|Bryant, R. - UNIVERSITY OF ARIZONA|
|Holifield Collins, Chandra|
|Mcelroy, S. - UNIVERSITY OF ARIZONA|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 1, 2002
Publication Date: January 1, 2003
Citation: Moran, M.S., Bryant, R., Holifield Collins, C.D., Mcelroy, S. 2003. A refined empirical line approach for retrieving surface reflectance from EO-1 ali images. Rem. Sens. Env. 78:71-82. Interpretive Summary: A new satellite sensor, called Earth Observation (EO-1), was recently launched to test new technology that could be used for imaging Earth land and oceans. To assess the quality of EO-1 images for monitoring Earth conditions, the images need to be normalized to a surface parameter that is unaffected by sensor signal drift and changing atmospheric conditions. An approach has been developed to convert the sensor signal to values of surface reflectance which indicate changes in soil and plant conditions over time. This approach can be used operationally for future and archived images because it is based only on general atmospheric theory, minimal one-time ground measurements, and information derived from the image itself. This normalization approach has proven accurate for a set of six EO-1 images in Arizona. With this simple and accurate means to convert the EO-1 signal to surface reflectance, it will be possible to assess the new EO-1 sensor technology for Earth observation.
Technical Abstract: The refined empirical line (REL) approach was used to convert the EO-1 ALI sensor digital number (dn) to surface spectral reflectance (¿¿). The dn-to-¿¿ relation was derived from a bright target of known reflectance in the image, and the modeled estimates of the image dn at ¿¿=0. The mean absolute percent difference (¿%) between ¿¿ retrieved from ALI using the REL approach and ground-measured ¿¿ for 15 targets on 6 dates were 42%, 6% and 13% in the ALI visible, NIR and SWIR spectral bands, respectively. The ¿% for ¿¿ retrieved from ALI without any atmospheric correction were 155%, 9% and 10% for visible, NIR and SWIR bands, respectively. For the clear, dry atmospheric conditions in Arizona, REL correction was most crucial for the dark targets in the visible bands. Given the published values of ALI dn for ¿¿=0, the REL offers a simple approach for retrieving reflectance from multiple ALI images for temporal surface analysis.