Chen Wang, Justin E. Stopa, Doug Vandemark, Ralph Foster, Alex Ayet, Alexis Mouche, Bertrand Chapron, Peter Sadowski
{"title":"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","authors":"Chen Wang, Justin E. Stopa, Doug Vandemark, Ralph Foster, Alex Ayet, Alexis Mouche, Bertrand Chapron, Peter Sadowski","doi":"10.1002/gdj3.282","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 1","pages":"1-14"},"PeriodicalIF":3.3000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.282","citationCount":"0","resultStr":"{\"title\":\"A multi-tagged SAR ocean image dataset identifying atmospheric boundary layer structure in winter tradewind conditions\",\"authors\":\"Chen Wang, Justin E. 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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. 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A multi-tagged SAR ocean image dataset identifying atmospheric boundary layer structure in winter tradewind conditions
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.
Geoscience Data JournalGEOSCIENCES, 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.