https://orcid.org/0000-0002-1586-5117
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ABSTRACT
Unified representation of spatial earth data is an essential scientific issue. The analysis and mining of interdisciplinary spatial earth data resources can help discover hidden scientific knowledge, and even reveal the intrinsic relationship among different disciplines. However, the different description methods and inner structures among interdisciplinary spatial earth data bring significant challenges to unified data management and collaborative analysis in earth environment research. To address this issue, this paper proposes a unified representation method for interdisciplinary spatial earth data. First, this paper establishes a general metadata model and realizes the unified description of interdisciplinary data. Second, an entity data organization model is presented, which can realize the unified organization of entity data with different inner structures. Finally, we introduce the Spatial Earth Data Format (SEDF), a data format based on HDF5 for implementing the data organization model of interdisciplinary spatial earth data. Data representation experiments and validation are conducted to verify the availability and practicability of the proposed data representation method. The results suggest the powerful ability to represent spatial earth data efficiently and ensure data integrity, which is convenient for data management and application.
Acknowledgments
The authors would like to thank USGS Earth Explorer (https://earthexplorer.usgs.gov/). The Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center (https://ladsweb.modaps.eosdis.nasa.gov/), NOAA Physical Sciences Laboratory (ftp://ftp.cdc.noaa.gov/) and IGS Data Center of Wuhan University (ftp://igs.gnsswhu.cn/pub/gps/products/) for providing experimental data. The authors would also like to thank the anonymous reviewers and editors for commenting on this paper.
Disclosure statement
No potential conflict of interest was reports by the authors.
Data availability statement
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/20964471.2022.2091310.
Additional information
Funding
Notes on contributors
Shuang Wang
Shuang Wang is currently pursuing the Ph.D. degree in signal and information processing with the University of Chinese Academy of Sciences. She received her M.S. degree from the School of Information Science and Technology, Beijing Forestry University. Her research interests include data engineering and geospatial data management.
Jian Wang
Jian Wang is a senior engineer in Aerospace Information Research Institute, Chinese Academy of Sciences. He received his Ph.D. degree from Institute of Software, Chinese Academy of Sciences. His research areas include high-performance geographic computing, parallel computing, remote sensing big data infrastructure.
Qin Zhan
Qin Zhan is an associate professor in Aerospace Information Research Institute, Chinese Academy of Sciences. She received her Ph.D. degree of photogrammetry and remote sensing from Wuhan University. Her research interests include digital earth, big earth data, natural disaster, and data visualization.
Lianchong Zhang
Lianchong Zhang is the deputy director of National Earth Observation Data Center of China (NODA), the deputy director of ChinaGEOSS Data Sharing Network, and an Assistant Professor in Aerospace Information Research Institute, Chinese Academy of Sciences. He received Ph.D. degree in signal and information processing from the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences in 2019 and received his postdoctoral training from Aerospace Information Research Institute, Chinese Academy of Sciences in 2022. His favorite research areas concern high performance remote-sensing image-processing technology and the big earth data.
Xiaochuang Yao
Xiaochuang Yao is working in the College of Land Science and Technology, China Agricultural University. He received his Ph.D. degree from China Agricultural University in 2017. His research interests include spatial big data and agricultural applications.
Guoqing Li
Guoqing Li is the director of National Earth Observation Data Center of China (NODA), the director of ChinaGEOSS Data Sharing Work, and a professor of Aerospace Information Research Institute, Chinese Academy of Sciences. He received Master of Science and Ph.D. of Science from Chinese Academy of Sciences in 1999 and 2005 respectively, majored in Cartography and Geographic Information System. He also has taken the visiting studies in ESA/ESRIN in 2007–2008 and Purdue University in 2010. His favorite research areas concern high-performance remote-sensing image-processing technology and big earth data. His main focus is currently on next-generation spatial data infrastructure and nature disaster data management.