Modeling surface albedo coefficient derived from sebal algorithm to estimate the level of snow cover (case study: chegeni basin(

This study was conducted in chegeni mountainous basin, with an area of 1836 Km2 .It is sub-basin of Doab Kashkan, that located in the north of the Lorestan province So that Landsat 7 Satellite ETM imagery sensor, selected for studies and correction of missing lines was conducted and albedo Surface amount, calculated by using Sebal algorithms. Since the except snow, two phenomena yellow and white sand and water have greater than 0.3 albedo, then the snow cover map were provided for α > 0.3, α > 0.35, α > 0.4, α > 0.45 , α > 0.5 and α > 0.55 albedos. To extract snow cover with sufficient precision, with this assuming, that snow cover is unknown, unmonitored classification was conducted for the albedo, then with applying of monitored classification on primary corrected image. The snow cover amount, was estimated, and kappa coefficient selected for evaluate the results of monitored classification and unmonitored classification for the mentioned albedo. The results showed that kappa coefficient for α > 0.45 albedo, has highest value 0.85. So α > 0.45 albedo can extract snow cover to an acceptable level for us.