the earth exhibits various shades of blue with green vegetation, tan deserts, and white snow and ice. In nonvisible parts of the spectrum--infrared, for instance--other variations of reflected radiation allow researchers to distinguish types of crops and patterns of urban development. The varied features of the earth's surface each reflect sunlight and other wavelengths of solar radiation in a highly specific way. This principle provides the foundation for the science of satellite-based remote sensing, which, drawing on computational techniques for the processing of signals and images, allows a great variety of automated remote mapping and map-analyzing techniques.

A vexing problem confronting remote-sensing researchers, however, is that the reflected radiation observed from remote locations are significantly contaminated by the atmosphere. (Astronomers have faced this problem for hundreds of years, though they are generally looking in the opposite direction.) Atmospheric particles that get in the way include the molecules of atmospheric gases, and the small particles and droplets called aerosols, such as smoke, dust, mist, and cloud droplets. These aerosols and molecules scatter and absorb solar photons reflected by the surface in such a way that only some of the surface radiation can be detected by a sensor. Even before reflection, atmospheric particles scatter some of the sunlight into the sensor's field of view directly, resulting in a radiation that does not contain any surface information at all.

The combined atmospheric effects due to scattering and absorption are wavelength-dependent. They also vary in time and space, and depend on the surface reflectance and its spatial variation. For data from band 1 of the Thematic Mapper instruments aboard the Landsat satellites, it is likely that the aerosol contribution is of the order of 50% even for relatively clear sky conditions. Although qualitative evaluation of these remotely sensed data has been very useful, developing the correct quantitative links between satellite imagery and surface characteristics greatly depends on removing the atmospheric effects. It has been demonstrated that this so-called atmospheric correction (sic) can significantly improve the accuracy of image classification.

Atmospheric correction algorithms basically consist of two major steps. First, the optical characteristics of the atmosphere are either estimated empirically by using special features of the ground surface or by directly measuring the atmospheric constituents, or estimated theoretically by models. Various quantities related to the atmospheric correction can then be computed by radiative transfer algorithms, given the atmospheric optical properties. Second, the remotely sensed imagery is corrected by inversion procedures that derive the surface reflectance.

The Coastal Marsh Project uses an algorithm developed by Dr. Shunlin Liang to correct any atmospheric distortions that occur in satellite scenes. For more information:

Fast Algorithms for Removing Atmospheric Effects from Satellite Images by Hassan Fallah-Adl, Joseph JaJa, Shunlin Liang, and John Townshend (University of Maryland) and Yoram J. Kaufman (NASA Goddard Space Flight Center) IEEE Computational Science and Engineering: Summer 1996