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

• Main Authors: Koo, YoungHyun, Xie, Hongjie, Ackley, Stephen F., Mestas-Nuñez, Alberto M., Macdonald, Grant J., Hyun, Chang-Uk
• Format: Text
• Language: English
• Published: 2021
• Subjects: Amundsen Sea; Antarctic; Ross Sea; Southern Ocean; Antarc*; Iceberg*; Sea ice
• Online Access: https://doi.org/10.5194/tc-2021-131; https://tc.copernicus.org/preprints/tc-2021-131/

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.