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@Article{Truckenbrodt2019,
  author     = {John Truckenbrodt and Terri Freemantle and Chris Williams and Tom Jones and David Small and Cl{\'{e}}mence Dubois and Christian Thiel and Cristian Rossi and Asimina Syriou and Gregory Giuliani},
  title      = {{Towards Sentinel-1 {SAR} Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube}},
  doi        = {10.3390/data4030093},
  number     = {3},
  pages      = {93},
  volume     = {4},
  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 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.},
  file       = {:Truckenbrodt2019 - Towards Sentinel 1 SAR Analysis Ready Data_ a Best Practices Assessment on Preparing Backscatter Data for the Cube.pdf:PDF},
  groups     = {SAR, ARD, Background, Methods/Results, Discussion},
  journal    = {Data},
  keywords   = {prio1, skimmed},
  month      = {jul},
  priority   = {prio1},
  publisher  = {{MDPI} {AG}},
  readstatus = {skimmed},
  year       = {2019},
}

@Article{Frantz2019,
  author     = {David Frantz},
  title      = {{FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond}},
  doi        = {10.3390/rs11091124},
  number     = {9},
  pages      = {1124},
  volume     = {11},
  abstract   = {Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.},
  file       = {:Frantz2019 - FORCE _ Landsat + Sentinel 2 Analysis Ready Data and beyond.pdf:PDF},
  groups     = {Optical, ARD, Background, Methods/Results},
  journal    = {Remote Sensing},
  keywords   = {prio1, skimmed},
  month      = {may},
  priority   = {prio1},
  publisher  = {{MDPI} {AG}},
  readstatus = {skimmed},
  year       = {2019},
}

@Article{Giuliani2017,
  author     = {Gregory Giuliani and Bruno Chatenoux and Andrea De Bono and Denisa Rodila and Jean-Philippe Richard and Karin Allenbach and Hy Dao and Pascal Peduzzi},
  title      = {{Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)}},
  doi        = {10.1080/20964471.2017.1398903},
  number     = {1-2},
  pages      = {100--117},
  volume     = {1},
  abstract   = {Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities. EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form. Systematic and regular provision of Analysis Ready Data (ARD) will significantly reduce the burden on EO data users. Nevertheless, ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task. This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains. The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.},
  file       = {:Giuliani2017 - Building an Earth Observations Data Cube_ Lessons Learned from the Swiss Data Cube (SDC) on Generating Analysis Ready Data (ARD).pdf:PDF},
  groups     = {EODC - OpenDataCube, Background, Methods/Results},
  journal    = {Big Earth Data},
  keywords   = {prio1, read},
  month      = {nov},
  priority   = {prio1},
  publisher  = {Informa {UK} Limited},
  readstatus = {read},
  year       = {2017},
}

@Article{Lewis2017,
  author     = {Adam Lewis and Simon Oliver and Leo Lymburner and Ben Evans and Lesley Wyborn and Norman Mueller and Gregory Raevksi and Jeremy Hooke and Rob Woodcock and Joshua Sixsmith and Wenjun Wu and Peter Tan and Fuqin Li and Brian Killough and Stuart Minchin and Dale Roberts and Damien Ayers and Biswajit Bala and John Dwyer and Arnold Dekker and Trevor Dhu and Andrew Hicks and Alex Ip and Matt Purss and Clare Richards and Stephen Sagar and Claire Trenham and Peter Wang and Lan-Wei Wang},
  title      = {{The Australian Geoscience Data Cube — Foundations and lessons learned}},
  doi        = {10.1016/j.rse.2017.03.015},
  pages      = {276--292},
  volume     = {202},
  abstract   = {The Australian Geoscience Data Cube (AGDC) aims to realise the full potential of Earth observation data holdings by addressing the Big Data challenges of volume, velocity, and variety that otherwise limit the usefulness of Earth observation data. There have been several iterations and AGDC version 2 is a major advance on previous work.
The foundations and core components of the AGDC are: (1) data preparation, including geometric and radiometric corrections to Earth observation data to produce standardised surface reflectance measurements that support time-series analysis, and collection management systems which track the provenance of each Data Cube product and formalise re-processing decisions; (2) the software environment used to manage and interact with the data; and (3) the supporting high performance computing environment provided by the Australian National Computational Infrastructure (NCI).
A growing number of examples demonstrate that our data cube approach allows analysts to extract rich new information from Earth observation time series, including through new methods that draw on the full spatial and temporal coverage of the Earth observation archives. To enable easy-uptake of the AGDC, and to facilitate future cooperative development, our code is developed under an open-source, Apache License, Version 2.0. This opensource approach is enabling other organisations, including the Committee on Earth Observing Satellites (CEOS), to explore the use of similar data cubes in developing countries.},
  file       = {:Lewis2017 - The Australian Geoscience Data Cube _ Foundations and Lessons Learned.pdf:PDF},
  groups     = {EODC - OpenDataCube, Background, Methods/Results},
  journal    = {Remote Sensing of Environment},
  keywords   = {prio1, read},
  month      = {dec},
  priority   = {prio1},
  publisher  = {Elsevier {BV}},
  readstatus = {read},
  year       = {2017},
}

@InProceedings{Lewis2018,
  author     = {Adam Lewis and Jennifer Lacey and Susanne Mecklenburg and Jonathon Ross and Andreia Siqueira and Brian Killough and Zoltan Szantoi and Takeo Tadono and Ake Rosenavist and Philippe Goryl and Nuno Miranda and Steven Hosford},
  booktitle  = {{IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium}},
  title      = {{CEOS Analysis Ready Data for Land (CARD4L) Overview}},
  doi        = {10.1109/igarss.2018.8519255},
  pages      = {7407-7410},
  publisher  = {{IEEE}},
  abstract   = {For many land monitoring applications using remote sensing, lack of data is no longer an issue, as it may have been in the past. Programs, such as Copernicus by the European Commission and the Landsat Missions by the United States Geological Survey, have adopted systematic acquisition strategies, and distribute vast amounts of satellite data under open licenses. In parallel, storage and computing capability have evolved to make it cost-effective and practical to process and analyze these data at various scales. Data architecture solutions, such as the Open Data Cube (ODC) and the Copernicus Data and Information Access Services (DIAS), are providing frameworks that make [scientific] analysis much simpler and straightforward. However, enabling non-expert users without the expertise and/or computation resources to re-process and store lowlevel data products in order to exploit these capabilities, has proven more challenging. The Committee on Earth Observation Satellites (CEOS1) is working to address this challenge through the CEOS Analysis Ready Data for Land (CARD4L) initiative [1]. CARD4L is foreseen to enable users to access satellite data products that are ‘ready to use’ for a wide range of land applications. Moreover, CARD4L aims to enable non-expert users access to products that have been processed ‘far enough’ to be suitable for immediate analysis for a range of applications, while ensuring they are not too specific to only be used for particular topics or areas.
CARD4L will be an important enabler of the Open Data Cube (ODC) initiative [2]. Through CARD4L, users will be able to easily locate products that are suitable for ingestion
into Data Cubes [3], and will have confidence that these different CARD4L products will limit as far as possible barriers to interoperability.},
  file       = {:Lewis2018 - CEOS Analysis Ready Data for Land (CARD4L) Overview.pdf:PDF},
  groups     = {ARD, Background},
  keywords   = {prio1, read},
  month      = {jul},
  priority   = {prio1},
  readstatus = {read},
  year       = {2018},
}

@InProceedings{Killough2018,
  author     = {Brian Killough},
  booktitle  = {{IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium}},
  title      = {{Overview of the Open Data Cube Initiative}},
  doi        = {10.1109/igarss.2018.8517694},
  pages      = {8629-8632},
  publisher  = {{IEEE}},
  abstract   = {The Open Data Cube (ODC) initiative seeks to increase the value and impact of global Earth observation satellite data by providing an open and freely accessible data exploitation architecture and to foster a community to develop, sustain, and grow the technology and the breadth and depth of its applications for societal benefit. Successful ODC implementations exist in Australia, Colombia, Switzerland and Taiwan and are under development or consideration in another 39 countries. Since its inception in early 2017, the ODC has made significant progress in the advancement of open source software tools and algorithms that support the deployment and operation of data cubes on local or cloud computing systems around the world. With the increased interest in data cubes, the ODC is poised to become a world leader in open source data cube technologies to enhance the value of satellite data for all users.},
  file       = {:Killough2018 - Overview of the Open Data Cube Initiative.pdf:PDF},
  groups     = {EODC - OpenDataCube, Background},
  keywords   = {prio2, read},
  month      = {jul},
  priority   = {prio2},
  readstatus = {read},
  year       = {2018},
}

@InProceedings{Woodcock2018,
  author    = {Woodcock, Robert and Paget, Matt and Wang, Peter and Held, Alex},
  booktitle = {IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium},
  title     = {Accelerating Industry Innovation Using the Open Data Cube in Australia},
  doi       = {10.1109/IGARSS.2018.8519170},
  pages     = {8636-8638},
  year      = {2018},
}

@InProceedings{Roberts2018,
  author    = {Dale Roberts and Bex Dunn and Norman Mueller},
  booktitle = {{IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium}},
  title     = {{Open Data Cube Products Using High-Dimensional Statistics of Time Series}},
  doi       = {10.1109/igarss.2018.8518312},
  pages     = {8647-8650},
  publisher = {{IEEE}},
  abstract  = {We describe some new techniques based on high-dimensional statistics of time series to create continental-scale products from Earth Observation data. These methods are developed on the Australian Open Data Cube called `Digital Earth Australia'. We provide an overview of these new `second order' statistical algorithms and show how they can be used in various ways.},
  file      = {:Roberts2018 - Open Data Cube Products Using High Dimensional Statistics of Time Series.pdf:PDF},
  groups    = {EODC - Use/Impact},
  keywords  = {prio3},
  month     = {jul},
  priority  = {prio3},
  year      = {2018},
}

@InProceedings{Giuliani2018,
  author    = {Gregory Giuliani and Bruno Chatenoux and Erica Honeck and Jean-Philippe Richard},
  booktitle = {{IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium}},
  title     = {{Towards Sentinel-2 Analysis Ready Data: a Swiss Data Cube Perspective}},
  doi       = {10.1109/igarss.2018.8517954},
  pages     = {8659-8662},
  publisher = {{IEEE}},
  abstract  = {Earth Observations Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with Earth Observations (EO) data. They can provide access to large spatio-temporal data in analysis ready format. Systematic and regular provision of Analysis Ready Data (ARD) can significantly reduce the burden on EO data users by minimizing the time and scientific knowledge required to access and prepare remotely-sensed data having consistent and spatially aligned calibrated surface reflectance observations. Currently, Sentinel-2 ARD are not commonly generated by the Copernicus program and consequently getting uniform and consistent Sentinel-2 ARD remains a challenging task. This paper presents an approach to generate Sentinel-2 ARD using an automated processing services chain. The approach has been tested and validated to complete the Swiss Data Cube with Sentinel-2 data.},
  file      = {:Giuliani2018 - Towards Sentinel 2 Analysis Ready Data_ a Swiss Data Cube Perspective.pdf:PDF},
  groups    = {Optical, ARD, EODC - OpenDataCube},
  keywords  = {prio2},
  month     = {jul},
  priority  = {prio2},
  year      = {2018},
}

@Article{Giuliani2019,
  author     = {Gregory Giuliani and Joan Mas{\'{o}} and Paolo Mazzetti and Stefano Nativi and Alaitz Zabala},
  title      = {{Paving the Way to Increased Interoperability of Earth Observations Data Cubes}},
  doi        = {10.3390/data4030113},
  number     = {3},
  pages      = {113},
  volume     = {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 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.},
  file       = {:Giuliani2019 - Paving the Way to Increased Interoperability of Earth Observations Data Cubes.pdf:PDF},
  groups     = {EODC - Concept, Background, ARD},
  journal    = {Data},
  keywords   = {prio2, read},
  month      = {jul},
  priority   = {prio2},
  publisher  = {{MDPI} {AG}},
  readstatus = {read},
  year       = {2019},
}

@Article{Giuliani2019a,
  author     = {Gregory Giuliani and Gilberto Camara and Brian Killough and Stuart Minchin},
  title      = {{Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes}},
  doi        = {10.3390/data4040147},
  number     = {4},
  pages      = {147},
  volume     = {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 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.},
  file       = {:Giuliani2019a - Earth Observation Open Science_ Enhancing Reproducible Science Using Data Cubes.pdf:PDF},
  groups     = {EODC - Concept},
  journal    = {Data},
  keywords   = {prio2, read},
  month      = {nov},
  priority   = {prio2},
  publisher  = {{MDPI} {AG}},
  readstatus = {read},
  year       = {2019},
}

@Article{Ticehurst2019,
  author    = {Catherine Ticehurst and Zheng-Shu Zhou and Eric Lehmann and Fang Yuan and Medhavy Thankappan and Ake Rosenqvist and Ben Lewis and Matt Paget},
  title     = {{Building a {SAR}-Enabled Data Cube Capability in Australia Using {SAR} Analysis Ready Data}},
  doi       = {10.3390/data4030100},
  number    = {3},
  pages     = {100},
  volume    = {4},
  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 (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.},
  file      = {:Ticehurst2019 - Building a SAR Enabled Data Cube Capability in Australia Using SAR Analysis Ready Data.pdf:PDF},
  groups    = {SAR, ARD, Background},
  journal   = {Data},
  keywords  = {prio1},
  month     = {jul},
  priority  = {prio1},
  publisher = {{MDPI} {AG}},
  year      = {2019},
}

@Article{Claverie2018,
  author    = {Martin Claverie and Junchang Ju and Jeffrey G. Masek and Jennifer L. Dungan and Eric F. Vermote and Jean-Claude Roger and Sergii V. Skakun and Christopher Justice},
  title     = {{The Harmonized Landsat and Sentinel-2 surface reflectance data set}},
  doi       = {10.1016/j.rse.2018.09.002},
  pages     = {145--161},
  volume    = {219},
  abstract  = {The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a Virtual Constellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, bidirectional reflectance distribution function normalization and spectral bandpass adjustment. Three products are derived from the HLS processing chain: (i) S10: full resolution MSI SR at 10 m, 20 m and 60 m spatial resolutions; (ii) S30: a 30 m MSI Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR); (iii) L30: a 30 m OLI NBAR. All three products are processed for every Level-1 input products from Landsat 8/OLI (L1T) and Sentinel-2/MSI (L1C). As of version 1.3, the HLS data set covers 10.35 million km2 and spans from first Landsat 8 data (2013); Sentinel-2 data spans from October 2015.
The L30 and S30 show a good consistency with coarse spatial resolution products, in particular MODIS Collection 6 MCD09CMG products (overall deviations do not exceed 11%) that are used as a reference for quality assurance. The spatial co-registration of the HLS is improved compared to original Landsat 8 L1T and Sentinel-2A L1C products, for which misregistration issues between multi-temporal data are known. In particular, the resulting computed circular errors at 90% for the HLS product are 6.2 m and 18.8 m, for S10 and L30 products, respectively. The main known issue of the current data set remains the Sentinel-2 cloud mask with many cloud detection omissions. The cross-comparison with MODIS was used to flag products with most evident non-detected clouds. A time series outlier filtering approach is suggested to detect remaining clouds. Finally, several time series are presented to highlight the high potential of the HLS data set for crop monitoring.},
  file      = {:Claverie2018 - The Harmonized Landsat and Sentinel 2 Surface Reflectance Data Set.pdf:PDF},
  groups    = {Optical, Discussion},
  journal   = {Remote Sensing of Environment},
  keywords  = {prio3},
  month     = {dec},
  priority  = {prio3},
  publisher = {Elsevier {BV}},
  year      = {2018},
}

@Article{Asmaryan2019,
  author       = {Shushanik Asmaryan and Vahagn Muradyan and Garegin Tepanosyan and Azatuhi Hovsepyan and Armen Saghatelyan and Hrachya Astsatryan and Hayk Grigoryan and Rita Abrahamyan and Yaniss Guigoz and Gregory Giuliani},
  journaltitle = {{Data}},
  title        = {{Paving the Way towards an Armenian Data Cube}},
  doi          = {10.3390/data4030117},
  number       = {3},
  pages        = {117},
  volume       = {4},
  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 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.},
  file         = {:Asmaryan2019 - Paving the Way Towards an Armenian Data Cube.pdf:PDF},
  groups       = {EODC - OpenDataCube, Background},
  journal      = {Data},
  keywords     = {prio3, skimmed},
  month        = {aug},
  priority     = {prio3},
  publisher    = {{MDPI} {AG}},
  readstatus   = {skimmed},
  year         = {2019},
}

@Article{Augustin2019,
  author    = {Hannah Augustin and Martin Sudmanns and Dirk Tiede and Stefan Lang and Andrea Baraldi},
  title     = {{Semantic Earth Observation Data Cubes}},
  doi       = {10.3390/data4030102},
  number    = {3},
  pages     = {102},
  volume    = {4},
  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 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.},
  file      = {:Augustin2019 - Semantic Earth Observation Data Cubes.pdf:PDF},
  groups    = {EODC - Projects},
  journal   = {Data},
  keywords  = {prio3},
  month     = {jul},
  priority  = {prio3},
  publisher = {{MDPI} {AG}},
  year      = {2019},
}

@Article{Maso2019,
  author     = {Joan Maso and Alaitz Zabala and Ivette Serral and Xavier Pons},
  title      = {{A Portal Offering Standard Visualization and Analysis on top of an Open Data Cube for Sub-National Regions: The Catalan Data Cube Example}},
  doi        = {10.3390/data4030096},
  number     = {3},
  pages      = {96},
  volume     = {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 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.},
  file       = {:Maso2019 - A Portal Offering Standard Visualization and Analysis on Top of an Open Data Cube for Sub National Regions_ the Catalan Data Cube Example.pdf:PDF},
  groups     = {EODC - OpenDataCube, Introduction},
  journal    = {Data},
  keywords   = {read},
  month      = {jul},
  publisher  = {{MDPI} {AG}},
  readstatus = {read},
  year       = {2019},
}

@Article{Kopp2019,
  author     = {Steve Kopp and Peter Becker and Abhijit Doshi and Dawn J. Wright and Kaixi Zhang and Hong Xu},
  title      = {{Achieving the Full Vision of Earth Observation Data Cubes}},
  doi        = {10.3390/data4030094},
  number     = {3},
  pages      = {94},
  volume     = {4},
  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 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.},
  file       = {:Kopp2019 - Achieving the Full Vision of Earth Observation Data Cubes.pdf:PDF},
  groups     = {EODC - Concept, Background},
  journal    = {Data},
  keywords   = {prio3, read},
  month      = {jul},
  priority   = {prio3},
  publisher  = {{MDPI} {AG}},
  readstatus = {read},
  year       = {2019},
}

@Article{Appel2019,
  author       = {Marius Appel and Edzer Pebesma},
  journaltitle = {Data},
  title        = {{On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library}},
  doi          = {10.3390/data4030092},
  number       = {3},
  pages        = {92},
  volume       = {4},
  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, 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.},
  file         = {:Appel2019 - On Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library.pdf:PDF},
  groups       = {EODC - Projects, Background},
  journal      = {Data},
  keywords     = {prio3},
  month        = {jun},
  priority     = {prio3},
  publisher    = {{MDPI} {AG}},
  year         = {2019},
}

@Article{Dhu2019,
  author     = {Trevor Dhu and Gregory Giuliani and Jimena Ju{\'{a}}rez and Argyro Kavvada and Brian Killough and Paloma Merodio and Stuart Minchin and Steven Ramage},
  title      = {{National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices}},
  doi        = {10.3390/data4040144},
  number     = {4},
  pages      = {144},
  volume     = {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 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.},
  file       = {:Dhu2019 - National Open Data Cubes and Their Contribution to Country Level Development Policies and Practices.pdf:PDF},
  groups     = {EODC - Use/Impact, Background},
  journal    = {Data},
  keywords   = {prio1, read},
  month      = {nov},
  priority   = {prio1},
  publisher  = {{MDPI} {AG}},
  readstatus = {read},
  year       = {2019},
}

@TechReport{Yee2020,
  author      = {Chan Yee and Chris Durbin and Patrick Quinn and Dana Shum},
  institution = {{NASA EOSDIS}},
  title       = {{Task 51-Cloud-Optimized Format Study}},
  number      = {20200001178},
  type        = {techreport},
  url         = {https://ntrs.nasa.gov/citations/20200001178},
  abstract    = {Purpose:
“The cloud infrastructure provides a number of capabilities that can dramatically improve access and use of Earth Observation data. However, in many cases, data may need to be reorganized and/or reformatted in order to make them tractable to support cloud-native analysis/access patterns. The purpose of this study is to examine the pros and cons of different formats for storing data on the cloud. The evaluation will focus on both enabling high-performance data access and usage as well as to meet the existing scientific data stewardship needs of EOSDIS.”
(Task 51 Statement of Work, Revision B - Enabling EOSDIS Data Usage Statement of Work)
With the above purpose in mind, the evaluation we present seeks to inform future decisions on both archival formats for cloud data as well as formats to use for analysis, if different from the archival format. We do not expect to produce a recommendation for a single format for use in all cases but rather hope to inform format selection for a given use based on archival and usability needs. Further study may be warranted as formats, EO data, cloud infrastructure and user behavior evolve.},
  file        = {:Yee2020 - Task 51 Cloud Optimized Format Study.pdf:PDF},
  groups      = {Background, Discussion, Data / Data Format / Grid / ...},
  keywords    = {prio2, read},
  month       = jan,
  priority    = {prio2},
  readstatus  = {read},
  year        = {2020},
}

@Article{Giuliani2020,
  author       = {Gregory Giuliani and Elvire Egger and Julie Italiano and Charlotte Poussin and Jean-Philippe Richard and Bruno Chatenoux},
  journaltitle = {{Data}},
  title        = {{Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?}},
  doi          = {10.3390/data5040100},
  issue        = {4},
  pages        = {100},
  volume       = {5},
  abstract     = {Environmental sustainability is nowadays a major global issue that requires efficient and effective responses from governments. Essential variables (EV) have emerged in different scientific communities as a means to characterize and follow environmental changes through a set of measurements required to support policy evidence. To help track these changes, our planet has been under continuous observation from satellites since 1972. Currently, petabytes of satellite Earth observation (EO) data are freely available. However, the full information potential of EO data has not been yet realized because many big data challenges and complexity barriers hinder their effective use. Consequently, facilitating the production of EVs using the wealth of satellite EO data can be beneficial for environmental monitoring systems. In response to this issue, a comprehensive list of EVs that can take advantage of consistent time-series satellite data has been derived. In addition, a set of use-cases, using an Earth Observation Data Cube (EODC) to process large volumes of satellite data, have been implemented to demonstrate the practical applicability of EODC to produce EVs. The proposed approach has been successfully tested showing that EODC can facilitate the production of EVs at different scales and benefiting from the spatial and temporal dimension of satellite EO data for enhanced environmental monitoring.},
  file         = {:Giuliani2020 - Essential Variables for Environmental Monitoring_ What Are the Possible Contributions of Earth Observation Data Cubes_.pdf:PDF},
  groups       = {EODC - Use/Impact, Introduction},
  incitefulid  = {263541638},
  keywords     = {prio2},
  magid        = {3094204188},
  priority     = {prio2},
  year         = {2020},
}

@InProceedings{Misev2019,
  author      = {Dimitar Misev and Peter Baumann and Dimitris Bellos and Stefan Wiehle},
  booktitle   = {{2019 IEEE International Conference on Big Data (Big Data)}},
  title       = {{BigDataCube: A Scalable, Federated Service Platform for Copernicus}},
  doi         = {10.1109/bigdata47090.2019.9006222},
  pages       = {4103-4112},
  abstract    = {The European Copernicus programme generates massive amounts of Earth Observation (EO) data, with the goal of improving our environmental understanding and management on local, regional, and global level. An initiative of this magnitude comes with a set of challenges, especially pertaining to effective service management once the huge volumes of raster data files are distributed across open data centers and commercial companies.The BigDataCube project responds to this challenge through the concept of federated, analysis-ready datacubes, exposed via the open OGC geo standard interfaces for interoperable access and processing, WMS, WCS, and WCPS.Such federated datacube services have been established in the project between the public German Copernicus hub, CODE-DE, and a commercial cloud provider, cloudeo AG, altogether offering access to more than 500 TB. Both data pools are federated in a location-transparent manner, establishing a common information space where users can query and combine datacubes without knowing their location of storage. In this nucleus of a growing federation recently the Alfred Wegener maritime research institute has joined, and further data centers are in the line. The spectrum of functionality available is showcased through several realistic use cases, including a value-adding Sentinel-1 SAR product on sea state and wind speed in the North Sea. The platform used is the pioneer datacube engine and OGC reference implementation, rasdaman. We report on the outcomes of the project, lessons learned, and further work foreseen in this area.},
  file        = {:Misev2019 - BigDataCube_ a Scalable, Federated Service Platform for Copernicus.pdf:PDF},
  groups      = {EODC - Projects, Background},
  incitefulid = {57292206},
  keywords    = {prio3},
  magid       = {3008412778},
  priority    = {prio3},
  year        = {2019},
}

@Article{Mahecha2020,
  author      = {Miguel D. Mahecha and Fabian Gans and Gunnar Brandt and Rune Christiansen and Sarah E. Cornell and Normann Fomferra and Guido Kraemer and Jonas Peters and Paul Bodesheim and Gustau Camps-Valls and Jonathan F. Donges and Wouter Dorigo and Lina M. Estupiñán-Suárez and Victor H. Gutierrez-Velez and Martin Gutwin and Martin Jung and Maria C. Londoño and Diego G. Miralles and Phillip Papastefanou and Markus Reichstein},
  title       = {Earth system data cubes unravel global multivariate dynamics},
  doi         = {10.5194/esd-11-201-2020},
  issue       = {1},
  pages       = {201--234},
  volume      = {11},
  abstract    = {Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.},
  file        = {:Mahecha2020 - Earth System Data Cubes Unravel Global Multivariate Dynamics.pdf:PDF},
  groups      = {EODC - Projects, Background},
  incitefulid = {100613904},
  journal     = {Earth System Dynamics Discussions},
  keywords    = {prio3},
  magid       = {2982196210},
  priority    = {prio3},
  year        = {2020},
}

@Article{Killough2019,
  author      = {Brian Killough},
  title       = {{The Impact of Analysis Ready Data in the Africa Regional Data Cube}},
  doi         = {10.1109/igarss.2019.8898321},
  pages       = {5646-5649},
  abstract    = {Users of satellite Earth observation data typically invest a large amount of effort into data preparation which is a major barrier for many global users and limits the impact and utilization of this data around the world. The Committee on Earth Observation Satellites (CEOS) has initiated an effort to define and promote the production and use of Analysis Ready Data (ARD) to lower the burden on global users and increase the use and impact of its data. Through the Open Data Cube (ODC) initiative and its demonstration in Africa, ARD is making an impact on this developing region of the world. This paper discusses how this data and the associated ODC tools are being used to address water extent, land change and urbanization, which are key application areas aligned with the United Nations Sustainable Development Goals (SDG). Through the ODC, the impact of ARD is growing rapidly around the world and our ability to address local and national decision-making needs will continue to improve.},
  file        = {:Killough2019 - The Impact of Analysis Ready Data in the Africa Regional Data Cube.pdf:PDF},
  groups      = {EODC - Use/Impact, Background},
  incitefulid = {96962756},
  keywords    = {read, prio2},
  magid       = {2983099966},
  priority    = {prio2},
  readstatus  = {read},
  year        = {2019},
}

@Article{Lewis2016,
  author      = {Adam Lewis and Leo Lymburner and Matthew B. J. Purss and Brendan P. Brooke and Benjamin J. K. Evans and Alex Ip and Arnold G. Dekker and James R. Irons and Stuart Minchin and Norman Mueller and Simon Oliver and Dale Roberts and Barbara Ryan and Medhavy Thankappan and Robert Woodcock and Lesley Wyborn},
  title       = {{Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube}},
  doi         = {10.1080/17538947.2015.1111952},
  issue       = {1},
  pages       = {106-111},
  volume      = {9},
  abstract    = {The effort and cost required to convert satellite Earth Observation (EO)data  into  meaningful  geophysical  variables  has  prevented  thesystematic analysis of all available observations. To overcome theseproblems, we utilise an integrated High Performance Computing andData environment to rapidly process, restructure and analyse theAustralian Landsat data archive. In this approach, the EO data areassigned to a common grid framework that spans the full geospatialand temporal extent of the observations–the EO Data Cube. Thisapproach  is  pixel-based  and  incorporates  geometric  and  spectralcalibration and quality assurance of each Earth surface reflectancemeasurement. We demonstrate the utility of the approach with rapidtime-series mapping of surface water across the entire Australiancontinent using 27 years of continuous, 25 m resolution observations.Our preliminary analysis of the Landsat archive shows how the EOData Cube can effectively liberate high-resolution EO data from theircomplex sensor-specific data structures and revolutionise our ability tomeasure environmental change.},
  file        = {:Lewis2016 - Rapid, High Resolution Detection of Environmental Change Over Continental Scales from Satellite Data – the Earth Observation Data Cube.pdf:PDF},
  groups      = {EODC - OpenDataCube, Background},
  incitefulid = {46919419},
  journal     = {International Journal of Digital Earth},
  keywords    = {skimmed},
  magid       = {2236111634},
  readstatus  = {skimmed},
  year        = {2016},
}

@InProceedings{Siqueira2019,
  author    = {Siqueira, Andreia and Lewis, Adam and Thankappan, Medhavy and Szantoi, Zoltan and Goryl, Philippe and Labahn, Steven and Ross, Jonathon and Hosford, Steven and Mecklenburg, Susanne and Tadono, Takeo and Rosenqvist, Ake and Lacey, Jennifer},
  booktitle = {{IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium}},
  title     = {{CEOS Analysis Ready Data For Land – An Overview on the Current and Future Work}},
  doi       = {10.1109/IGARSS.2019.8899846},
  pages     = {5536-5537},
  year      = {2019},
}

@Article{Nativi2017,
  author       = {Stefano Nativi and Paolo Mazzetti and Max Craglia},
  journaltitle = {{Big Earth Data}},
  title        = {A view-based model of data-cube to support big earth data systems interoperability},
  doi          = {10.1080/20964471.2017.1404232},
  number       = {1-2},
  pages        = {75-99},
  volume       = {1},
  abstract     = {Big  Earth  Data-Cube  infrastructures  are  becoming  more  and  more  popular  to  provide Analysis  Ready  Data,  especially  for  managing  satellite  time  series.  These  infrastructures build  on  the  concept  of  multidimensional  data  model  (data  hypercube)  and  are  complex  systems engaging different disciplines and expertise. For this reason, their interoperability capacity has become a challenge in the Global Change and Earth System science domains. To address this challenge, there is a pressing need in the community to reach a widely agreed definition  of  Data-Cube  infrastructures  and  their  key  features.  In  this  respect,  a  discussion  has  started  recently  about  the  definition  of  the  possible  facets  characterizing    Data-Cube  in  the  Earth  Observation  domain.  This  manuscript  contributes  to  such  debate  by  introducing  a  view-based  model  of  Earth  Data-Cube  systems  to  design its infrastructural architecture and content schemas, with the final  goal  of  enabling  and  facilitating  interoperability.  It  introduces  six modeling views, each of them is described according to: its main concerns,  principal  stakeholders,  and  possible  patterns  to  be  used.  The manuscript onsiders the Business Intelligence experience with Data Warehouse and multidimensional “cubes” along with the more recent and analogous development in the Earth Observation domain, and  puts  forward  a  set  of  interoperability  recommendations  based  on the modeling views.},
  file         = {:Nativi2017 - A View Based Model of Data Cube to Support Big Earth Data Systems Interoperability.pdf:PDF},
  groups       = {EODC - Concept, Background},
  incitefulid  = {50366670},
  keywords     = {skimmed, prio2},
  magid        = {2771417949},
  priority     = {prio2},
  readstatus   = {skimmed},
  year         = {2017},
}

@Article{Sudmanns2020,
  author      = {Martin Sudmanns and Dirk Tiede and Stefan Lang and Helena Bergstedt and Georg Trost and Hannah Augustin and Andrea Baraldi and Thomas Blaschke},
  title       = {{Big Earth data: disruptive changes in Earth observation data management and analysis?}},
  doi         = {10.1080/17538947.2019.1585976},
  issue       = {7},
  pages       = {832-850},
  volume      = {13},
  abstract    = {Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we
consider relevant for the imminent uptake of big Earth data.},
  file        = {:Sudmanns2020 - Big Earth Data_ Disruptive Changes in Earth Observation Data Management and Analysis_.pdf:PDF},
  groups      = {BigData, Background},
  incitefulid = {151457084},
  journal     = {International Journal of Digital Earth},
  keywords    = {prio1, read},
  magid       = {2922388164},
  priority    = {prio1},
  readstatus  = {read},
  year        = {2020},
}

@Article{Giuliani2020a,
  author      = {Gregory Giuliani and Bruno Chatenoux and Thomas Piller and Frédéric Moser and Pierre Marcel Anselme Lacroix},
  title       = {{Data Cube on Demand (DCoD): Generating an earth observation Data Cube anywhere in the world}},
  doi         = {10.1016/j.jag.2019.102035},
  issue       = {102035},
  volume      = {87},
  abstract    = {To tackle Big Data challenges such as Volume, Variety, and Velocity, the Earth Observations Data Cube (EODC)concept has emerged as a solution for lowering barriers and offering new possibilities to harness the informationpower of satellite EO data. However, installing, configuring, and managing an EODC instance is still difficultrequiring specific knowledge and capabilities. Consequently, facilitating and automating the generation andprovision of EODC given specific user’s requirements can be beneficial.In response to this issue, this paper presents the Data Cube on Demand (DCoD) approach, a proof-of-conceptthat aims at facilitating the generation and use of an EODC instance virtually anywhere in the World. Users areonly required to specify an area of interest; select the types of sensors between Landsat 5-7-8 and Sentinel-2;choose a desired temporal frame; and provide their email address to receive notifications. Then automatically anempty ODC instance is instantiated and desired data are ingested.The proposed approach has been successfully tested in two sites in Bolivia and DRC in thefield of environ-mental monitoring. It has lowered many complexity barriers of such a new technology; greatly facilitated thegeneration and use of the Data Cube technology; enhanced data sovereignty; and ultimately can help reachinglarge adoption and acceptance.},
  file        = {:Giuliani2020a - Data Cube on Demand (DCoD)_ Generating an Earth Observation Data Cube Anywhere in the World.pdf:PDF},
  groups      = {EODC - Use/Impact, Discussion},
  incitefulid = {181346687},
  journal     = {International Journal of Applied Earth Observation and Geoinformation},
  keywords    = {prio1, skimmed},
  magid       = {2995869204},
  priority    = {prio1},
  readstatus  = {skimmed},
  year        = {2020},
}

@Article{Giuliani2020b,
  author       = {Gregory Giuliani and Bruno Chatenoux and Antonio Benvenuti and Pierre Marcel Anselme Lacroix and Mattia Santoro and Paolo Mazzetti},
  journaltitle = {{Big Earth Data}},
  title        = {{Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube}},
  doi          = {10.1080/20964471.2020.1711633},
  issue        = {1},
  pages        = {3-22},
  volume       = {4},
  abstract     = {Avoiding, reducing, and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth. To halt and reverse the current trends in land degradation, there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands, as required by the Sustainable Development Goals (SDGs), in particular, the SDG indicator 15.3.1 (“proportion of land that is degraded over total land area”). Earth Observations (EO) can play an important role both for generating this indicator as well as complementing or enhancing national official data sources. Implementations like Trends. Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR. Consequently, there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data (e.g. Landsat or Sentinels). In response to this issue, this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.},
  file         = {:Giuliani2020b - Monitoring Land Degradation at National Level Using Satellite Earth Observation Time Series Data to Support SDG15 – Exploring the Potential of Data Cube.pdf:PDF},
  groups       = {EODC - Use/Impact},
  incitefulid  = {344109},
  keywords     = {prio3},
  magid        = {2999971033},
  priority     = {prio3},
  year         = {2020},
}

@Article{Appel2018,
  author      = {Marius Appel and Florian Lahn and Wouter Buytaert and Edzer Pebesma},
  title       = {{Open and scalable analytics of large Earth observation datasets: from scenes to multidimensional arrays using SciDB and GDAL}},
  doi         = {10.1016/j.isprsjprs.2018.01.014},
  volume      = {138},
  abstract    = {Earth observation (EO) datasets are commonly provided as collection of scenes, where individual scenes represent a temporal snapshot and cover a particular region on the Earth’s surface. Using these data in complex spatiotemporal modeling becomes difficult as soon as data volumes exceed a certain capacity or analyses include many scenes, which may spatially overlap and may have been recorded at different dates. In order to facilitate analytics on large EO datasets, we combine and extend the geospatial data abstraction library (GDAL) and the array-based data management and analytics system SciDB. We present an approach to automatically convert collections of scenes to multidimensional arrays and use SciDB to scale computationally intensive analytics. We evaluate the approach in three study cases on national scale land use change monitoring with Landsat imagery, global empirical orthogonal function analysis of daily precipitation, and combining historical climate model projections with satellite-based observations. Results indicate that the approach can be used to represent various EO datasets and that analyses in SciDB scale well with available computational resources. To simplify analyses of higher-dimensional datasets as from climate model output, however, a generalization of the GDAL data model might be needed. All parts of this work have been implemented as open-source software and we discuss how this may facilitate open and reproducible EO analyses.},
  file        = {:Appel2018 - Open and Scalable Analytics of Large Earth Observation Datasets_ from Scenes to Multidimensional Arrays Using SciDB and GDAL.pdf:PDF},
  groups      = {BigData},
  incitefulid = {74213988},
  journal     = {Isprs Journal of Photogrammetry and Remote Sensing},
  magid       = {2792826355},
  year        = {2018},
}

@InProceedings{Strobl2017,
  author      = {Peter Strobl and Peter Baumann and Lewis Adam and Szantoi Zoltan and Killough Brian and Purss Matthew and Craglia Massimo and Nativi Stefano and Held Alex and Dhu Trevor},
  booktitle   = {{Proceedings of the 2017 conference on Big Data from Space (BIDS' 2017)}},
  title       = {The six faces of the data cube},
  doi         = {10.2760/383579},
  pages       = {32--35},
  publisher   = {{Publications Office of the European Union}},
  abstract    = {Big Data from Space refers to Earth and Space observation data collected by space-borne and ground-based sensors. Whether for Earth or Space observation, they qualify being called 'big data' given the sheer volume of sensed data (archived data reaching the exabyte scale), their high velocity (new data is acquired almost on a continuous basis and with an increasing rate), their variety (data is delivered by sensors acting over various frequencies of the electromagnetic spectrum in passive and active modes), as well as their veracity (sensed data is associated with uncertainty and accuracy measurements). Last but not least, the value of big data from space depends on our capacity to extract information and meaning from them. The goal of the Big Data from Space conference is to bring together researchers, engineers, developers, and users in the area of Big Data from Space. It is co-organised by ESA, the Joint Research Centre (JRC) of the European Commission, and the European Union Satellite Centre (SatCen). The 2017 edition of the conference was hosted by CNES and held at the Pierre Baudis Convention Centre in Toulouse (France) from the 28th to the 30th of November 2017. These proceedings consist of a collection of 126 short papers corresponding to the oral and poster presentations presented at the conference. They are organised in sections matching the order of the conference sessions followed by the contributions that were presented during the poster session, also organised by topics. They provide a snapshot of the current research activities, developments, and initiatives in Big Data from Space. While a continued number of contributions are devoted to infrastructures and platforms enabling to exploit the value behind the volume, velocity, and variety of Big Data from Space, this third edition of the Big Data from Space conference shows a sharp increase of applications particularly related to large scale analysis including the temporal dimensions in view of better understanding the dynamics of the processes that are shaping our planet and our universe. Other new trends regard the information extraction using advanced machine learning techniques such as those based on deep learning and convolution neural networks. The development of new standards to ensure the interoperability of Big Data from Space is also gaining attention similarly to data cubes and multidimensional array representations. All these topics as well as other generic key aspects of big data are mirrored onto dedicated sections in these proceedings.},
  file        = {:Strobl2017 - The Six Faces of the Data Cube.pdf:PDF},
  groups      = {EODC - Concept, Background},
  incitefulid = {156121694},
  keywords    = {prio1},
  magid       = {2891300828},
  priority    = {prio1},
  year        = {2017},
}

@Article{Gomes2020,
  author      = {Vitor Conrado Faria Gomes and Gilberto Ribeiro de Queiroz and Karine Reis Ferreira},
  title       = {{An Overview of Platforms for Big Earth Observation Data Management and Analysis}},
  doi         = {10.3390/rs12081253},
  issue       = {8},
  pages       = {1253},
  volume      = {12},
  abstract    = {In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest.},
  file        = {:Gomes2020 - An Overview of Platforms for Big Earth Observation Data Management and Analysis.pdf:PDF},
  groups      = {EODC - Projects, Background},
  incitefulid = {17071695},
  journal     = {Remote Sensing},
  keywords    = {prio2, skimmed},
  magid       = {3016495922},
  priority    = {prio2},
  readstatus  = {skimmed},
  year        = {2020},
}

@Article{Boulton2018,
  author     = {Geoffrey Boulton},
  title      = {{The challenges of a Big Data Earth}},
  doi        = {10.1080/20964471.2017.1397411},
  number     = {1},
  pages      = {1--7},
  volume     = {2},
  abstract   = {The potential of big data fused with the vision of a digital Earth offers powerful opportunities to deepen understanding of the whole Earth system and the management of a sustainable planet. It is important to stand back from often confusing detail to clarify what those opportunities are and how they might be seized. The essential scientific potential of data, big or small, is to reveal patterns, which have often been the fundamental first step in stimulating inquiry, leading to new questions, new perspectives and potentially to new answers. The digital revolution has created  a “digital microscope” that permits us to see patterns that have not been seen before, and when coupled with machine learning technologies to analyse them in creating statistical predictions of the behaviour of both human and non-human systems. These potentials converge with the imperative to represent an Earth system with interacting non-human and human components, as a vital contribution to the understanding and actions required  in  working  towards  planetary  sustainability. But a digital Earth is also capable of being represented mathematically as a digitally networked phenomenon, analogous to an analogue computer, and should be an important target for a Big Earth Data Journal. We should also return to Al Gore’s vision of an  accessible digital Earth with wide usability. Pre-determining the separate functions of parallel digital Earths risks losing one of the great potentials of big data and learning algorithms, the identification and analysis of unanticipated relationships and processes.},
  file       = {:Boulton2018 - The Challenges of a Big Data Earth.pdf:PDF},
  groups     = {BigData, Background},
  journal    = {Big Earth Data},
  keywords   = {prio3, read},
  month      = {jan},
  priority   = {prio3},
  publisher  = {Informa {UK} Limited},
  readstatus = {read},
  year       = {2018},
}

@Article{Giuliani2017a,
  author    = {Gregory Giuliani and Hy Dao and Andrea De Bono and Bruno Chatenoux and Karin Allenbach and Pierric De Laborie and Denisa Rodila and Nikos Alexandris and Pascal Peduzzi},
  title     = {{Live Monitoring of Earth Surface (LiMES): A framework for monitoring environmental changes from Earth Observations}},
  doi       = {10.1016/j.rse.2017.05.040},
  pages     = {222--233},
  volume    = {202},
  abstract  = {Global environmental changes are mostly induced by human activities (e.g., food and energy production, urbanization, mining activities). To assess and understand these changes that are occurring all around the planet, regular and continuous monitoring is an essential condition. However, due to the potentially large area spread over numerous locations that need to be followed, this usually leads to a low frequency of monitoring of environmental changes of only a few selected sites at best. With the increasing number of freely and openly accessible big remotely-sensed Earth Observations (EO) Data repositories and the increasing capabilities of open and interoperable software solutions it is now possible to automate various EO data processing tasks to monitor environmental changes at large scale. This paper presents the Live Monitoring of Earth Surface (LiMES) framework that helps to automate image processing tasks in transforming raw data into information and knowledge through workflows using interoperable processing service chains for monitoring environmental changes. Both benefits and limitations are demonstrated and discussed through the implementation of a prototype to facilitate the update on the status of some of the 278 UNEP Environmental Hotspots. We believe that such a framework can help to reduce the gap between massive volumes of EO data and the users such as International Organizations (IO) in order to help them better fulfil their environmental monitoring mandates by bringing raw data to a level which can be used by non-remote sensing experts for basic impacts assessments.},
  file      = {:Giuliani2017a - Live Monitoring of Earth Surface (LiMES)_ a Framework for Monitoring Environmental Changes from Earth Observations.pdf:PDF},
  groups    = {Optical, Background},
  journal   = {Remote Sensing of Environment},
  month     = {dec},
  publisher = {Elsevier {BV}},
  year      = {2017},
}

@Article{Guo2016,
  author     = {Huadong Guo and Zhen Liu and Hao Jiang and Changlin Wang and Jie Liu and Dong Liang},
  title      = {{Big Earth Data: a new challenge and opportunity for Digital Earth's development}},
  doi        = {10.1080/17538947.2016.1264490},
  number     = {1},
  pages      = {1--12},
  volume     = {10},
  abstract   = {Digital Earth has seen great progress during the last 19 years. When it entered into the era of big data, Digital Earth developed into a new stage, namely one characterized by ‘Big Earth Data’, confronting new challenges and opportunities. In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development. Then, the opportunities and challenges that Big Earth Data faces are discussed. As a data-intensive scientific research approach, Big Earth Data provides a new vision and methodology to Earth sciences, and the paper identifies the advantages of Big Earth Data to scientific research, especially in knowledge discovery and global change research. We believe that Big Earth Data will advance and promote the development of Digital Earth.},
  file       = {:Guo2016 - Big Earth Data_ a New Challenge and Opportunity for Digital Earth's Development.pdf:PDF},
  groups     = {BigData, Background},
  journal    = {International Journal of Digital Earth},
  keywords   = {prio2, skimmed},
  month      = {dec},
  priority   = {prio2},
  publisher  = {Informa {UK} Limited},
  readstatus = {skimmed},
  year       = {2016},
}

@Article{Anderson2017,
  author       = {Katherine Anderson and Barbara Ryan and William Sonntag and Argyro Kavvada and Lawrence Friedl},
  journaltitle = {{Geo-spatial Information Science}},
  title        = {{Earth observation in service of the 2030 Agenda for Sustainable Development}},
  doi          = {10.1080/10095020.2017.1333230},
  number       = {2},
  pages        = {77-96},
  volume       = {20},
  abstract     = {This paper reviews the key role that Earth Observations (EO) play in achieving the Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document and in monitoring, measuring, and reporting on progress towards the associated targets. This paper also highlights how the Group on Earth Observations (GEO) would contribute to ensure the actual use of EO in support of the 2030 Agenda; and how the Global Earth Observations System of Systems meets requirements for efficient investments in science and technology and a good return on investment, which is elaborated in the Addis Ababa Action Agenda on development financing. Through a number of examples, we first discuss how extensive EO use would: provide a substantial contribution to the achievements of the SDGs by enabling informed decision-making and by allowing monitoring of the expected results; improve national statistics for greater accuracy, by ensuring that the data are “spatially-explicit” and directly contribute to calculate the agreed SDG Targets and Indicators support the fostering of synergy between the SDGs and multilateral environmental agreements by addressing cross-cutting themes such as climate and energy; and facilitate countries’ approaches for working across different development sectors, which is, according to the special adviser on the 2030 Agenda, a key challenge to achieve the SDGs. We then focus on the role that GEO could play in enabling actual use of EO in support of the 2030 Agenda by directly addressing the Strategic Development Goal 17 on partnerships.},
  groups       = {Introduction},
  journal      = {Geo-spatial Information Science},
  keywords     = {skimmed, prio3},
  month        = {apr},
  priority     = {prio3},
  publisher    = {Informa {UK} Limited},
  readstatus   = {skimmed},
  year         = {2017},
}

@Article{Hollmann2013,
  author     = {R. Hollmann and C. J. Merchant and R. Saunders and C. Downy and M. Buchwitz and A. Cazenave and E. Chuvieco and P. Defourny and G. de Leeuw and R. Forsberg and T. Holzer-Popp and F. Paul and S. Sandven and S. Sathyendranath and M. van Roozendael and W. Wagner},
  title      = {{The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables}},
  doi        = {10.1175/bams-d-11-00254.1},
  number     = {10},
  pages      = {1541--1552},
  volume     = {94},
  abstract   = {Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties.
There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together.
This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.},
  groups     = {Introduction},
  journal    = {Bulletin of the American Meteorological Society},
  keywords   = {skimmed, prio3},
  month      = {oct},
  priority   = {prio3},
  publisher  = {American Meteorological Society},
  readstatus = {skimmed},
  year       = {2013},
}

@Article{Eckman2012,
  author     = {Richard S. Eckman and Paul W. Stackhouse},
  title      = {{CEOS contributions to informing energy management and policy decision making using space-based Earth observations}},
  doi        = {10.1016/j.apenergy.2011.03.001},
  number     = {1},
  pages      = {206--210},
  volume     = {90},
  abstract   = {Earth observations are playing an increasingly significant role in informing decision making in the energy sector. In renewable energy applications, space-based observations now routinely augment sparse ground-based observations used as input for renewable energy resource assessment applications. As one of the nine Group on Earth Observations (GEO) societal benefit areas, the enhancement of management and policy decision making in the energy sector is receiving attention in activities conducted by the Committee on Earth Observation Satellites (CEOS). CEOS has become the “space arm” for the implementation of the Global Earth Observation System of Systems (GEOSS) vision. It is directly supporting the space-based, near-term tasks articulated in the GEO three-year work plan. This paper describes a coordinated program of demonstration projects conducted by CEOS member agencies and partners to utilize Earth observations to enhance energy management end-user decision support systems. We discuss the importance of engagement with stakeholders and understanding their decision support needs in successfully increasing the uptake of Earth observation products for societal benefit. Several case studies are presented, demonstrating the importance of providing data sets in formats and units familiar and immediately usable by decision makers. These projects show the utility of Earth observations to enhance renewable energy resource assessment in the developing world, forecast space weather impacts on the power grid, and improve energy efficiency in the built environment.},
  groups     = {Introduction},
  journal    = {Applied Energy},
  keywords   = {prio3, skimmed},
  month      = {feb},
  priority   = {prio3},
  publisher  = {Elsevier {BV}},
  readstatus = {skimmed},
  year       = {2012},
}

@Article{Wulder2012,
  author     = {Michael A. Wulder and Jeffrey G. Masek and Warren B. Cohen and Thomas R. Loveland and Curtis E. Woodcock},
  title      = {{Opening the archive: How free data has enabled the science and monitoring promise of Landsat}},
  doi        = {10.1016/j.rse.2012.01.010},
  pages      = {2--10},
  volume     = {122},
  abstract   = {Landsat occupies a unique position in the constellation of civilian earth observation satellites, with a long and rich scientific and applications heritage. With nearly 40 years of continuous observation – since launch of the first satellite in 1972 – the Landsat program has benefited from insightful technical specification, robust engineering, and the necessary infrastructure for data archive and dissemination. Chiefly, the spatial and spectral resolutions have proven of broad utility and have remained largely stable over the life of the program. The foresighted acquisition and maintenance of a global image archive has proven to be of unmatched value, providing a window into the past and fueling the monitoring and modeling of global land cover and ecological change. In this paper we discuss the evolution of the Landsat program as a global monitoring mission, highlighting in particular the recent change to an open (free) data policy. The new data policy is revolutionizing the use of Landsat data, spurring the creation of robust standard products and new science and applications approaches. Open data access also promotes increased international collaboration to meet the Earth observing needs of the 21st century.},
  groups     = {BigData, Introduction},
  journal    = {Remote Sensing of Environment},
  keywords   = {prio3, skimmed},
  month      = {jul},
  priority   = {prio3},
  publisher  = {Elsevier {BV}},
  readstatus = {skimmed},
  year       = {2012},
}

@Article{Gorelick2017,
  author     = {Noel Gorelick and Matt Hancher and Mike Dixon and Simon Ilyushchenko and David Thau and Rebecca Moore},
  title      = {{Google Earth Engine: Planetary-scale geospatial analysis for everyone}},
  doi        = {10.1016/j.rse.2017.06.031},
  pages      = {18--27},
  volume     = {202},
  abstract   = {Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.},
  groups     = {BigData, Background, EODC - Projects},
  journal    = {Remote Sensing of Environment},
  keywords   = {skimmed, prio2},
  month      = {dec},
  priority   = {prio2},
  publisher  = {Elsevier {BV}},
  readstatus = {skimmed},
  year       = {2017},
}

@Online{EuropeanCommission2013,
  author     = {{European Commission}},
  date       = {2013-07-12},
  title      = {{Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013 supplementing Regulation (EU) No 911/2010 of the European Parliament and of the Council on the European Earth monitoring programme (GMES) by establishing registration and licensing conditions for GMES users and defining criteria for restricting access to GMES dedicated data and GMES service information}},
  url        = {http://data.europa.eu/eli/reg_del/2013/1159/oj},
  urldate    = {2021-01-01},
  groups     = {Introduction},
  keywords   = {prio3, skimmed},
  priority   = {prio3},
  readstatus = {skimmed},
}

@Book{PwC2019,
  author     = {PricewaterhouseCoopers},
  date       = {2019},
  title      = {Copernicus market report},
  doi        = {10.2873/011961},
  isbn       = {978-92-79-98973-5},
  location   = {Luxembourg},
  publisher  = {Publications Office of the European Union},
  url        = {https://op.europa.eu/s/oDKc},
  urldate    = {2021-01-01},
  groups     = {Introduction},
  keywords   = {prio3, skimmed},
  priority   = {prio3},
  readstatus = {skimmed},
}

@Article{Pebesma2017,
  author     = {Pebesma, Edzer and Wagner, Wolfgang and Schramm, Matthias and Von Beringe, Alexandra and Paulik, Christoph and Neteler, Markus and Reiche, Johannes and Verbesselt, Jan and Dries, Jeroen and Goor, Erwin and Mistelbauer, Thomas and Briese, Christian and Notarnicola, Claudia and Monsorno, Roberto and Marin, Carlo and Jacob, Alexander and Kempeneers, Pieter and Soille, Pierre},
  date       = {2017},
  title      = {{OpenEO - a Common, Open Source Interface Between Earth Observation Data Infrastructures and Front-End Applications}},
  doi        = {10.5281/ZENODO.1065474},
  language   = {en},
  copyright  = {Creative Commons Attribution Share Alike 4.0 International},
  groups     = {EODC - Projects, Background},
  keywords   = {Earth observation, cloud computing, API, skimmed, prio3},
  priority   = {prio3},
  publisher  = {Zenodo},
  readstatus = {skimmed},
}

@Article{Picoli2020,
  author       = {M. C. A. Picoli and R. Simoes and M. Chaves and L. A. Santos and A. Sanchez and A. Soares and I. D. Sanches and K. R. Ferreira and G. R. Queiroz},
  date         = {2020-08},
  journaltitle = {{ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences}},
  title        = {{CBERS} {DATA} {CUBE}: A {POWERFUL} {TECHNOLOGY} {FOR} {MAPPING} {AND} {MONITORING} {BRAZILIAN} {BIOMES}},
  doi          = {10.5194/isprs-annals-v-3-2020-533-2020},
  pages        = {533--539},
  volume       = {V-3-2020},
  abstract     = {Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.},
  file         = {:Picoli2020 - CBERS DATA CUBE_ a POWERFUL TECHNOLOGY fOR MAPPING aND MONITORING BRAZILIAN BIOMES.pdf:PDF},
  groups       = {EODC - OpenDataCube, EODC - Use/Impact},
  keywords     = {skimmed},
  publisher    = {Copernicus {GmbH}},
  readstatus   = {skimmed},
}

@InProceedings{Yao2018,
  author     = {Xiaochuang Yao and Yingbo Liu and Qianqian Cao and Junjie Li and Ruihong Huang and Robert Woodcock and Matt Paget and Jian Wang and Guoqing Li},
  booktitle  = {{2018 International Workshop on Big Geospatial Data and Data Science (BGDDS)}},
  date       = {2018-09},
  title      = {{China Data Cube (CDC) for Big Earth Observation Data: Lessons Learned from the Design and Implementation}},
  doi        = {10.1109/bgdds.2018.8626825},
  publisher  = {{IEEE}},
  abstract   = {Earth Observation (EO) data have the macro-level decision and analysis capabilities, which plays a key role in Earth science and significant scientific discoveries [1]. With the rapid development of space technologies, more and more EO data are accessed freely and shared openly. However, it is increasingly difficult, also more complex, to mine the information and knowledge among these massive-temporal datasets. EO Data Cubes (DC) provides a best-of-breed technology to build a spatial data infrastructure to fill this gap [2]. Among them, Australian Geoscience Data Cube (AGDC) [3, 4], as the leader and contributor, has supported data processing and analytical capability by dividing and restructuring grids. Following this open source software and work experience, we design and develop China Data Cube (CDC) system in recent years. Based on the new OGC DGGS standard and cloud computing technologies, CDC has got better system performance, more EO data types and richer local application cases. According to current and future months of work, this paper will describe and share the lessons learned from design and implement of CDC system. It will include following several aspects: 1) Overview of CDC system; 2) parallel EO data ingestion technology; 3) Cloud based EO data storage strategy; 4) Extension of more EO data types, especially China EO data; 5) The future work for CDC.},
  file       = {:Yao2018 - China Data Cube (CDC) for Big Earth Observation Data_ Lessons Learned from the Design and Implementation.pdf:PDF},
  groups     = {EODC - OpenDataCube},
  keywords   = {read},
  readstatus = {read},
}

@InProceedings{ArizaPorras2017,
  author    = {Ariza-Porras, Christian and Bravo, Germ{\'a}n and Villamizar, Mario and Moreno, Andr{\'e}s and Castro, Harold and Galindo, Gustavo and Cabera, Edersson and Valbuena, Saralux and Lozano, Pilar},
  booktitle = {{Advances in Computing}},
  date      = {2017},
  title     = {{CDCol: A Geoscience Data Cube that Meets Colombian Needs}},
  editor    = {Solano, Andr{\'e}s and Ordo{\~{n}}ez, Hugo},
  isbn      = {978-3-319-66562-7},
  location  = {Cham},
  pages     = {87--99},
  publisher = {Springer International Publishing},
  abstract  = {Environmental analysts and researchers' time is an expensive and scarce resource that should be used efficiently. Creating analysis products from remote sensing images involves several steps that take time and can be either automatized or centralized. Among all these steps, product's lineage and reproducibility must be assured. We present CDCol, a geoscience data cube that addresses these concerns and fits the analysis needs of Colombian institutions, the forest and carbon monitoring system.},
  groups    = {EODC - OpenDataCube},
}

@InProceedings{Bravo2017,
  author    = {Bravo, Germ{\'a}n and Castro, Harold and Moreno, Andr{\'e}s and Ariza-Porras, Christian and Galindo, Gustavo and Cabrera, Edersson and Valbuena, Saralux and Lozano-Rivera, Pilar},
  booktitle = {{Advances in Computing}},
  date      = {2017},
  title     = {{Architecture for a Colombian Data Cube Using Satellite Imagery for Environmental Applications}},
  editor    = {Solano, Andr{\'e}s and Ordo{\~{n}}ez, Hugo},
  isbn      = {978-3-319-66562-7},
  location  = {Cham},
  pages     = {227--241},
  publisher = {Springer International Publishing},
  abstract  = {SOLAP data cubes are a main tool to help on the processing of satellite imagery. This article presents the work developed for adapting an existing SOLAP Data Cube to the current Colombian Protocol for processing satellite images to analyze deforestation. We studied different technological alternatives to support such protocol and extend the capabilities of the Australian data cube to include the whole analysis process. In this way, it is possible for different institutions to produce and consume information to/from the data cube allowing the standardization of such information. In consequence, an institution can generate new results based on previous information generated for another institution, keeping the associated metadata along the whole process. This paper introduces the defined architecture, a first implementation and the first results obtained by IDEAM, the Colombian official institution responsible for the monitoring of deforestation in the country.},
  file      = {:Bravo2017 - Architecture for a Colombian Data Cube Using Satellite Imagery for Environmental Applications.pdf:PDF},
  groups    = {EODC - OpenDataCube, Introduction},
}

@Article{Qiu2018,
  author       = {Shi Qiu and Yukun Lin and Rong Shang and Junxue Zhang and Lei Ma and Zhe Zhu},
  date         = {2018-12},
  journaltitle = {{Remote Sensing}},
  title        = {{Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data}},
  doi          = {10.3390/rs11010051},
  number       = {1},
  pages        = {51},
  volume       = {11},
  abstract     = {Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis.},
  groups       = {Optical},
  keywords     = {prio3},
  priority     = {prio3},
  publisher    = {{MDPI} {AG}},
}

@Article{Ferreira2020,
  author       = {K. R. Ferreira and G. R. Queiroz and L. Vinhas and R. F. B. Marujo and R. E. O. Simoes and Michelle C. A. Picoli and Gilberto Camara and Ricardo Cartaxo and Vitor C. F. Gomes and Lorena A. Santos and Alber H. Sanchez and Jeferson S. Arcanjo and Jos{\'{e}} Guilherme Fronza and Carlos Alberto Noronha and Raphael W. Costa and Matheus C. Zaglia and Fabiana Zioti and Thales S. Korting and Anderson R. Soares and Michel E. D. Chaves and Leila M. G. Fonseca},
  date         = {2020-12},
  journaltitle = {{Remote Sensing}},
  title        = {{Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products}},
  doi          = {10.3390/rs12244033},
  number       = {24},
  pages        = {4033},
  volume       = {12},
  abstract     = {Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.},
  file         = {:Ferreira2020 - Earth Observation Data Cubes for Brazil_ Requirements, Methodology and Products.pdf:PDF},
  groups       = {EODC - OpenDataCube, Background},
  keywords     = {prio1, read},
  priority     = {prio1},
  publisher    = {{MDPI} {AG}},
  readstatus   = {read},
}

@Article{Scheffler2020,
  author       = {Daniel Scheffler and David Frantz and Karl Segl},
  date         = {2020-05},
  journaltitle = {{Remote Sensing of Environment}},
  title        = {{Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors}},
  doi          = {10.1016/j.rse.2020.111723},
  pages        = {111723},
  volume       = {241},
  abstract     = {TL;DR: In this paper, we compare the effect of multivariate spectral harmonization techniques on the inter-sensor reflectance consistency and derived products such as spectral indices or land cover classifications.},
  file         = {:Scheffler2020 - Spectral Harmonization and Red Edge Prediction of Landsat 8 to Sentinel 2 Using Land Cover Optimized Multivariate Regressors.pdf:PDF},
  groups       = {Optical, Discussion},
  keywords     = {skimmed, prio3},
  priority     = {prio3},
  publisher    = {Elsevier {BV}},
  readstatus   = {skimmed},
}

@Article{Zekoll2021,
  author       = {Viktoria Zekoll and Magdalena Main-Knorn and Jerome Louis and David Frantz and Rudolf Richter and Bringfried Pflug},
  date         = {2021-01},
  journaltitle = {{Remote Sensing}},
  title        = {{Comparison of Masking Algorithms for Sentinel-2 Imagery}},
  doi          = {10.3390/rs13010137},
  number       = {1},
  pages        = {137},
  volume       = {13},
  abstract     = {Masking of clouds, cloud shadow, water and snow/ice in optical satellite imagery is an important step in automated processing chains. We compare the performance of the masking provided by Fmask (“Function of mask” implemented in FORCE), ATCOR (“Atmospheric Correction”) and Sen2Cor (“Sentinel-2 Correction”) on a set of 20 Sentinel-2 scenes distributed over the globe covering a wide variety of environments and climates. All three methods use rules based on physical properties (Top of Atmosphere Reflectance, TOA) to separate clear pixels from potential cloud pixels, but they use different rules and class-specific thresholds. The methods can yield different results because of different definitions of the dilation buffer size for the classes cloud, cloud shadow and snow. Classification results are compared to the assessment of an expert human interpreter using at least 50 polygons per class randomly selected for each image. The class assignment of the human interpreter is considered as reference or “truth”. The interpreter carefully assigned a class label based on the visual assessment of the true color and infrared false color images and additionally on the bottom of atmosphere (BOA) reflectance spectra. The most important part of the comparison is done for the difference area of the three classifications considered. This is the part of the classification images where the results of Fmask, ATCOR and Sen2Cor disagree. Results on difference area have the advantage to show more clearly the strengths and weaknesses of a classification than results on the complete image. The overall accuracy of Fmask, ATCOR, and Sen2Cor for difference areas of the selected scenes is 45%, 56%, and 62%, respectively. User and producer accuracies are strongly class- and scene-dependent, typically varying between 30% and 90%. Comparison of the difference area is complemented by looking for the results in the area where all three classifications give the same result. Overall accuracy for that “same area” is 97% resulting in the complete classification in overall accuracy of 89%, 91% and 92% for Fmask, ATCOR and Sen2Cor respectively.},
  file         = {:Zekoll2021 - Comparison of Masking Algorithms for Sentinel 2 Imagery.pdf:PDF},
  groups       = {Optical},
  publisher    = {{MDPI} {AG}},
}

@Article{Rufin2020,
  author       = {Philippe Rufin and David Frantz and Lin Yan and Patrick Hostert},
  date         = {2020},
  journaltitle = {{IEEE Geoscience and Remote Sensing Letters}},
  title        = {{Operational Coregistration of the Sentinel-2A/B Image Archive Using Multitemporal Landsat Spectral Averages}},
  doi          = {10.1109/lgrs.2020.2982245},
  pages        = {1--5},
  abstract     = {Geometric misalignment between Landsat and Sentinel-2 data sets as well as multitemporal inconsistency of Sentinel-2A and -2B data sets currently complicate multitemporal analyses. Operational coregistration of Sentinel-2A and -2B imagery is thus required. We present a modification of the established Landsat Sentinel Registration (LSReg) algorithm. The modifications enabled LSReg to be included in an operational preprocessing workflow to automatically coregister large volumes of Sentinel-2 imagery with Landsat base images that represent multiannual monthly spectral average values. The modified LSReg was tested for the complete Sentinel-2 archive covering Crete, Greece, which is a particularly challenging region due to steep topographic gradients and high shares of water in Sentinel-2 tiles. A coregistration success rate of 87.5% of all images was obtained with a mean coregistration precision of 4.4 m. The mean shifts of 14.0 m in the x-direction and 13.4 m in the y-direction before coregistration were found, with maxima exceeding four pixels. Time series noise in locations with land cover transitions (n = 585) was effectively reduced by 43% using the presented approach. The multitemporal geometric consistency of the Sentinel-2 data set was substantially improved, thus enabling time series analyses within the Sentinel-2 data record, as well as integrated Landsat and Sentinel-2A and -2B data sets. The modified algorithm is implemented in the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) version 3.0 (https://github.com/davidfrantz/force).},
  file         = {:Rufin2020 - Operational Coregistration of the Sentinel 2A_B Image Archive Using Multitemporal Landsat Spectral Averages.pdf:PDF},
  groups       = {Optical, Discussion},
  publisher    = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{Younes2021,
  author       = {Nicolas Younes and Karen E. Joyce and Stefan W. Maier},
  date         = {2021-05},
  journaltitle = {{International Journal of Applied Earth Observation and Geoinformation}},
  title        = {All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia},
  doi          = {10.1016/j.jag.2020.102285},
  pages        = {102285},
  volume       = {97},
  abstract     = {Satellite-derived phenology (or apparent phenology) is frequently used to illustrate changes in plant phenology (i.e. true phenology) and the effects of climate forcing. However, each study uses a different method to detect phenology. Plant phenology refers to the relationship between the life cycle of plants and weather and climate events. Phenology is often studied in the field, but recently studies have transitioned towards using satellite images to monitor phenology at the plot, country, and continental scales. The problem with this approach is that there is an ever-increasing variety of earth observation satellites collecting data with different spatial, spectral, and temporal characteristics. In this paper we ask if studies that detect phenology using different sensors over the same site produce comparable results. Mangrove forests are one example where different methods have been used to examine their apparent phenology. In general, plant phenology, including mangroves, is described using few individual plants, but continental-scale descriptions of phenological events are scarce or inexistent. Few attempts have been made to describe the phenology of mangroves using satellite imagery, and each study presents a different method. We hypothesize that apparent phenology changes with: 1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of cloud contaminated observations. Intuitively, one would assume that these hypotheses hold true, yet few studies have investigated this. For example, one would expect that clouds change the observed phenology of vegetation, that the number of species captured at spatial resolution will impact the apparent phenology, or that mangroves in different places display different phenologies, but how are these changes represented in the apparent phenology? We use the Enhanced Vegetation Index (EVI) to examine the changes in the start of season and peak growing season dates, as well as the shape and amplitude of the apparent phenology in each hypothesis. We use Landsat and Sentinel 2 imagery over the mangrove forests in Darwin Harbour (Northern Territory, Australia) as a case study, and found that apparent phenology does change with the sensor, site, and cloud contamination. Importantly, the apparent phenology is comparable between Landsat and Sentinel 2 sensors, but it is not comparable to phenology derived from MODIS. This is due to differences in the spatial resolution of the sensors. Cloud contamination also significantly changes the apparent phenology of vegetation. In this paper we expose the complexity of modelling phenology with remote sensing and help guide future phenology investigations.},
  groups       = {Roda usecase},
  keywords     = {prio2, skimmed},
  priority     = {prio2},
  publisher    = {Elsevier {BV}},
  readstatus   = {skimmed},
}

@Article{Puhm2020,
  author       = {Martin Puhm and Janik Deutscher and Manuela Hirschmugl and Andreas Wimmer and Ursula Schmitt and Mathias Schardt},
  date         = {2020-09},
  journaltitle = {{Remote Sensing}},
  title        = {{A Near Real-Time Method for Forest Change Detection Based on a Structural Time Series Model and the Kalman Filter}},
  doi          = {10.3390/rs12193135},
  number       = {19},
  pages        = {3135},
  volume       = {12},
  abstract     = {The increasing availability of dense time series of earth observation data has incited a growing interest in time series analysis for vegetation monitoring and change detection. Vegetation monitoring algorithms need to deal with several time series characteristics such as seasonality, irregular sampling intervals, and signal artefacts. While common algorithms based on deterministic harmonic regression models account for intra-annual seasonality, inter-annual variations of the seasonal pattern related to shifts in vegetation phenology due to different temperature and rainfall are usually not accounted for. We propose a transition to stochastic modelling and present a near real-time change detection method that combines a structural time series model with the Kalman filter. The model continuously adapts to new observations and allows to better separate phenology-related deviations from vegetation anomalies or land cover changes. The method is tested in a forest change detection application aiming at the assessment of damages caused by storm events and insect calamities. Forest changes are detected based on the cumulative sum control chart (CUSUM) which is used to decide if new observations deviate from model-based forecasts. The performance is evaluated in two test sites, one in Malawi (dry tropical forest) and one in Austria (temperate deciduous, coniferous and mixed forests) based on Sentinel-2 time series. Both forest areas are characterized by a distinct, but temporally varying leaf-off season. The presented change detection method shows overall accuracies above 99%, users’ accuracies of 76.8% to 88.6%, and producers’ accuracies of 68.2% to 80.4% for the forest change stratum (minimum mapping unit: 0.1 ha). Results are based on visually interpreted points derived by stratified random sampling. A further analysis revealed that increasing the time series density by merging data from two Sentinel-2 orbits yields better forest change detection accuracies in comparison to using data from one orbit only. The resulting increase in users’ accuracy amounts to 7.6%. The presented method is capable of near real-time processing and could be used for a variety of automated forest monitoring applications.},
  file         = {:Puhm2020 - A near Real Time Method for Forest Change Detection Based on a Structural Time Series Model and the Kalman Filter.pdf:PDF},
  groups       = {Roda usecase},
  publisher    = {{MDPI} {AG}},
}

@Book{Guo2020,
  date       = {2020},
  title      = {Manual of Digital Earth},
  doi        = {10.1007/978-981-32-9915-3},
  editor     = {Huadong Guo and Michael F. Goodchild and Alessandro Annoni},
  publisher  = {Springer Singapore},
  file       = {:Other_Books/Guo2020 - Manual of Digital Earth.pdf:PDF},
  groups     = {BigData},
  keywords   = {prio2, skimmed},
  priority   = {prio2},
  readstatus = {skimmed},
}

@Article{Abernathey2020,
  author       = {Ryan Abernathey and Tom Augspurger and Anderson Banihirwe and Charles C. Blackmon-Luca and Timothy J. Crone and Chelle L. Gentemann and Joseph J. Hamman and Naomi Henderson and Chiara Lepore and Theo A. Mccaie and Niall H. Robinson and Richard P. Signell},
  date         = {2020-11},
  journaltitle = {{Computing in Science & Engineering}},
  title        = {{Cloud-Native Repositories for Big Scientific Data}},
  doi          = {10.22541/au.160443768.88917719/v1},
  issue        = {2},
  pages        = {26-35},
  volume       = {23},
  abstract     = {Scientific data has traditionally been distributed via downloads from data server to
local computer. This way of working suffers from limitations as scientific datasets grow towards the petabyte scale. A “cloud-native data repository,” as defined in this paper, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access & inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.},
  file         = {:Abernathey2020 - Cloud Native Repositories for Big Scientific Data.pdf:PDF},
  groups       = {BigData, Background, Discussion},
  keywords     = {prio1},
  priority     = {prio1},
  publisher    = {Authorea, Inc.},
}

@Article{Diaz2012,
  author       = {Laura Diaz and Albert Remke},
  date         = {2012-11},
  journaltitle = {{International Journal of Spatial Data Infrastructures Research}},
  title        = {{Future SDI – Impulses from Geoinformatics Research and IT Trends}},
  doi          = {10.2902/1725-0463.2012.07.art18},
  issn         = {1725-0463},
  number       = {7},
  pages        = {378--410},
  abstract     = {The term Spatial Data Infrastructure (SDI) was defined in the nineties as a set of policies, technologies and institutional arrangements for improving the availability
and accessibility of spatial data and information. SDIs are typically driven by
governmental organizations, and thus follow top-down structures based on
regulations and agreements. The drawback is that it renders SDIs less easily capable of evolving with new technological trends. While organizations are still
struggling to implement SDIs, the World Wide Web is increasingly developing into
a Geospatial Web, i.e. one that extensively supports the spatial and temporal
aspects of information. This article is our contribution to the discussion on the
future technological directions in the field of SDIs. We give a conceptual view of
the dynamics of both SDIs and the Geospatial Web. We present a picture of the
SDI of the future, one which benefits from these developments, based on an
analysis of geoinformatics research topics and current ICT trends. We provide
recommendations on how to improve the adaptability and usability of SDIs as to
facilitate the assimilation of new ICT developments and to leverage selfreinforcing growth.},
  file         = {:Diaz2012 - Future SDI – Impulses from Geoinformatics Research and IT Trends.pdf:PDF},
  groups       = {BigData, Background},
  place        = {IT},
  publisher    = {Publications Office of the European Union},
}

@Article{Gore1998,
  author       = {Al Gore},
  date         = {1998-06},
  journaltitle = {{Australian Surveyor}},
  title        = {{The Digital Earth}},
  doi          = {10.1080/00050348.1998.10558728},
  number       = {2},
  pages        = {89--91},
  volume       = {43},
  file         = {:Gore1998 - The Digital Earth.pdf:PDF},
  groups       = {BigData, Background},
  keywords     = {prio3},
  priority     = {prio3},
  publisher    = {Informa {UK} Limited},
}

@InCollection{Schade2019,
  author    = {Sven Schade and Carlos Granell and Glenn Vancauwenberghe and Carsten Ke{\ss}ler and Danny Vandenbroucke and Ian Masser and Michael Gould},
  booktitle = {{Manual of Digital Earth}},
  date      = {2019-11},
  title     = {{Geospatial Information Infrastructures}},
  doi       = {10.1007/978-981-32-9915-3_5},
  pages     = {161--190},
  publisher = {Springer Singapore},
  groups    = {BigData, Background},
  keywords  = {prio3},
  priority  = {prio3},
}

@Article{Baumann2015,
  author       = {Peter Baumann and Paolo Mazzetti and Joachim Ungar and Roberto Barbera and Damiano Barboni and Alan Beccati and Lorenzo Bigagli and Enrico Boldrini and Riccardo Bruno and Antonio Calanducci and Piero Campalani and Oliver Clements and Alex Dumitru and Mike Grant and Pasquale Herzig and George Kakaletris and John Laxton and Panagiota Koltsida and Kinga Lipskoch and Alireza Rezaei Mahdiraji and Simone Mantovani and Vlad Merticariu and Antonio Messina and Dimitar Misev and Stefano Natali and Stefano Nativi and Jelmer Oosthoek and Marco Pappalardo and James Passmore and Angelo Pio Rossi and Francesco Rundo and Marcus Sen and Vittorio Sorbera and Don Sullivan and Mario Torrisi and Leonardo Trovato and Maria Grazia Veratelli and Sebastian Wagner},
  date         = {2015-03},
  journaltitle = {{International Journal of Digital Earth}},
  title        = {{Big Data Analytics for Earth Sciences: the EarthServer approach}},
  doi          = {10.1080/17538947.2014.1003106},
  number       = {1},
  pages        = {3--29},
  volume       = {9},
  abstract     = {Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.},
  file         = {:Baumann2015 - Big Data Analytics for Earth Sciences_ the EarthServer Approach.pdf:PDF},
  groups       = {EODC - Projects, Background},
  keywords     = {prio3},
  priority     = {prio3},
  publisher    = {Informa {UK} Limited},
}

@Article{BauerMarschallinger2014,
  author       = {Bernhard Bauer-Marschallinger and Daniel Sabel and Wolfgang Wagner},
  date         = {2014-11},
  journaltitle = {Computers {\&} {G}eosciences},
  title        = {Optimisation of global grids for high-resolution remote sensing data},
  doi          = {10.1016/j.cageo.2014.07.005},
  pages        = {84--93},
  volume       = {72},
  abstract     = {Upcoming remote sensing systems onboard satellites will generate unprecedented volumes of spatial data, hence challenging processing facilities in terms of storage and processing capacities. Thus, an efficient handling of remote sensing data is of vital importance, demanding a well-suited definition of spatial grids for the data׳s storage and manipulation. For high-resolution image data, regular grids defined by map projections have been identified as practicable, cognisant of their drawbacks due to geometric distortions. To this end, we defined a new metric named grid oversampling factor (GOF) that estimates local data oversampling appearing during projection of generic satellite images to a regular raster grid. Based on common map projections, we defined sets of spatial grids optimised to minimise data oversampling. Moreover, they ensure that data undersampling cannot occur at any location. From the resulting GOF-values we concluded that equidistant projections are most suitable, with a global mean oversampling of 2% when using a system of seven continental grids (introduced under the name Equi7 Grid). Opposed to previous studies that suggested equal-area projections, we recommend the Plate Carrée, the Equidistant Conic and the Equidistant Azimuthal projection for global, hemispherical and continental grids, respectively.},
  file         = {:BauerMarschallinger2014 - Optimisation of Global Grids for High Resolution Remote Sensing Data.pdf:PDF},
  groups       = {Methods/Results, Data / Data Format / Grid / ...},
  keywords     = {prio1},
  priority     = {prio1},
  publisher    = {Elsevier {BV}},
}

@InProceedings{Truckenbrodt2019a,
  author    = {Truckenbrodt, John and Cremer, Felix and Baris, Ismail and Eberle, Jonas},
  booktitle = {{Proceedings of the 2019 conference on Big Data from Space (BiDS'19)}},
  date      = {2019},
  title     = {{pyroSAR: A Framework for Large-Scale SAR Satellite Data Processing}},
  doi       = {10.2760/848593},
  editor    = {Pierre Soille and S. Loekken and S. Albani},
  language  = {en},
  pages     = {197-200},
  publisher = {{Publications Office of the European Union}},
  url       = {https://op.europa.eu/en/publication-detail/-/publication/7075ed48-3a4d-11e9-8d04-01aa75ed71a1/language-en},
  urldate   = {2021-05-03},
  file      = {:Truckenbrodt2019a - PyroSAR_ a Framework for Large Scale SAR Satellite Data Processing.pdf:PDF},
  groups    = {SAR, ARD, Methods/Results, Software},
  keywords  = {prio2},
  priority  = {prio2},
}

@Article{Ariav1986,
  author       = {Gad Ariav},
  date         = {1986-12},
  journaltitle = {{ACM Transactions on Database Systems}},
  title        = {A temporally oriented data model},
  doi          = {10.1145/7239.7350},
  number       = {4},
  pages        = {499--527},
  volume       = {11},
  abstract     = {The research into time and data models has so far focused on the identification of extensions to the classical relational model that would provide it with “adequate” semantic capacity to deal with time. The temporally oriented data model (TODM) presented in this paper is a result of a different approach, namely, it directly operationalizes the pervasive three-dimensional metaphor for time. One of the main results is thus the development of the notion of the data cube: a three-dimensional and inherently temporal data construct where time, objects, and attributes are the primary dimensions of stored data. TODM's cube adds historical depth to the tabular notions of data and provides a framework for storing and retrieving data within their temporal context. The basic operations in the model allow the formation of new cubic views from existing ones, or viewing data as one moves up and down in time within cubes.

This paper introduces TODM, a consistent set of temporally oriented data constructs, operations, and constraints, and then presents TOSQL, a corresponding end-user's SQL-like query syntax. The model is a restricted but consistent superset of the relational model, and the query syntax incorporates temporal notions in a manner that likewise avoids penalizing users who are interested solely in the current view of data (rather than in a temporal perspective). The naturalness of the spatial reference to time and the added semantic capacity of TODM come with a price—the definitions of the cubic constructs and basic operations are relatively cumbersome. As rudimentary as it is, TODM nonetheless provides a comprehensive basis for formulating an external data model for a temporally oriented database.},
  groups       = {EODC - Concept, Background},
  publisher    = {Association for Computing Machinery ({ACM})},
}

@Article{Egorov2019,
  author       = {Alexey Egorov and David Roy and Hankui Zhang and Zhongbin Li and Lin Yan and Haiyan Huang},
  date         = {2019-02},
  journaltitle = {{Remote Sensing}},
  title        = {{Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring}},
  doi          = {10.3390/rs11040447},
  number       = {4},
  pages        = {447},
  volume       = {11},
  abstract     = {The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous United States (CONUS) are quantified for a 36-year period (1 January 1982 to 31 December 2017). Complex patterns of ARD availability occur due to the satellite orbit and sensor geometry, cloud, sensor acquisition and health issues and because of changing relative orientation of the ARD tiles with respect to the Landsat orbit paths. Quantitative per-pixel and summary ARD tile results are reported. Within the CONUS, the average annual number of non-cloudy observations in each 150 × 150 km ARD tile varies from 0.53 to 16.80 (Landsat 4 TM), 11.08 to 22.83 (Landsat 5 TM), 9.73 to 21.72 (Landsat 7 ETM+) and 14.23 to 30.07 (all three sensors). The annual number was most frequently only 2 to 4 Landsat 4 TM observations (36% of the CONUS tiles), increasing to 14 to 16 Landsat 5 TM observations (26% of tiles), 12 to 14 Landsat 7 ETM+ observations (31% of tiles) and 18 to 20 observations (23% of tiles) when considering all three sensors. The most frequently observed ARD tiles were in the arid south-west and in certain mountain rain shadow regions and the least observed tiles were in the north-east, around the Great Lakes and along parts of the north-west coast. The quality of time series algorithm results is expected to be reduced at ARD tiles with low reported availability. The smallest annual number of cloud-free observations for the Landsat 5 TM are over ARD tile h28v04 (northern New York state), for Landsat 7 ETM+ are over tile h25v07 (Ohio and Pennsylvania) and for Landsat 4 TM are over tile h22v08 (northern Indiana). The greatest annual number of cloud-free observations for the Landsat 5 TM and 7 ETM+ ARD are over southern California ARD tile h04v11 and for the Landsat 4 TM are over southern Arizona tile h06v13. The reported results likely overestimate the number of good surface observations because shadows and cirrus clouds were not considered. Implications of the findings for terrestrial monitoring and future ARD research are discussed.},
  file         = {:Egorov2019 - Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage Over the Conterminous United States and Implications for Terrestrial Monitoring.pdf:PDF},
  groups       = {Optical, ARD},
  publisher    = {{MDPI} {AG}},
}

@Article{Wulder2019,
  author       = {Michael A. Wulder and Thomas R. Loveland and David P. Roy and Christopher J. Crawford and Jeffrey G. Masek and Curtis E. Woodcock and Richard G. Allen and Martha C. Anderson and Alan S. Belward and Warren B. Cohen and John Dwyer and Angela Erb and Feng Gao and Patrick Griffiths and Dennis Helder and Txomin Hermosilla and James D. Hipple and Patrick Hostert and M. Joseph Hughes and Justin Huntington and David M. Johnson and Robert Kennedy and Ayse Kilic and Zhan Li and Leo Lymburner and Joel McCorkel and Nima Pahlevan and Theodore A. Scambos and Crystal Schaaf and John R. Schott and Yongwei Sheng and James Storey and Eric Vermote and James Vogelmann and Joanne C. White and Randolph H. Wynne and Zhe Zhu},
  date         = {2019-05},
  journaltitle = {{Remote Sensing of Environment}},
  title        = {{Current status of Landsat program, science, and applications}},
  doi          = {10.1016/j.rse.2019.02.015},
  pages        = {127--147},
  volume       = {225},
  abstract     = {Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality.

Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat.},
  file         = {:Wulder2019 - Current Status of Landsat Program, Science, and Applications.pdf:PDF},
  groups       = {Optical, ARD, Background},
  keywords     = {prio3},
  priority     = {prio3},
  publisher    = {Elsevier {BV}},
}

@Article{Dwyer2018,
  author       = {John Dwyer and David Roy and Brian Sauer and Calli Jenkerson and Hankui Zhang and Leo Lymburner},
  date         = {2018-08},
  journaltitle = {{Remote Sensing}},
  title        = {{Analysis Ready Data: Enabling Analysis of the Landsat Archive}},
  doi          = {10.3390/rs10091363},
  number       = {9},
  volume       = {10},
  abstract     = {Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.},
  file         = {:Dwyer2018 - Analysis Ready Data_ Enabling Analysis of the Landsat Archive.pdf:PDF},
  groups       = {ARD, Optical, Background, Methods/Results},
  keywords     = {prio3},
  priority     = {prio3},
  publisher    = {{MDPI} {AG}},
}

@Article{Potapov2020,
  author       = {Peter Potapov and Matthew C. Hansen and Indrani Kommareddy and Anil Kommareddy and Svetlana Turubanova and Amy Pickens and Bernard Adusei and Alexandra Tyukavina and Qing Ying},
  date         = {2020-01},
  journaltitle = {{Remote Sensing}},
  title        = {{Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping}},
  doi          = {10.3390/rs12030426},
  number       = {3},
  pages        = {426},
  volume       = {12},
  abstract     = {The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.},
  file         = {:Potapov2020 - Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping.pdf:PDF},
  groups       = {ARD, Optical},
  publisher    = {{MDPI} {AG}},
}

@Article{Laney2001,
  author    = {Laney, Doug},
  title     = {{3D data management: Controlling data volume, velocity and variety}},
  number    = {70},
  pages     = {1},
  volume    = {6},
  groups    = {Introduction},
  journal   = {META group research note},
  publisher = {Stanford},
  year      = {2001},
}

@InProceedings{Kellogg2020,
  author    = {Kent Kellogg and Pamela Hoffman and Shaun Standley and Scott Shaffer and Paul Rosen and Wendy Edelstein and Charles Dunn and Charles Baker and Phillip Barela and Yuhsyen Shen and Ana Maria Guerrero and Peter Xaypraseuth and V. Raju Sagi and C. V. Sreekantha and Nandini Harinath and Raj Kumar and Rakesh Bhan and C. V. H. S. Sarma},
  booktitle = {{2020 IEEE Aerospace Conference}},
  date      = {2020-03},
  title     = {{NASA-ISRO Synthetic Aperture Radar (NISAR) Mission}},
  doi       = {10.1109/aero47225.2020.9172638},
  pages     = {1-21},
  publisher = {{IEEE}},
  groups    = {SAR, Background},
}

@Article{Hilbert2011,
  author       = {M. Hilbert and P. Lopez},
  date         = {2011-02},
  journaltitle = {Science},
  title        = {{The World’s Technological Capacity to Store, Communicate, and Compute Information}},
  doi          = {10.1126/science

branch_name: refs/heads/main