Abstract
One of the classical weaknesses of atmospheric correction algorithms of remotely sensed data is their statistically limited validation data sets. Validation procedures are generally carried out over a limited sparse ground based data sets. To overcome this constraint, the authors developed an approach using the multi-altitude regression more appropriate to the validation needs of high altitude or satellite sensors. The latter has been used to validate a single-altitude AOD (Aerosol Optical Depth) inversion algorithm which uses ARVI (Atmospherically Resistant Vegetation Index) criterion to select DDV (Dense Dark Vegetation) pixels in the Canadian boreal forest image acquired by the CASI (Compact Airborne Spectrographic Imager). The multi-altitude regression procedure permits the extraction of AOD images which are reasonably independent of the surface BRF (Bidirectional Reflectance Factor) required as input to the DDV-based AOD inversion algorithm. This is a fundamental requirement for the validation of DDV-based algorithm since the basic weakness of this algorithm is its sensitivity to surface BRF variations. The results indicate that the multi-altitude regression procedure is an effective tool for validating DDV inversion algorithms.
Published Version
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