Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Giuliani, Gaston; Chatenoux, Bruno; Bono, Andrea; Rodila, Denisa; Richard, Jean-Philippe; Allenbach, Karin; Dao, Hy; Peduzzi, Pascal

doi:10.1080/20964471.2017.1398903

Cited by 154 publications

(133 citation statements)

References 41 publications

(45 reference statements)

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“…It is worth mentioning that, as a relatively complete grid-based ecology, Open Data Cube is increasingly being used to systematically manage and process Earth observation data [63], such as the Australian Geoscience Data Cube [67], Colombian Data Cube [75], Swiss Data Cube [76], China Data Cube [77], and Armenian Data Cube [78]. Based on these data cubes, EO data management, analysis, Remote Sens.…”

Section: Dggs For Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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Section: Dggs For Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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“…To tackle these issues and bridge the gap between users' expectations and current Big Data analytical capabilities, EO Data Cubes (EODC) are a promising solution to store, organize, manage and analyze EO data. The main objective of EODC is to facilitate EO data usage by addressing Volume, Velocity, Variety challenges, and providing access to large spatio-temporal data, for a given geographic area over a specified time period, in an analysis ready format (Giuliani et al, 2017a). data can be merged with social data, we can build an integrative dataset of SES.…”

Section: 2mentioning

confidence: 99%

“…These initiatives are paving the way to broaden the use of EO data to larger communities of users; they represent about 50 different implementations worldwide supporting decision-makers with timely and actionable information converted in meaningful geophysical variables; and ultimately are unlocking the information power of EO data. For example, the Swiss Data Cube holds currenlty 34 years of Landsat and 3 years of Sentinel-2 (2015-2018) Analysis Ready Data (ARD) 7 over Switzerland (Giuliani et al, 2017a). This corresponds approximately to 7000 scenes for a total volume of 4TB and more than 110 billion observations that can be converted in different environmental variables (Figure 4).…”

Section: 2mentioning

confidence: 99%

Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security

Bolognesi¹,

Gerlak²,

Giuliani³

2018

Preprint

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The Social-Ecological Systems (SES) framework serves as a valuable framework to explore and understand social and ecological interactions, and pathways in water governance. Yet, it lacks a robust understanding of change. We argue an analytical and methodological approach to engaging global changes in SES is critical to strengthening the scope and relevance of the SES framework. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change that institutions and natural resources systems co-evolve to provide a dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We illustrate how Data Cube technology could overcome current limitations and offer reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by offering to combine spatial and time data with no major technical requirements for users.

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“…It proves that by providing a summarized view of a given spatial extent remotely sensed EO becomes an important element to monitor the ecological state of the different environmental compartments (water, soil, plants, etc.). So, precise and reliable data are an important component of the environmental monitoring systems [5]. There are several open remote sensing (RS) data repositories that provide highly valuable, timely and precise remotely sensed EO information.…”

Section: Introductionmentioning

confidence: 99%

Paving the Way towards an Armenian Data Cube

Asmaryan

Muradyan

Tepanosyan

et al. 2019

Data

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Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

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Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Cited by 154 publications

References 41 publications

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security

Paving the Way towards an Armenian Data Cube

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