Preprints
https://doi.org/10.5194/tc-2021-131
https://doi.org/10.5194/tc-2021-131

  17 May 2021

17 May 2021

Review status: this preprint is currently under review for the journal TC.

Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine

YoungHyun Koo1, Hongjie Xie1, Stephen F. Ackley1, Alberto M. Mestas-Nuñez1, Grant J. Macdonald1, and Chang-Uk Hyun2 YoungHyun Koo et al.
  • 1NASA MIRO Center for Advanced Measurements in Extreme Environments (CAMEE), University of Texas at San Antonio, San Antonio, TX 78249, USA
  • 2Department of Energy and Mineral Resources Engineering, Dong-A University, Busan 49315, Republic of Korea

Abstract. Sentinel-1 C-band synthetic aperture radar (SAR) images can be used to observe the drift of icebergs over the Southern Ocean with around 1–3 days of temporal resolution and 10–40 m of spatial resolution. The Google Earth Engine (GEE) cloud-based platform allows processing of a large quantity of Sentinel-1 images, saving time and computational resources. In this study, we process Sentinel-1 data via GEE to detect and track the drift of iceberg B43 during its lifespan of 3 years (2017–2020) in the Southern Ocean. First, to detect all candidate icebergs in Sentinel-1 images, we employ an object-based image segmentation (simple non-iterative clustering – SNIC) and a traditional backscatter threshold method. Next, we automatically choose and trace the location of the target iceberg by comparing the centroid distance histograms (CDHs) of all detected icebergs in subsequent days with the CDH of the reference target iceberg. Using this approach, we successfully track the iceberg B43 from the Amundsen Sea to the Ross Sea, and examine its changes in area, speed, and direction. Three periods with sudden losses of area (i.e. split-offs) coincide with periods of low sea ice concentration, warm air temperature, and high waves. This implies that these variables may be related to mechanisms causing the split-off of the iceberg. Since the iceberg is generally surrounded by compacted sea ice, its drift correlates in part with sea ice motion and wind velocity. Given that the bulk of the iceberg is under water (~30–60 m freeboard and ~150–400 m thickness), its motion is predominantly driven by the westward-flowing Antarctic Coastal Current (ACoC) which dominates the circulation of the region. Considering the complexity of modeling icebergs, there is a demand for a large iceberg database to better understand the behavior of icebergs and their interactions with surrounding environments. The GEE-based semi-automated iceberg tracking method presented here can be used for this purpose.

YoungHyun Koo et al.

Status: open (until 12 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2021-131', David G. Long, 27 May 2021 reply
    • RC1: 'CC1 again as RC', David G. Long, 03 Jun 2021 reply
      • AC1: 'Reply on RC1', YoungHyun Koo, 03 Jun 2021 reply
  • RC2: 'Comment on tc-2021-131', Anonymous Referee #2, 03 Jun 2021 reply

YoungHyun Koo et al.

YoungHyun Koo et al.

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Short summary
This study demonstrates for the first time the potential of Sentinel-1 synthetic aperture radar (SAR) images and Google Earth Engine (GEE) cloud-computing platform for semi-automated tracking of area changes and movements of iceberg B43. Our novel GEE-based iceberg tracking can be used to construct a large iceberg database for a better understanding of the behavior of icebergs and their interactions with surrounding environments.