•  
  •  
 

Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

Digital Earth, marine environmental support, convergence of technologies, policy recommendations

Document Type

Precision Support Technology for Marine Environment

Abstract

The convergence of Digital Earth technologies and the field of marine environmental support represent a significant trend in global scientific and technological advancement. In response to the characteristics of marine environmental data such as sparsity, dynamic complexity, and multi-scale interdependence, and by integrating the evolution of international Digital Earth technology with China’s independent innovation practices, this study constructs three core methodological paradigms. The first is the element-integrated ontology, which proposes a hybrid representation paradigm of “spatiotemporal embedded field + ontology” to achieve integrated and dynamically evolving modeling of marine physical environments alongside geological, biological, chemical, and other multidimensional elements. The second is the cyclically nested spatiotemporal principle, introducing a spatiotemporal framework of “multi-period temporal decomposition and cross-scale structural transformation” to provide a physically interpretable and uncertainty-quantifiable predictive framework for complex marine phenomena. The third is the information transition and value-adding method, which establishes an information transition mechanism of “structure-semantics-utility” to facilitate the transformation and enhancement of massive raw data into high-value decision-making knowledge. Building on the above research, targeted policy recommendations are proposed across three dimensions: national-level infrastructure planning, data fusion development models, and value-adding channels for industrial applications. These recommendations aim to support the development and industrial implementation of an autonomous and controllable marine Digital Earth technology system in China.

First page

130

Last Page

141

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1. 郭华东, 刘珍, 王长林. 国际数字地球学会二十年：回顾与前瞻. 中国科学院院刊, 2024, 39(12): 2131-2139. Guo H D, Liu Z, Wang C L. Twenty years of International Society for Digital Earth: Review and prospect. Bulletin of Chinese Academy of Sciences, 2024, 39(12): 2131-2139. (in Chinese)

2. 郭华东, 邵芸, 杨崇俊, 等. 数字地球基础理论研究报告. 北京: 中国科学院遥感与数字地球研究所, 2003. Guo H D, Shao Y, Yang C J, et al. Digital Earth Fundamental Theoretical Research Report. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 2003. (in Chinese)

3. 吴立新, 荆钊, 陈显尧, 等. 我国海洋科学发展现状与未来展望. 地学前缘, 2022, 29(5): 12. Wu L X, Jing Z, Chen X Y, et al. Marine science in China: Current status and future outlooks. Earth Science Frontiers, 2022, 29(5): 12. (in Chinese)

4. Maike S. observationsBridging, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters, 2021, doi: 10.1088/1748-9326/ac0eb0

5. Gore A. The Digital Earth: Understanding our planet in the 21st century. Photogrammetric Engineering & Remote Sensing, 1998, doi: 10.1016/S0375-6505(98)00007-8.

6. Binder J. Earth to Google: Satellite images on your screen. Aerospace America, 2005, 43(11): 18-20.

7. Lam R, Sanchez-Gonzalez A, Willson M, et al. Learning skillful medium-range global weather forecasting. Science, 2023, 382: 1416-1421.

8. Mildenhall B, Srinivasan P P, Tancik M, et al. NeRF: representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 2022, 65(1): 99-106.

9. Brown C F, Kazmierski M R, Pasquarella V J, et al. Alphaearth foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv, 2025: 2507.22291.

10. Aaron B, Amit A, Amr H, et al. Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning. arXiv, 2025: 2510.18318.

11. 郭华东. 数字技术让人类用全新视角“解码”地球. 中国科技产业, 2025, (7): 16. Guo H D. Digital technology allows humanity to “decode” the Earth from a completely new perspective. China Science and Technology Industry, 2025, (7): 16. (in Chinese)

12. 李献斌, 马超, 杨俊. Palantir公司人工智能技术体系及其军事应用. 国防科技, 2025, 46(2): 28-35. Li X B, Ma C, Yang J. Palantir company’s artificial intelligence technology system and its military applications. National Defense Science and Technology, 2025, 46(2): 28-35. (in Chinese)

13. Feng Y, You H, Zhang Z, et al. Hypergraph neural networks. Proceedings of the AAAI conference on artificial intelligence. 2019, 33(1): 3558-3565.

14. Wang Q, Zhou W, Zeng L,et al.Intraseasonal variability of cross-slope flow in the northern South China Sea.Journal of Physical Oceanography. 2020, doi: 10.1175/JPO-D-19-0293.1.

15. Wang Q, Zeng L, Chen J, et al. The linkage of kuroshio intrusion and mesoscale eddy variability in the Northern South China Sea: Subsurface Speed Maximum.Geophysical Research Letters. 2020, 47(11), doi: 10.1029/2020GL087034.

16. Uhe P, Lucas C, Hawker L, et al. FathomDEM: An improved global terrain map using a hybrid vision transformer model. Environmental Research Letters, 2025, 20(3): 034002.

17. 王圣洁. 我国海洋地质知识体系DIKW模型及其规模估计与增长研究. 中国地质调查, 2018, 5(5):6-16. Wang S J. Research on the DIKW model of China’s marine geology knowledge system and its scale estimation and growth. China Geological Survey, 2018, 5(5): 6-16. (in Chinese)

18. 汪跃, 唐志军, 车德朝, 等. 战场态势一张图技术综述.指挥信息系统与技术, 2020, 11(1): 6. Wang Y, Tang Z J, Che D Z, et al. The integrated operational picture in battlefield management: A comprehensive survey. The Integrated Command, Control, and Information Systems (C2IS) Technology, 2020, 11(1): 6. (in Chinese)

19. 郭华东, 王长林. 数字地球：十五年发展与前瞻. 中国科学院院刊, 2013, (z1): 59-66. Guo H D,Wang C L. Digital Earth: Fifteen years experience and perspective. Bulletin of Chinese Academy of Sciences, 2013, (z1): 59-66. (in Chinese)

Download
Request a Copy

Share

COinS