The importance of accurate visibility parameterization during atmospheric correction: Impact on boreal forest classification
Observation of the Earth's surface from spaceborne platforms is complicated by the various layers of the Earth's atmosphere that reflect, scatter, and attenuate electromagnetic radiation passing through them, thus influencing (upward or downward) the signal strength recorded at the sensor relative to the true quantity of radiance reflected from the observed surfaces. The magnitude and spatial distribution of atmospheric effects is non-stationary and will vary due to numerous factors. While the effect of these factors cannot be eliminated completely, the understanding of radiative transfer physics, atmospheric states, and electromagnetic wave propagation permits much of these effects to be appropriately modelled and minimized. Such corrections for atmospheric effects permit the extraction of more accurate physical properties of surface materials and states from imagery than if atmospheric effects were ignored. Modelling of atmospheric effects with radiative transfer models, however, requires appropriate parameterization. We explore the sensitivity of the important visibility parameter of the popular Atmospheric and Topographic Correction (ATCOR) model for atmospheric correction over boreal forest land cover. Further, we provide a methodology for estimating reasonable values for the visibility parameter in the event that this information is not readily available. Our sensitivity analyses, performed on Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery from northern Québec and Ontario, rely on both incremental adjustments to the visibility parameter to assess the degree of atmospheric effect removal and the cascading effect on land-cover classification. We build confidence around our measures using a spatial bootstrapping analysis within each of the two images we analyse. Our analysis demonstrates that exceeding a magnitude of error of approximately 2 km in estimating a visibility parameter values can decrease classification accuracy by nearly 10%. Our assessments of the spatial structure of the mitigated atmospheric component within our scenes, testing for complete spatial randomness, clustering of like values, or evenness in value distributions are inconclusive, but hint towards more clustered results with greater magnitudes of parameterization error.
|Journal||International Journal of Remote Sensing|
Remmel, T.K. (Tarmo K.), & Mitchell, S. (2013). The importance of accurate visibility parameterization during atmospheric correction: Impact on boreal forest classification. International Journal of Remote Sensing, 34(14), 5213–5227. doi:10.1080/01431161.2013.788263