Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research

Abstract

Atmospheric correction is an important preprocessing step required in many remote sensing applications. The authors are engaged in the project 'Human Dimensions of Amazonia: Forest Regeneration and Landscape Structure' in NASA/INPE's Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) programme. This project requires use of corrected Landsat TM data since research foci integrate ground-based data and TM to: (1) measure and model biomass; (2) classify multiple stages of secondary succession; (3) model land cover/land use changes; and (4) derive spectral signatures consistent across different study areas. The 30+ scenes of TM data are historic and lack detailed atmospheric data needed by physically-based atmospheric correction models such as 6S (Second Simulation of the Satellite Signal in the Solar Spectrum). Imagebased DOS models are based on image measurements and explored in this article for application to LBA study areas. Two methods using theoretical spectral radiance and image acquisition date respectively were used to convert TM DN values to at-satellite radiance. Three image-based models were employed using each method to convert at-satellite radiance to surface reflectance. Analyses of these six different image-based models were conducted. The Improved Imagebased DOS was the best technique for correcting atmospheric effects in this LBA research with results similar to those obtained from physically-based approaches.