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A Semiautomatic Method for Predicting Subglacial Dry and Wet Zones Through Identifying Dry–Wet Transitions

S. Lang,Mingzhu Yang,X. Cui,Lin Li,Yiheng Cai,Xiaojun Liu,Jingxue Guo,Bo Sun,Martin Siegert

Published 2022 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

In the past decades, radio-echo sounding (RES) data have been used to predict basal dry–wet distributions in glaciated regions through manual inspection of the records. Extending such work, we propose a semiautomatic method for predicting such distributions. The method improves previous work in two ways: 1) subglacial water bodies are taken as a reference to correct the thresholds of dry and wet beds’ identification at a regional scale and 2) five distinct features are defined and used to automatically identify the dry–wet transition, allowing a classification model based on a support vector machine. To demonstrate its effectiveness, the method is applied to airborne RES data collected in recent years over Princess Elizabeth Land in East Antarctica. A comparative analysis of the new versus the previous method was carried out in the Ridge B region of East Antarctica and at the Thwaites Glacier region of West Antarctica. The results show that the method can obtain more accurate subglacial dry–wet distribution results with larger coverage and has the potential to determine dry–wet transitions at a continental scale if applied to the full set of known Antarctic RES data.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    IEEE Transactions on Geoscience and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Geology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/TGRS.2022.3225628
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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