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Accurate atmospheric correction is an important preprocessing step for studies of multi-temporal land-cover mapping using optical satellite data. Model-based surface reflectance predictions (e.g. 6S – Second Simulation of Satellite Signal in the Solar Spectrum) are highly dependent on the adjustment of aerosol optical thickness (AOT) data. For regions with no or insufficient spatial and temporal coverage of meteorological ground measurements, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived AOT data are a valuable alternative, especially with regard to the dynamics of atmospheric conditions. In this study, atmospheric correction strategies were assessed based on the change in standard deviation (σ) compared to the raw data and also by machine learning land-cover classification accuracies. For three Landsat 8 OLI (acquired in 2013) and two RapidEye (acquired in 2010 and 2014) scenes, seven different correction strategies were tested over an agricultural area in southeast Ireland. Visibility calculated from daily spatial averaged TERRA-MODIS estimates (1° × 1° Aerosol Product) served as input for the atmospheric correction. In almost all cases the standard deviation of the raw data is reduced after incorporation of terrain correction, compared to the atmospheric-corrected data. ATCOR®-IDL-based correction decreases the standard deviation almost consistently (ranging from −0.3 to −26.7). The 6S implementation in GRASS GIS showed a tendency of increasing the variation in the data, especially for the RapidEye data. No major differences in overall accuracies (OAs) and kappa values were observed between the three machine learning classification approaches. The results indicate that the ATCOR®-IDL-based correction and MODIS parameterization methods are able to decrease the standard deviation and are therefore an appropriate approach to approximate the top-of-canopy reflectance.
