Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS

Aerosol scatters solar radiation before it reaches the surface and scatters and absorbs it again after it is reflected from the surface and before it reaches the satellite sensor. The effect is spectrally and spatially dependent. Therefore, atmospheric aerosol (dust, smoke, and air pollution particles) has a significant effect on remote sensing. Correction for the aerosol effect on remote sensing of land areas was never achieved on an operational basis though several case studies were demonstrated. We distinguish between direct correction, in which the aerosol loading is derived from the image itself (or supplied from external sources) followed by correction of the image using an appropriate radiative transfer model, and indirect correction, achieved by defining remote sensing functions that are less dependent on the aerosol loading. To some degree this was already achieved in global remote sensing of vegetation where a composite of several days of the normalized difference vegetation index (NDVI) measurements, choosing the maximal value, was used instead of a single cloud-screened value. The atmospheric resistant vegetation index (ARVI) introduced recently for the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) is the most appropriate example of indirect correction, where the index is defined in such a way that the atmospheric effect in a blue spectral channel cancels to a large degree the atmospheric effect in the red channel in computations of the vegetation index. Atmospheric corrections can also use aerosol climatology or simultaneous measurements with ground-based instrumentation. These aspects of aerosol studies and remote sensing are reviewed in this article. New advances in ground-based instrumentation and future satellite systems (including measurements of polarization) are discussed. In the conclusions a strategy is introduced for a combination of ground-based measurements, with direct and indirect corrections that is implemented for the EOS-MODIS and recommended for other similar platforms. Such strategy, planned in advance, is a first step to face the challenge and take advantage of the opportunities that the remote sensing community will face with the launch of EOS and ADEOS in the next several years.