A six-year circum-Antarctic icebergs dataset (2018–2023)

Abstract. The distribution of Antarctic icebergs is crucial for understanding their impact on the Southern Ocean's atmosphere and physical environment, as well as their role in global climate change. Recent advancements in iceberg databases, based on remote sensing imagery and altimetry data, have led to products like the BYU/NIC iceberg database, the Altiberg database, and high-resolution SAR-based iceberg distribution data. However, no unified database exists that integrates various iceberg scales and covers the entire Southern Ocean. Our research presents a comprehensive circum-Antarctic iceberg dataset, developed using Sentinel-1 SAR imagery from the Google Earth Engine (GEE) platform, covering the Southern Ocean south of 55° S. A semi-automated classification method that integrated incremental random forest classification with manual correction was applied to extract icebergs larger than 0.04 km2, resulting in a dataset for each October from 2018 to 2023. The resulting dataset documents the geographic coordinates and geometric attributes of icebergs (area, perimeter, major axis, and minor axis), provides uncertainty estimates for area, and, under a fixed density assumption, employs the Iceberg Size Scaling to derive iceberg mass along with the associated uncertainty bounds. The dataset reveals significant interannual variability in iceberg number and total area. Specifically, the number of icebergs increased from 34 825 in 2018 to approximately 51 420 in 2021, while the total area expanded from 38 668 to 52 276 km2, both corresponding to major ice shelf calving events, followed by a decline in 2022. The annual average total iceberg area is 44 859 ± 4900 km2, and the average mass is 9162 ± 1935 Gt. Validation using test set samples shows that the integrated incremental random forest classification achieves accuracy, recall, and F1-score exceeding 0.90. Comparisons with existing iceberg products (including the BYU/NIC iceberg database and the Altiberg database) indicate a high consistency in spatial distribution, while our dataset demonstrates clear advantages in terms of spatial coverage, iceberg detection scale, and identification capabilities in regions with dense sea ice. This dataset serves as a novel data resource for investigating the impact of Antarctic icebergs on the Southern Ocean, the mass balance of ice sheets, the mechanisms underlying ice shelf collapse, and the response mechanisms of iceberg disintegration to climate change. The iceberg dataset is publicly available at https://doi.org/10.5281/zenodo.17165466 (Liu and Chen, 2025).

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Publication Details

Journal
Earth system science data
Published
2026-01-06
DOI
https://doi.org/10.5194/essd-18-147-2026
Citations
1
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
40.64
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article

A six-year circum-Antarctic icebergs dataset (2018–2023)

Tian Li, Zilong Chen, Zhenfu Guan, Lu Zheng et al.
1 citations
Earth system science data
Cryospheric studies and observations
40.64
article

A six-year circum-Antarctic icebergs dataset (2018–2023)

Tian Li, Zilong Chen, Zhenfu Guan, Lu Zheng, Xuying Liu, Qi Liang, Yan Liu, Jiping Liu, Teng Li, Xiao Cheng
article en
1 citations

Abstract

Abstract. The distribution of Antarctic icebergs is crucial for understanding their impact on the Southern Ocean's atmosphere and physical environment, as well as their role in global climate change. Recent advancements in iceberg databases, based on remote sensing imagery and altimetry data, have led to products like the BYU/NIC iceberg database, the Altiberg database, and high-resolution SAR-based iceberg distribution data. However, no unified database exists that integrates various iceberg scales and covers the entire Southern Ocean. Our research presents a comprehensive circum-Antarctic iceberg dataset, developed using Sentinel-1 SAR imagery from the Google Earth Engine (GEE) platform, covering the Southern Ocean south of 55° S. A semi-automated classification method that integrated incremental random forest classification with manual correction was applied to extract icebergs larger than 0.04 km2, resulting in a dataset for each October from 2018 to 2023. The resulting dataset documents the geographic coordinates and geometric attributes of icebergs (area, perimeter, major axis, and minor axis), provides uncertainty estimates for area, and, under a fixed density assumption, employs the Iceberg Size Scaling to derive iceberg mass along with the associated uncertainty bounds. The dataset reveals significant interannual variability in iceberg number and total area. Specifically, the number of icebergs increased from 34 825 in 2018 to approximately 51 420 in 2021, while the total area expanded from 38 668 to 52 276 km2, both corresponding to major ice shelf calving events, followed by a decline in 2022. The annual average total iceberg area is 44 859 ± 4900 km2, and the average mass is 9162 ± 1935 Gt. Validation using test set samples shows that the integrated incremental random forest classification achieves accuracy, recall, and F1-score exceeding 0.90. Comparisons with existing iceberg products (including the BYU/NIC iceberg database and the Altiberg database) indicate a high consistency in spatial distribution, while our dataset demonstrates clear advantages in terms of spatial coverage, iceberg detection scale, and identification capabilities in regions with dense sea ice. This dataset serves as a novel data resource for investigating the impact of Antarctic icebergs on the Southern Ocean, the mass balance of ice sheets, the mechanisms underlying ice shelf collapse, and the response mechanisms of iceberg disintegration to climate change. The iceberg dataset is publicly available at https://doi.org/10.5281/zenodo.17165466 (Liu and Chen, 2025).

Earth system science dataVol. 18(1)
Sun Yat-sen University (CN), Shaoxing University (CN), Beijing Normal University (CN), University of Bristol (GB), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), State Key Laboratory of Remote Sensing Science (CN)
Life below water
Openalex Percentile: Top 2%
Cryospheric studies and observations
40.64
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