Image-based atmospheric correction of QuickBird imagery of Minnesota cropland

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 (rho(lambda)) 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 rho(lambda) in the visible bands, but underestimated rho(lambda) in the near infrared (NIR) band. The DOS-COST model was most effective for the visible bands and produced rho(lambda) with the root mean square errors (RMSE) of less than 0.01. However, retrieved rho(lambda) 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-lambda(theta)) 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-lambda(theta) 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-lambda(theta), the accuracy of NIR band rho(lambda) was significantly improved where the RMSE and MRD were 0.06 and 0.03%, respectively, and the overall accuracy of rho(lambda) was acceptable for agricultural applications. (C) 2005 Elsevier Inc. All rights reserved.