Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm

A scene-average automated cloud-cover assessment (ACCA) algorithm has been used for the Landsat-7 Enhanced Thematic Mapper Plus (ETM� ) mission since its launch by NASA in 1999. ACCA assists in scheduling and confirming the acquisition of global “cloud-free” imagery for the U.S. archive. This paper documents the operational ACCA algorithm and validates its performance to a standard error of � 5 percent. Visual assessment of clouds in three-band browse imagery were used for comparison to the five-band ACCA scores from a stratified sample of 212 ETM� 2001 scenes. This comparison of independent cloud-cover estimators produced a 1:1 correlation with no offset. The largest commission errors were at high altitudes or at low solar illumination where snow was misclassified as clouds. The largest omission errors were associated with undetected optically thin cirrus clouds over water. There were no statistically significant systematic errors in ACCA scores analyzed by latitude, seasonality, or solar elevation angle. Enhancements for additional spectral bands, per-pixel masks, land/water boundaries, topography, shadows, multidate and multi-sensor imagery were identified for possible use in future ACCA algorithms.