Abstract

AbstractThe Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.

Highlights

  • The world's ocean covers more than 70% of the Earth's surface, playing a crucial role in influencing the climate system

  • We focus on the prescribed ten geophysical phenomena because they are commonly observed by the S‐1 wave mode (WV) Synthetic Aperture Radars (SAR) vignettes

  • As opposed to the dataset used for human visualization where the retained minimum and maximum values are specific for each image, fixed values of 0 and 3 common to the entire database are applied to all downsampled ssr images

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Summary

Funding information

ESA Sentinel‐1A Mission Performance Center, Grant/Award Number: 4000107360/12/I-LG; ESA S1‐4SCI Ocean Study, Grant/Award Number: 4000115170/15/I-SBo; CNES TOSCA program; NASA Physical Oceanography, Grant/Award Number: NNX17AH17G; China Scholarship Council (CSC)

| INTRODUCTION
Vignettes are mainly collected from the Southern Ocean near Antarctica
There is no obvious intensity gradient across the linear feature
Findings
| DISCUSSION AND
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