Earth observation big data for climate change research

Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.

Quantitative Expression of Paleogeographic Information Based on Big Data

Paleogeographic analysis accounts for an essential part of geological research,making important contributions in the reconstruction of depositional environments and tectonic evolution histories(Ingalls et al.,2016;Merdith et al.,2017),the prediction of mineral resource distributions in continental sedimentary basins(Sun and Wang,2009),and the investigation of climate patterns and ecosystems(Cox,2016).

“Deep-time Digital Basin” Based on Big Data and Artificial Intelligence

1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zhang et al.,2016;Teng et al.,2016;Tian and Li,2018).The United States has built an information-sharing platform for state-owned scientific data as a national strategy.

SDGs科研工作台架构设计与实现

【目的】地球大数据平台为可持续发展目标研究提供了计算资源和数据资源的支持,但各学科研究人员对于资源的占用不均衡,且对资源的使用率也不高。本文致力于解决地球大数据平台中资源使用的问题,更好地服务科研人员开展SDGs研究。【方法】本文采用云原生技术构建了一个面向可持续发展研究的一站式科研工作环境,为研究人员提供在线获取和处理数据、训练和使用模型、构建和部署应用软件等服务。【结果】极大地减轻了科研人员搭建科研软件栈的工作负担,提高了对占用资源的使用率,同时依托云原生的自动扩缩容能力,实现了资源的均衡使用。【结论】通过云原生架构构建的科研工作台,实现了云服务从研发、测试、部署、版本更新到使用的一体化应用生态,有效地支持了面向数据驱动的SDGs研究新范式。

全球矿床数据库建设现状、应用与展望

基于大数据和人工智能技术的数据驱动科学范式推动地球科学研究发生了变革。作为地球科学的重要分支,现代矿床学经历了百余年的发展,已经积累了海量的数据资料,这些数据的流通和共享是发挥其资源价值的关键。文章介绍了中国“地质云”与全球矿产资源储量动态评估数据库、澳大利亚深部地球探测计划AuScope、美国矿产资源在线空间数据库、国际经济地质学家学会(SEG)Geofacets数据库、美国标准普尔公司SNL Metals&Mining数据库等国际主要矿床数据库的情况;同时,列举了应用大数据思维和人工智能方法在区域成矿规律、矿床成因机制、矿床类型判别、资源潜力评价、战略咨询等方面取得的若干重要进展。文章提出,未来在深时数字地球国际大科学计划的平台下,整合全球海量矿床数据,建设开放、共享、统一的矿床大数据平台势在必行。

Big Earth Data for quantitative measurement of community resilience:current challenges,progresses and future directions

Quantitative assessment of community resilience can provide support for hazard mitigation,disaster risk reduction,disaster relief,and long-term sustainable development.Traditional resilience assessment tools are mostly theory-driven and lack empirical validation,which impedes scientific understanding of community resilience and practical decision-making of resilience improvement.In the advent of the Big Data Era,the increasing data availability and advances in computing and modeling techniques offer new opportunities to understand,measure,and promote community resilience.This article provides a comprehensive review of the definitions of community resilience,along with the traditional and emerging data and methods of quantitative resilience measurement.The theoretical bases,modeling principles,advantages,and disadvantages of the methods are discussed.Finally,we point out research avenues to overcome the existing challenges and develop robust methods to measure and promote community resilience.This article establishes guidance for scientists to further advance disaster research and for planners and policymakers to design actionable tools to develop sustainable and resilient communities.

Classification framework and semantic labeling for Big Earth Data

Big Earth Data refers to the multidimensional integration and association of scientific data,including geography,resources,environment,ecology,and biology.An effective data classification system and label management strategy are important foundations for long-term management of data resources.The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program(CASEarth).This study constructed two sets of classification and coding systems that realize classification by mapping each other;namely,the geosphere-level and Sustainable Development Goals(SDGs)indicator classifications.This technique was based on natural language processing technology and solved problems with subject-word segmentation,weight calculation,and dynamic matching.A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100.Furthermore,we expect our study to provide the methodology and technical support for useroriented classification and label management services for Big Earth Data.

Big Earth data for disaster risk reduction

In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.

An open compute and data federation as an alternative to monolithic infrastructures for big Earth data analytics

An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data.Three critical aspects of the federation and the chosen technologies are elaborated upon:(1)federated data discovery,(2)federated access and(3)software distribution.With these technologies the open federation aims to provide homogenous access to resources,thereby enabling its users to generate meaningful results quickly and easily.This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration,including discovery of data across distributed archives,away from the end-users.Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures.The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp.Digital Twins vision for a high precision digital model of the Earth to model,monitor and simulate natural phenomena and related human activities.

基于Google Earth Engine平台的官厅水库流域开放水体动态研究

利用二阶段城市水体指数(two steps urban water index)结合地形坡度掩膜方法,基于Google Earth Engine(GEE)平台调用了5196幅Landsat影像,提取并分析了官厅水库流域1987-2018年长时间序列的开放水体动态.水体提取总体精度达到93.5%,Kappa系数达到0.871,提取的开放水体可用于动态分析.在此基础上,基于分段线性回归,将1987-2018年官厅水库流域开放水体面积划分为缓慢上升、快速下降和迅速回升3阶段的变化.相应地,1987-1996、1997-2010和2011-2018年水体面积年均变化量分别为5.50、-7.45和15.19 km^(2).年均降水量的变化是官厅水库流域开放水体面积变化的重要影响因素,一元线性回归决定系数为0.29(P<0.01).1997年后,流域水体面积变化受人类用水增加及土地利用变化的影响增强.研究结果可为城市化背景下区域水资源保护,促进区域可持续发展提供借鉴.

2012–2014 China's Earth Observation and Earth Science Development

Human beings are now facing global and regional sustainable development challenges.In China, Earth observation data play a fundamental role in Earth system science research. The support given by Earth observation data is required by many studies, including those on Earth's limited natural resources, the rapid development of economic and social needs, global change, extreme events, food security, water resources, sustainable economic and urban development, and emergency response. Application operation systems in many ministries and departments in China have entered a stage of sustainable development, and the State Key Project of High-Resolution Earth Observation Systems has been progressing since 2006. Earth observation technology in China has entered a period of rapid development.

Progress of Earth Observation in China

Sustainability is the current theme of global development, and for China, it is not only an opportunity but also a challenge. In 2016, the Paris Agreement on climate change was adopted, addressing the need to limit the rise of global temperatures. The United Nations(UN) has set Sustainable Development Goals(SDGs) to transform our world in terms of closely linking human well-being, economic prosperity, and healthy environments. Sustainable development requires the support of spatial information and objective evaluation,and the capability of macroscopic, rapid, accurate Earth observation techniques plays an important role in sustainable development. Recently, Earth observation technologies are developing rapidly in China, where scientists are building coordinated, comprehensive and sustainable Earth observation systems for global monitoring programs. Recent efforts include the Digital Belt and Road Program(DBAR) and comparative studies of the "three poles". This and other researches will provide powerful support for solving problems such as global change and environmental degradation.

Progress of Earth Observation in China

China is expanding and sharing its capacity for Earth observation by developing sensors,platforms,and launch capabilities in tandem with growing lunar and deep space exploration.China is considering the Moon as a viable Earth observation platform to provide high-quality,planetary-scale data.The platform would produce consistent spatiotemporal data because of its long operational life and the geological stability of the Moon.China is also quickly improving its capabilities in processing and transforming Earth observation data into useful and practical information.Programs such as the Big Earth Data Science Engineering Program(CASEarth)provide opportunities to integrate data and develop“Big Earth Data”platforms to add value to data through analysis and integration.Such programs can offer products and services independently and in collaboration with international partners for data-driven decision support and policy development.With the rapid digital transformation of societies,and consequently increasing demand for big data and associated products,Digital Earth and the Digital Belt and Road Program(DBAR)allow Chinese experts to collaborate with international partners to integrate valuable Earth observation data in regional and global sustainable development.

综合古地理重建的数字智能化趋势与方法进展

古地理重建是研究地质历史时期地表构造过程、海陆格局和地貌环境特征的一项综合研究,并通过绘制表达海洋和大陆的古代轮廓以及重要的地形和地表环境的地图来呈现,是还原地球演化历史、预测能源矿产分布、认识生命和气候演变的基础性工作。随着大数据时代的到来,数字化方法的应用为古地理图快速更新和友好呈现提供了方便。目前,全球有多个团队发布了数字化的全球古地理重建模型以及相关的数据和方法,如EarthByte、PaleoMap、UNIL、Deep Time Maps等团队。笔者研究团队基于“深时数字地球(Deep-time Digital Earth,DDE)”国际大科学计划提出的数据-知识-模型驱动的古地理重建思想,提出基于数字化方法驱动的升级更新全球古地理图的新流程,并通过不断尝试地球科学与信息科学的交叉融合,从知识图谱、大数据分析和机器学习技术等方面开发了多项古地理重建应用技术。以东特提斯域中二叠世—中三叠世的古地理重建为例,首先在GPlates软件平台上重建了板块构造框架,再利用岩相古地理图自动生成地形地貌图并结合人工校正,最后在GPlates软件通过图层叠加实现了中二叠世—中三叠世东特提斯域的动态数字综合古地理重建。本用例与广泛使用的Scotese(2021)的古地理图对比,在成图效率、数据丰富性和可追溯性、模型准确性等方面都有明显提升,并为该时期板块运动、冰期消亡、大洋缺氧和生物灭绝等重大地质事件的研究提供新的约束和启示。

基于Google Earth Engine的国内外研究进展

随着遥感技术的发展,人类积累了海量的遥感数据,如何利用地理云平台存储、处理和分析这些数据,形成对人类有价值的知识,成为大数据时代研究的一个焦点.谷歌地球引擎(Google Earth Engine,GEE)是美国谷歌公司开发的一个地理云处理平台.对GEE的体系结构、数据集、功能进行了介绍.说明了当前的研究现状,包括发表数量及被引频次,期刊类别,所属机构和研究方向.总结了以GEE为支撑的研究进展情况,具体包括土地利用变化、农业、生态、水文、居民地和灾害监测等方面.最后还对这一平台的优势和不足进行了评价.这些研究多具有大空间尺度、高空间精度和多源数据融合的特征.以GEE为支撑的研究成果已经产生了重要的学术影响和社会价值.

数字孪生农业中的多模态数据融合研究现状和展望

为进一步提高我国的农业生产智能化水平,文章系统总结了农业大数据驱动下的数字孪生农业及国内外数字孪生农业中多模态数据融合研究现状,并基于此展望了综合运用卫星遥感、无人机航拍、地面传感网多模态数据开展农业生产全过程、多要素动态映射的研究中面临的挑战和未来发展趋势。

ESDRec:一种面向地球大数据平台的数据?推荐模型

【应用背景】地球大数据共享服务系统是中国科学院“地球大数据科学工程”战略性先导科技专项的数据门户窗口,为全球用户提供了一个数据、计算与服务为一体的数据共享系统,推动形成地球科学数据共享新模式。【目的】随着数据资源的持续汇交发布,用户仅通过筛选、检索等方式来获取数据资源的难度也将随之增加,如何利用推荐技术帮助用户更加高效地获取科学数据是一个值得研究的问题。【方法】因此,本文设计了一个地球科学数据推荐模型ESDRec,该模型使用双向长短时记忆网络与注意力机制对用户兴趣偏好进行建模,并对地球科学数据的元数据特征属性关联度进行计算。本文将地球科学数据的领域特征融入到推荐模型中,实现了更加准确的推荐。【结论】通过在平台真实数据集上进行对比实验,本文验证了ESDRec模型的有效性。

Big Earth Data from space: a new engine for Earth science

Big data is a strategic highland in the era of knowledge-driven economies, and it is also a new type of strategic resource for all nations. Big data collected from space for Earth observation—so-called Big Earth Data—is creating new opportunities for the Earth sciences and revolutionizing the innovation of methodologies and thought patterns. It has potential to advance in-depth development of Earth sciences and bring more exciting scientific discoveries.The Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space was held in Beijing in June of 2015.The forum analyzed the development of Earth observation technology and big data, explored the concepts and scientific connotations of Big Earth Data from space, discussed the correlation between Big Earth Data and Digital Earth, and dissected the potential of Big Earth Data from space to promote scientific discovery in the Earth sciences, especially concerning global changes.

Big Earth data:A new frontier in Earth and information sciences

Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource.“Big Earth data”,derived from,but not limited to,Earth observation has macro-level capabilities that enable rapid and accurate monitoring of the Earth,and is becoming a new frontier contributing to the advancement of Earth science and significant scientific discoveries.Within the context of the development of big data,this paper analyzes the characteristics of scientific big data and recognizes its great potential for development,particularly with regard to the role that big Earth data can play in promoting the development of Earth science.On this basis,the paper outlines the Big Earth Data Science Engineering Project(CASEarth)of the Chinese Academy of Sciences Strategic Priority Research Program.Big data is at the forefront of the integration of geoscience,information science,and space science and technology,and it is expected that big Earth data will provide new prospects for the development of Earth science.

Big Earth Data science:an information framework for a sustainable planet

The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before.These changes will unlikely stop or even decelerate in the near future.There is an urgent need for a new scientific approach and an advanced form of evidence-based decisionmaking towards the benefit of society,the economy,and the environment.To understand the impacts and interrelationships between humans as a society and natural Earth system processes,we propose a new engineering discipline,Big Earth Data science.This science is called to provide the methodologies and tools to generate knowledge from diverse,numerous,and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth.Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves.The manuscript introduces the universe of discourse characterizing this new science,its foundational paradigms and methodologies,and a possible technological framework to be implemented by applying an ecosystem approach.CASEarth and GEOSS are presented as examples of international implementation attempts.Conclusions discuss important challenges and collaboration opportunities.