An Improved Liberal Cloud-Mask for Addressing Snow/Cloud Confusion with MODIS

Abstract The utility of the daily MODIS snow products depends on the ability of the MODIS cloud-masking algorithm to differentiate between snow- and cloud-cover. Although few studies have explored the issue, snow/cloud confusion is a key issue limiting the accuracy of the MODIS snow products. Recent studies from the Southern Hemisphere suggested that snow/cloud confusion limited the utility of the MODIS snow products there. In this study, MODIS snow/cloud confusion over Australia was investigated using an improved liberal cloud-mask in conjunction with a snow-detection algorithm. The performance of the proposed cloud-mask was assessed using high-resolution ASTER imagery and in situ observations. Results indicated that the improved liberal cloud-masking algorithm reduced snow/ cloud confusion, successfully identifying snow-covered pixels that were previously identified as cloudy. The analysis further suggested that scale-related differences in imagery used in the standard MODIS cloud-masking workflow might be the source of some snow/cloud confusion previously reported.