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The existence and detection of leads are critical to obtain a local Sea Surface Height(SSH)reference for computing total freeboard and sea ice thickness from NASA's IceBridge ATM elevations.However,the shade on DMS images and the biased ATM elevations impact the correct determination of leads and SSH.This study develops an automated approach to overcome the above challenges to correctly determine SSHs by combining DMS images,ATM L1B's apparent reflectivity and statistic discrimination.We demonstrate the applications of this automated approach in detecting leads and SSHs and in computing total freeboard and sea ice thickness on the selected four sections along a flight track of IceBridge in the Bellingshausen Sea in 2011 and 2012.The high agreement of SSHs from this automated approach with those from manual selection indicates the reliability and usefulness of this approach.The fine SSHs from this automated approach have the potential to work as ground truth to validate those from other methods.Within a 45-km section of one ATM L1B file,SSH demonstrates a linear gradient,which is applied to derive other SSHs where there was no lead,then all SSHs are used to compute total freeboard and ice thickness.The histograms of both total freeboard and ice thickness on the four sections in 2011 and 2012 are uni-modal,tailing off toward the larger values,and are consistent with previous studies.This automated approach can be utilized to process the vast amount of IceBridge DMS images and ATM and other similar data to retrieve the total freeboard and then sea ice thickness,and is of much significance in the sea ice community.