Special Issue "Earth Observation Data Cubes"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (15 June 2019).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Gregory Giuliani
Website
Guest Editor
Institute for Environmental Sciences & UNEP/GRID-Geneva, University of Geneva, 66 Boulevard Carl-Vogt, CH – 1205 Geneva, Switzerland
Interests: earth observations; data cube, sustainable development; GEO/GEOSS, environmental sciences
Special Issues and Collections in MDPI journals
Prof. Dr. Gilberto Camara
Website
Guest Editor
Group on Earth Observations (GEO), 1211 Geneva 2, Switzerland.
Interests: Earth observation; land change monitoring; open source software
Dr. Brian Killough
Website
Guest Editor
National Aeronautics and Space Administration (NASA), Langley Research Center, Virginia 23681, USA.
Interests: Satellite Earth observations; data cubes; systems engineering
Dr. Stuart Minchin
Website
Guest Editor
Geoscience Australia, Canberra ACT 2601, Australia
Interests: Earth observations; GEO/GEOSS; environmental sciences; sustainable development; spatial sciences; water resources

Special Issue Information

Dear colleagues,

Remotely sensed Earth Observations (EO) data have already exceeded the petabyte-scale and are increasingly freely and openly available from different data holdings. This poses a certain number of issues in terms of volume (e.g., data volumes have increased 10x in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/spectral resolutions). Traditional approaches to the acquisition, management, distribution and analysis of EO data have limitations (e.g., data size, heterogeneity and complexity) that impede their true information potential to be realized.

The fact that the full information potential of EO data has not yet been realized and therefore remains still underutilized is explained by various reasons: (1) It requires scientific knowledge to understand what data is needed… optical (which resolution?), radar (which type?); (2) It is difficult to access and download the increasing volumes of data generated by satellites; (3) there is a lack of expertise and computing resources to efficiently prepare and utilize EO data; (4) the particular structure of EO data; and (5) the significant effort and cost required to store and process data limit its effective use.

Addressing Big Data challenges such as volume, velocity and variety, requires a change of paradigm and moving away from traditional local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. In particular, data volume and velocity will continue to grow as the demands increase for decision-support information derived from these data.

To tackle these issues and bridge the gap between users’ expectations and current Big Data analytical capabilities, EO Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with EO data and 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 in an analysis ready format.

Different EODC implementations are currently operational such as Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. These initiatives are paving the way to broaden the use of EO data to larger communities of users; support decision-makers with timely and actionable information converted into meaningful geophysical variables; and ultimately are unlocking the information power of EO data.

This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations and welcomes contributions with respect to (but without being restricted to):

  • Methods for generating Analysis Ready Data for both optical and SAR imagery
  • Interoperability challenges between EO Data Cubes
  • Algorithms for generating decision-ready products
  • Data fusion techniques in EO Data Cubes
  • Data mining using Machine Learning, Deep Learning, …
  • Data quality, reliability, …
  • Cost/Benefits analysis of EO Data Cubes
  • Thematic applications (e.g. biodiversity, climate, health, natural hazards, …) using EO Data Cubes
  • New innovative tools and solutions to work with EO Data Cubes
  • Use of high to very-high resolution EO data
  • Integration of in-situ observations
  • Local, national, regional implementations
  • Cloud-based computing
  • Architecture design of EO Data Cubes (HPC, Distributed Computing, Super Computers)
  • Capacity building and training
  • Support to policy framework such as the Sustainable Development Goals, the Paris agreement, Aichi targets, or Water Framework Directive
  • Links with initiatives like Copernicus or the Global Earth Observation System of Systems (GEOSS).

Dr. Gregory Giuliani
Dr. Brian Killough
Dr. Stuart Minchin
Prof. Dr. Gilberto Camara
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Important dates

• September 1, 2018: Launch of the Call for Papers
• December 31, 2018: Deadline for abstract submission (800-word) to guest editors
• January 15, 2019: Notification of abstract acceptance and full paper submission invitation
• May 30, 2019: Deadline for submission
• June 30, 2019: Revision/rejection notification
• October 31, 2019: Paper acceptance notification

Keywords

  • Earth Observations
  • Data Cube
  • Landsat
  • Sentinel-1
  • Sentinel-2
  • Sustainable Development Goals
  • Interoperability
  • High-Resolution Data
  • In-Situ Data Integration
  • User-Driven Applications

Published Papers (15 papers)

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Editorial

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Open AccessEditorial
Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes
Data 2019, 4(4), 147; https://doi.org/10.3390/data4040147 - 25 Nov 2019
Cited by 4
Abstract
Earth Observation Data Cubes (EODC) have emerged as a promising solution to efficiently and effectively handle Big Earth Observation (EO) Data generated by satellites and made freely and openly available from different data repositories. The aim of this Special Issue, “Earth Observation Data [...] Read more.
Earth Observation Data Cubes (EODC) have emerged as a promising solution to efficiently and effectively handle Big Earth Observation (EO) Data generated by satellites and made freely and openly available from different data repositories. The aim of this Special Issue, “Earth Observation Data Cube”, in Data, is to present the latest advances in EODC development and implementation, including innovative approaches for the exploitation of satellite EO data using multi-dimensional (e.g., spatial, temporal, spectral) approaches. This Special Issue contains 14 articles covering a wide range of topics such as Synthetic Aperture Radar (SAR), Analysis Ready Data (ARD), interoperability, thematic applications (e.g., land cover, snow cover mapping), capacity development, semantics, processing techniques, as well as national implementations and best practices. These papers made significant contributions to the advancement of a more Open and Reproducible Earth Observation Science, reducing the gap between users’ expectations for decision-ready products and current Big Data analytical capabilities, and ultimately unlocking the information power of EO data by transforming them into actionable knowledge. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available

Research

Jump to: Editorial, Other

Open AccessFeature PaperArticle
National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices
Data 2019, 4(4), 144; https://doi.org/10.3390/data4040144 - 05 Nov 2019
Cited by 4
Abstract
The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This [...] Read more.
The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
Land Cover Mapping using Digital Earth Australia
Data 2019, 4(4), 143; https://doi.org/10.3390/data4040143 - 01 Nov 2019
Cited by 2
Abstract
This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or [...] Read more.
This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of Queensland and New South Wales. This was achieved by progressively and hierarchically combining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessFeature PaperArticle
Snow Cover Evolution in the Gran Paradiso National Park, Italian Alps, Using the Earth Observation Data Cube
Data 2019, 4(4), 138; https://doi.org/10.3390/data4040138 - 09 Oct 2019
Cited by 4
Abstract
Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One [...] Read more.
Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One of the key variables to study is snow cover, since it represents an essential driver of many ecological, hydrological and socioeconomic processes in mountains. As remotely sensed data can contribute to filling the gap of sparse in-situ stations in high-altitude environments, a methodology for snow cover detection through time series analyses using Landsat satellite observations stored in an Open Data Cube is described in this paper, and applied to a case study on the Gran Paradiso National Park, in the western Italian Alps. In particular, this study presents a proof of concept of the preliminary version of the snow observation from space algorithm applied to Landsat data stored in the Swiss Data Cube. Implemented in an Earth Observation Data Cube environment, the algorithm can process a large amount of remote sensing data ready for analysis and can compile all Landsat series since 1984 into one single multi-sensor dataset. Temporal filtering methodology and multi-sensors analysis allows one to considerably reduce the uncertainty in the estimation of snow cover area using high-resolution sensors. The study highlights that, despite this methodology, the lack of available cloud-free images still represents a big issue for snow cover mapping from satellite data. Though accurate mapping of snow extent below cloud cover with optical sensors still represents a challenge, spatial and temporal filtering techniques and radar imagery for future time series analyses will likely allow one to reduce the current cloud cover issue. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
Paving the Way towards an Armenian Data Cube
Data 2019, 4(3), 117; https://doi.org/10.3390/data4030117 - 02 Aug 2019
Cited by 6
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
Dynamic Data Citation Service—Subset Tool for Operational Data Management
Data 2019, 4(3), 115; https://doi.org/10.3390/data4030115 - 01 Aug 2019
Cited by 1
Abstract
In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to [...] Read more.
In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of “dynamic data citation”. The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessFeature PaperArticle
Paving the Way to Increased Interoperability of Earth Observations Data Cubes
Data 2019, 4(3), 113; https://doi.org/10.3390/data4030113 - 30 Jul 2019
Cited by 4
Abstract
Earth observations data cubes (EODCs) are a paradigm transforming the way users interact with large spatio-temporal Earth observation (EO) data. It enhances connections between data, applications and users facilitating management, access and use of analysis ready data (ARD). The ambition is allowing users [...] Read more.
Earth observations data cubes (EODCs) are a paradigm transforming the way users interact with large spatio-temporal Earth observation (EO) data. It enhances connections between data, applications and users facilitating management, access and use of analysis ready data (ARD). The ambition is allowing users to harness big EO data at a minimum cost and effort. This significant interest is illustrated by various implementations that exist. The novelty of the approach results in different innovative solutions and the lack of commonly agreed definition of EODC. Consequently, their interoperability has been recognized as a major challenge for the global change and Earth system science domains. The objective of this paper is preventing EODC from becoming silos of information; to present how interoperability can be enabled using widely-adopted geospatial standards; and to contribute to the debate of enhanced interoperability of EODC. We demonstrate how standards can be used, profiled and enriched to pave the way to increased interoperability of EODC and can help delivering and leveraging the power of EO data building, efficient discovery, access and processing services. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
Semantic Earth Observation Data Cubes
Data 2019, 4(3), 102; https://doi.org/10.3390/data4030102 - 17 Jul 2019
Cited by 3
Abstract
There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information [...] Read more.
There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
Building a SAR-Enabled Data Cube Capability in Australia Using SAR Analysis Ready Data
Data 2019, 4(3), 100; https://doi.org/10.3390/data4030100 - 15 Jul 2019
Cited by 3
Abstract
A research alliance between the Commonwealth Scientific and Industrial Research Organization and Geoscience Australia was established in relation to Digital Earth Australia, to develop a Synthetic Aperture Radar (SAR)-enabled Data Cube capability for Australia. This project has been developing SAR analysis ready data [...] Read more.
A research alliance between the Commonwealth Scientific and Industrial Research Organization and Geoscience Australia was established in relation to Digital Earth Australia, to develop a Synthetic Aperture Radar (SAR)-enabled Data Cube capability for Australia. This project has been developing SAR analysis ready data (ARD) products, including normalized radar backscatter (gamma nought, γ0), eigenvector-based dual-polarization decomposition and interferometric coherence, all generated from the European Space Agency (ESA) Sentinel-1 interferometric wide swath mode data available on the Copernicus Australasia Regional Data Hub. These are produced using the open source ESA SNAP toolbox. The processing workflows are described, along with a comparison of the γ0 backscatter and interferometric coherence ARD produced using SNAP and the proprietary software GAMMA. This comparison also evaluates the effects on γ0 backscatter due to variations related to: Near- and far-range look angles; SNAP’s default Shuttle Radar Topography Mission (SRTM) DEM and a refined Australia-wide DEM; as well as terrain. The agreement between SNAP and GAMMA is generally good, but also presents some systematic geometric and radiometric differences. The difference between SNAP’s default SRTM DEM and the refined DEM showed a small geometric shift along the radar view direction. The systematic geometric and radiometric issues detected can however be expected to have negligible effects on analysis, provided products from the two processors and two DEMs are used separately and not mixed within the same analysis. The results lead to the conclusion that the SNAP toolbox is suitable for producing the Sentinel-1 ARD products. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
A Portal Offering Standard Visualization and Analysis on top of an Open Data Cube for Sub-National Regions: The Catalan Data Cube Example
Data 2019, 4(3), 96; https://doi.org/10.3390/data4030096 - 10 Jul 2019
Cited by 4
Abstract
The amount of data that Sentinel fleet is generating over a territory such as Catalonia makes it virtually impossible to manually download and organize as files. The Open Data Cube (ODC) offers a solution for storing big data products in an efficient way [...] Read more.
The amount of data that Sentinel fleet is generating over a territory such as Catalonia makes it virtually impossible to manually download and organize as files. The Open Data Cube (ODC) offers a solution for storing big data products in an efficient way with a modest hardware and avoiding cloud expenses. The approach will still be useful up to the next decade. Yet, ODC requires a level of expertise that most people who could benefit from the information do not have. This paper presents a web map browser that gives access to the data and goes beyond a simple visualization by combining the OGC WMS standard with modern web browser capabilities to incorporate time series analytics. This paper shows how we have applied this tool to analyze the spatial distribution of the availability of Sentinel 2 data over Catalonia and revealing differences in the number of useful scenes depending on the geographical area that ranges from one or two images per month to more than one image per week. The paper also demonstrates the usefulness of the same approach in giving access to remote sensing information to a set of protected areas around Europe participating in the H2020 ECOPotential project. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
Achieving the Full Vision of Earth Observation Data Cubes
Data 2019, 4(3), 94; https://doi.org/10.3390/data4030094 - 06 Jul 2019
Cited by 5
Abstract
Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology [...] Read more.
Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology over the last decade have enabled a new approach for how to organize, analyze, and share Earth observation imagery, broadly referred to as a data cube. The vision and promise of image data cubes is to lower these hurdles and expand the user community by making analysis ready data readily accessible and providing modern approaches to more easily analyze and visualize the data, empowering a larger community of users to improve their knowledge of place and make better informed decisions. Image data cubes are large collections of temporal, multivariate datasets typically consisting of analysis ready multispectral Earth observation data. Several flavors and variations of data cubes have emerged. To simplify access for end users we developed a flexible approach supporting multiple data cube styles, referencing images in their existing structure and storage location, enabling fast access, visualization, and analysis from a wide variety of web and desktop applications. We provide here an overview of that approach and three case studies. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessFeature PaperArticle
Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube
Data 2019, 4(3), 93; https://doi.org/10.3390/data4030093 - 05 Jul 2019
Cited by 3
Abstract
This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis [...] Read more.
This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively. Full article
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Open AccessArticle
On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library
Data 2019, 4(3), 92; https://doi.org/10.3390/data4030092 - 28 Jun 2019
Cited by 8
Abstract
Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, [...] Read more.
Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Open AccessArticle
A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis
Data 2019, 4(2), 86; https://doi.org/10.3390/data4020086 - 18 Jun 2019
Cited by 5
Abstract
Continental and global datasets based on earth observations or computational models challenge the existing map algebra approaches. The available datasets differ in their spatio-temporal extents and their spatio-temporal granularity, which makes it difficult to process them as time series data in map algebra [...] Read more.
Continental and global datasets based on earth observations or computational models challenge the existing map algebra approaches. The available datasets differ in their spatio-temporal extents and their spatio-temporal granularity, which makes it difficult to process them as time series data in map algebra expressions. To address this issue we introduce a new map algebra approach that is topology based. This topology based map algebra uses spatio-temporal topological operators (STTOP and STTCOP) to specify spatio-temporal operations between topological related map layers of different time-series data. We have implemented several topology based map algebra tools in the open source geoinformation system GRASS GIS and its open source cloud processing engine actinia. We demonstrate the application of our topology based map algebra by solving real world big data problems using a single algebraic expression. This included the massively parallel computation of the NDVI from a series of 100 Sentinel2A scenes organized as earth observation data cubes. The processing was performed and benchmarked on a many core computer setup and in a distributed container environment. The design of our topology based map algebra allows us to deploy it as a standardized service in the EU Horizon 2020 project openEO. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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Other

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Open AccessConcept Paper
A Transformative Concept: From Data Being Passive Objects to Data Being Active Subjects
Data 2019, 4(4), 135; https://doi.org/10.3390/data4040135 - 02 Oct 2019
Cited by 1
Abstract
The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive [...] Read more.
The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive objects (DPO) to a new perception of data as active subjects (DAS). This transition would greatly increase data usage and exploitation, and support the extraction of knowledge from data products. Enabling the data subjects to actively reach out to potential users would revolutionize data dissemination and sharing and facilitate collaboration in user communities. The three core elements of the transformative DAS concept are: (1) “intelligent semantic data agents” (ISDAs) that have the capabilities to communicate with their human and digital environment. Each ISDA provides a voice to the data product it represents. It has comprehensive knowledge of the represented product including quality, uncertainties, access conditions, previous uses, user feedbacks, etc., and it can engage in transactions with users. (2) A knowledge base that constructs extensive graphs presenting a comprehensive picture of communities of people, applications, models, tools, and resources and provides tools for the analysis of these graphs. (3) An interaction platform that links the ISDAs to the human environment and facilitates transaction including discovery of products, access to products and derived knowledge, modifications and use of products, and the exchange of feedback on the usage. This platform documents the transactions in a secure way maintaining full provenance. Full article
(This article belongs to the Special Issue Earth Observation Data Cubes) Printed Edition available
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