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Image-based atmospheric correction of QuickBird imagery of Minnesota cropland [An article from: Remote Sensing of Environment]
Book Details
Author(s)J. Wu, D. Wang, M.E. Bauer
PublisherElsevier
ISBN / ASINB000RR6S0C
ISBN-13978B000RR6S04
AvailabilityAvailable for download now
Sales Rank14,314,697
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
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 (@r"@l) 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 @r"@l in the visible bands, but underestimated @r"@l in the near infrared (NIR) band. The DOS-COST model was most effective for the visible bands and produced @r"@l with the root mean square errors (RMSE) of less than 0.01. However, retrieved @r"@l 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"@l^@q) 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"@l^@q 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"@l^@q, the accuracy of NIR band @r"@l was significantly improved where the RMSE and MRD were 0.06 and 0.03%, respectively, and the overall accuracy of @r"@l was acceptable for agricultural applications.
Description:
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 (@r"@l) 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 @r"@l in the visible bands, but underestimated @r"@l in the near infrared (NIR) band. The DOS-COST model was most effective for the visible bands and produced @r"@l with the root mean square errors (RMSE) of less than 0.01. However, retrieved @r"@l 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"@l^@q) 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"@l^@q 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"@l^@q, the accuracy of NIR band @r"@l was significantly improved where the RMSE and MRD were 0.06 and 0.03%, respectively, and the overall accuracy of @r"@l was acceptable for agricultural applications.
