Abstract
A new technique is proposed for sea-ice mapping using observations from geostationary satellite over the Caspian Sea. A two end-member linear-mixture approach has been employed. A neural-network-based approach was used to simulate water and ice reflectances for all possible sun–satellite geometries. The ice-mapping technique incorporates an advanced cloud-detection algorithm with adaptive threshold values. The average percentage of cloud reduction because of the daily compositing ranged from 22% to 25%. Daily maps of ice distribution and concentration with minimal cloud coverage were produced for the winter seasons of 2007 and 2008. The retrieved ice distribution demonstrated a good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) images and National Oceanic Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice charts. The obtained correlation coefficients with IMS charts for 2007 and 2008 were 0.92 and 0.83, respectively. The technique has been proposed as one of the candidate ice-mapping techniques for the future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advance Baseline Imager (ABI) instrument.
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