Evaluating Modified Atmospheric Correction Methods for Landsat Imagery: Image-Based and Model-Based Calibration Methods
To increase the accuracy of remotely sensed data for agricultural forecasting, pixel values must be corrected for atmospheric effects and converted to spectral reflectance. The objective of this research was to compare two atmospheric correction methods of Landsat imagery under a range
of atmospheric conditions. Ground-based dark-object subtraction (GDOS) is an image-based calibration method that used in situ ground data that the dark-object subtraction (DOS) method did not use, whereas atmospheric calibration (AC) is a model-based calibration method that required a standard
atmospheric profile refined with the use of in situ atmospheric data. GDOS and AC methods improved the reflectance values and had relationships with measured bands, which were approximately 1 to 1 in all bands. However, the GDOS generally had lower root-mean-square errors (RMSE) than AC. Data
from this study suggest that at the present time the GDOS method may be more accurate than the AC method.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Geographic Information Science Center of Excellence, South Dakota State University, Brookings, South Dakota, USA
Plant Science Department, South Dakota State University, Brookings, South Dakota, USA
Physics Department, South Dakota State University, Brookings, South Dakota, USA
Engineering Resource Center, South Dakota State University, Brookings, South Dakota, USA
Publication date: May 1, 2008