A multi-tagged SAR ocean image dataset identifying atmospheric boundary layer structure in winter tradewind conditions

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Chen Wang, Justin E. Stopa, Doug Vandemark, Ralph Foster, Alex Ayet, Alexis Mouche, Bertrand Chapron, Peter Sadowski
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Abstract

A dataset of multi-tagged sea surface roughness synthetic aperture radar (SAR) satellite images was established near Barbados from January to June 2016 to 2019. It is an advancement of the Sentinel-1 Wave Mode TenGeoP-SARwv (a labelled SAR imagery dataset of 10 geophysical phenomena from Sentinel-1 wave mode) dataset that targets SAR marine atmospheric boundary layer (MABL) coherent structures. Twelve tags define roll vortices, convective cells, mixed rolls and convective cells, fronts, rain cells, cold pools and low winds. Examples are provided for each signature. The final dataset is comprised of 2100 Sentinel-1 wave mode SAR images acquired at 36 incidence angle over an 8° × 8°region centered at 51° W, 15° N. Each image is tagged with one or multiple phenomena by five experts. This strategy extends the TenGeoP-SARwv by identifying coexisting phenomena within a single SAR image and by the addition of mixed roll/cell states and cold pools. The dataset includes PNG-formatted SAR image files along with two text files containing the file name, the central latitude/longitude, expert tags for each image, and all dataset metadata. There is a high degree of consensus among expert tags. The dataset complements existing hand-labelled ocean SAR image datasets and offers the potential for new deep-learning SAR image classification model developments. Future use is also expected to yield new insights into the tradewind MABL processes such as structure transitions and their relation to the stratification.

Abstract Image

识别冬季贸易风条件下大气边界层结构的多标记合成孔径雷达海洋图像数据集
2016 年至 2019 年 1 月至 6 月,在巴巴多斯附近建立了一个多标签海面粗糙度合成孔径雷达(SAR)卫星图像数据集。它是哨兵-1 波浪模式 TenGeoP-SARwv (哨兵-1 波浪模式 10 种地球物理现象的标注合成孔径雷达图像数据集)数据集的升级版,以合成孔径雷达海洋大气边界层(MABL)相干结构为目标。12 个标签定义了卷涡、对流单元、混合卷和对流单元、锋面、雨单元、冷池和低风。每个特征都提供了示例。最终数据集由以西经 51°、北纬 15°为中心的 8° × 8° 区域内以 36 个入射角采集的 2100 张哨兵-1 波模式合成孔径雷达图像组成。这一策略通过识别单张合成孔径雷达图像中的共存现象以及增加混合滚动/细胞状态和冷池,扩展了 TenGeoP-SARwv。数据集包括 PNG 格式的合成孔径雷达图像文件以及两个文本文件,其中包含文件名、中心经纬度、每幅图像的专家标签以及所有数据集元数据。专家标签之间达成了高度共识。该数据集补充了现有的手工标记海洋合成孔径雷达图像数据集,并为新的深度学习合成孔径雷达图像分类模型的开发提供了潜力。未来使用该数据集还有望对贸易风 MABL 过程(如结构转换及其与分层的关系)产生新的认识。
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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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