Abstract

Abstract 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 Airborne Topographic Mapper (ATM) elevations. However, the shaded areas of the Digital Mapping System (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 and to correctly determine SSHs by combining DMS images, ATM L1B's apparent reflectivity and statistical discrimination. Dynamic pixel intensity thresholds are established to classify leads under different solar illuminations. This automated approach is then validated by manual selection in detecting SSHs. The high agreement of SSHs from this automated approach with those from manual selection indicates the reliability and usefulness of this approach. Within a 45-km section of one ATM L1B file, SSH demonstrates a linear gradient, which is applied to derive SSHs where there are no leads. The resulted SSHs are then used to compute total freeboard and ice thickness. This automated approach is also tried to retrieve SSH and then compute the total freeboard on one entire IceBridge sea ice flight each in Arctic and Antarctica.

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