Atmospheric Correction for Multispectral Satellite Imagery: An introductory review

Remote Sensing provides valuable information about objects or remote areas, either using active sensors (i.e. RADAR and LiDAR) or passive (i.e. multispectral or hyperspectral imagery) using satellite retrieved images. Quality of remote sensing satellite images is frequently degraded due to several reasons, in par-ticular earth atmosphere. Satellite imagery atmospheric correction (AC) is an active research area in the remote sensing community, the goal of this pre-processing method is to retrieve the surface reflectance (i.e. land or water) from the at-satellite radiance, usually obtained as raw binary numbers or digital numbers. Every image taken by satellite sensors is affected by atmospheric effects (i.e. scattering and absorption), so it is necessary to compensate or considering these effects to be able to remove them from the image before proceeding to extract useful characteristics from the image itself in a second feature extraction stage. This article provides a review of some of the most used Atmospheric Correction methods currently available from the simplest ones such as image-based methods to the state-of-the-art methods, based on complex Radiative Transfer Models, described in the literature to provide an entry point for engineers, practitioners and researchers interested on Remote Sensing from satellite imagery.