Sentinel-1 EW mode dataset for Antarctica from 2014–2020 produced by the CASEarth Cloud Service Platform

ABSTRACT

Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00085.

KEYWORDS:

• Big Earth data
• CASEarth
• cloud computing
• Sentinel-1
• Antarctica

Acknowledgments

The authors are grateful for helpful comments from many researchers and colleagues. Thanks go to Jianhui Li, Tie Niu, and Yiming Liu for feedback, as well as the editor and anonymous referees.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data Availability Statement

The data that support the findings of this study are openly available in ScienceDB at http://www.doi.org/10.11922/sciencedb.j00076.00085.

Open Scholarship

This article has earned the Center for Open Science badge for Open Data. The data are openly accessible at http://www.doi.org/10.11922/sciencedb.j00076.00085.

Supplemental data

Supplemental data for this article can be accessed here.

Additional information

Funding

The research presented in this paper was funded by the Chinese Academy of Sciences Strategic Priority Research Program of the Big Earth Data Science Engineering Program (CASEarth), grant numbers XDA19090000, XDA19030000, Capacity Building Project of Big Earth Data Science Data Center of the Chinese Academy of Sciences, grant number WX145XQ07-13, and National Natural Science Foundation of China, grant number 41876226

Notes on contributors

Dong Liang

Dong Liang received B.Sc. and M.Sc. degrees in applied mathematics from the University of Hull, U.K. (2006), and Mälardalen University, Sweden (2009). He is pursuing a Ph.D. in cartography and GIS at the University of Chinese Academy of Sciences, with a focus on Big Earth Data and polar remote sensing.

Huadong Guo

Huadong Guo is a Professor of the Chinese Academy of Sciences (CAS) Aerospace Information Research Institute. He specializes in remote sensing, radar for Earth observation, and Digital Earth science. Currently he is the Chief Scientist of the International Research Center of Big Data for Sustainable Development Goals and CAS Big Earth Data Science Engineering Program. He has published more than 510 papers and 24 books, and is the awardee of 18 domestic and international prizes.

Lu Zhang

Lu Zhang received a Ph.D. degree in synthetic aperture radar (SAR) remote sensing from the Chinese Academy of Sciences (CAS) Institute of Remote Sensing Application in 2008. He is an Associate Professor of the Key Laboratory of Digital Earth Science at the CAS Aerospace Information Research Institute. He was a visiting scholar at the Institute of Electronics and Telecommunications of Rennes, University of Rennes 1, France, in 2013. His research interests include SAR image processing, information extraction, physical parameter estimation models, Big Earth Data, moon-based Earth observation methods, and polar remote sensing. He is the author of more than 80 journal articles and 9 patents in these fields.

Haipeng Li

Haipeng Li received a B.Sc. degree in software engineering from Liaoning Petrochemical University in 2017. He is a software engineer, mainly engaged in software development and testing, and data and image acquisition and processing

Xuezhi Wang

Xuezhi Wang received a B.Sc. degree from Hunan Agricultural University, an M.Sc. degree from the Institute of Zoology of the Chinese Academy of Sciences, and a Ph.D. degree from the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences in 2001, 2005, and 2009 respectively. In March 2009, he started working at the Computer Network Information Center of the Chinese Academy of Sciences. His research interests are big data analysis techniques, remote sensing processing, and big data platform development.