Image-based atmospheric correction of QuickBird imagery of Minnesota cropland

Abstract

High spatial resolution QuickBird satellite data have provided new opportunities for remote sensing applications in agriculture. In this study, image-based algorithms for atmospheric correction were evaluated on QuickBird imagery for retrieving surface reflectance ( ρ λ ) of corn and potato canopies in Minnesota. The algorithms included the dark object subtraction technique (DOS), the cosine approximation model (COST), and the apparent reflectance model (AR). The comparison with ground-based measurements of canopy reflectance during a 3-year field campaign indicated that the AR model generally overestimated ρ λ in the visible bands, but underestimated ρ λ in the near infrared (NIR) band. The DOS–COST model was most effective for the visible bands and produced ρ λ with the root mean square errors (RMSE) of less than 0.01. However, retrieved ρ λ in the NIR band were more than 20% (mean relative difference or MRD) lower than ground measurements and the RMSE was as high as 0.16. The evaluation of the COST model showed that atmospheric transmittance ( T λ θ ) was substantially overestimated on humid days, particularly for the NIR band because of the undercorrection of water vapor absorption. Alternatively, a contour map was developed to interpolate appropriate T λ θ for the NIR band for clear days under average atmospheric aerosol conditions and as a function of precipitable water content and solar zenith angle or satellite view angle. With the interpolated T λ θ , the accuracy of NIR band ρ λ was significantly improved where the RMSE and MRD were 0.06 and 0.03%, respectively, and the overall accuracy of ρ λ was acceptable for agricultural applications.