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Big Earth Data Volume 7, 2023 - Issue 3: Emerging trends in big Earth data management and analysis
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Secondary Literature Review Article

Think global, cube local: an Earth Observation Data Cube’s contribution to the Digital Earth vision

Martin Sudmannsa Department of Geoinformatics, Paris Lodron University Salzburg, Salzburg, AustriaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-0473-1260View further author information
ORCID Icon,
Hannah Augustina Department of Geoinformatics, Paris Lodron University Salzburg, Salzburg, AustriaORCID Iconhttps://orcid.org/0000-0002-3334-5350View further author information
ORCID Icon,
Brian Killoughb NASA Langley Research Center, Hampton, VA, USAView further author information
,
Gregory Giulianic Institute for Environmental Sciences, University of Geneva, enviroSPACE, Geneva, Switzerland;d Institute for Environmental Sciences, University of Geneva, GRID-Geneva, Geneva, SwitzerlandORCID Iconhttps://orcid.org/0000-0002-1825-8865View further author information
ORCID Icon,
Dirk Tiedea Department of Geoinformatics, Paris Lodron University Salzburg, Salzburg, AustriaORCID Iconhttps://orcid.org/0000-0002-5473-3344View further author information
ORCID Icon,
Alex Leithe Geoscience Australia, Canberra, ACT, AustraliaView further author information
,
Fang Yuane Geoscience Australia, Canberra, ACT, AustraliaORCID Iconhttps://orcid.org/0000-0001-8315-4176View further author information
ORCID Icon &
Adam Lewisf Group on Earth Observations (GEO) Digital Earth Africa, Geneva, SwitzerlandView further author information
 show all
Pages 831-859 | Received 28 Feb 2022, Accepted 01 Jul 2022, Published online: 21 Jul 2022

References

  • Amazon. (2021). Climate Next: How data and community can save Zanzibar’s mangroves. Retrieved from https://www.aboutamazon.com/news/aws/climate-next-how-data-and-community-can-save-zanzibars-mangroves
  • Amazon. (2022). Amazon Sustainability Data Initiative (ASDI). Retrieved from https://sustainability.aboutamazon.com/environment/the-cloud/asdi
  • Anderson, K., Ryan, B., Sonntag, W., Kavvada, A., & Friedl, L. (2017). Earth observation in service of the 2030 agenda for sustainable development. Geo-Spatial Information Science, 20(2), 77–96. doi:10.1080/10095020.2017.1333230
  • Andries, A., Morse, S., Murphy, R., Lynch, J., Woolliams, E., & Fonweban, J. (2019). Translation of Earth observation data into sustainable development indicators: An analytical framework. Sustainable Development, 27(3), 366–376. doi:10.1002/sd.1908
  • Aschbacher, J., & Milagro-Pérez, M. P. (2012). The European Earth monitoring (GMES) programme: Status and perspectives. Remote Sensing of Environment, 120, 3–8. doi:10.1016/j.rse.2011.08.028
  • Augustin, H., Sudmanns, M., Tiede, D., Lang, S., & Baraldi, A. (2019). Semantic earth observation data cubes. Data, 4(3), 102. doi:10.3390/data4030102
  • Barras, A. G., Braunisch, V., Arlettaz, R., & Maiorano, L. (2021). Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change. Diversity & Distributions, 27(6), 989–1004. doi:10.1111/ddi.13247
  • Baumann, P., Dehmel, A., Furtado, P., Ritsch, R., & Widmann, N. (1998). The multidimensional database system RasDaman. ACM SIGMOD Record, 27(2), 575–577. doi:10.1145/276305.276386
  • Baumann, P., Mazzetti, P., Ungar, J., Barbera, R., Barboni, D., Beccati, A. … Wagner, S. (2016). Big data analytics for earth sciences: The earthserver approach. International Journal of Digital Earth, 9(1), 3–29. doi:10.1080/17538947.2014.1003106
  • Burgelman, J.-C., Pascu, C., Szkuta, K., Von Schomberg, R., Karalopoulos, A., Repanas, K., & Schouppe, M. (2019). Open science, open data, and open scholarship: European policies to make science fit for the twenty-first century. Frontiers in Big Data, 2, 43. doi:10.3389/fdata.2019.00043
  • Burton, C., Yuan, F., Ee-Faye, C., Halabisky, M., Ongo, D., Mar, F., … Adimou, S. (2021, December 13–17). Co-Production of a 10-m cropland extent map for continental Africa using Sentinel-2, Cloud computing, and the open-data-cube. In Geography. American Geophysical Union Fall Meeting 2021, New Orleans, LA, USA. doi:10.1002/essoar.10510081.1
  • Chatenoux, B., Richard, J.-P., Small, D., Roeoesli, C., Wingate, V., Poussin, C., Rodila, D., Peduzzi, P., Steinmeier, C.,Ginzler, C., Psomas, A., Schaepman, M. E., & Giuliani, G. (2021). The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Scientific Data, 8(1), 295. doi:10.1038/s41597-021-01076-6
  • Cloudscene. (2022). Number of data centers worldwide in 2022, by country. Statista. Retrieved from https://www.statista.com/statistics/1228433/data-centers-worldwide-by-country/
  • Craglia, M., de Bie, K., Jackson, D., Pesaresi, M., Remetey-Fülöpp, G., Wang, C. … Woodgate, P. (2012). Digital Earth 2020: Towards the vision for the next decade. International Journal of Digital Earth, 5(1), 4–21. doi:10.1080/17538947.2011.638500
  • Craglia, M., & Nativi, S. (2018). Mind the Gap: Big Data vs. interoperability and reproducibility of science. In P.-P. Mathieu & C. Aubrecht (Eds.), Earth observation open science and innovation (pp. 121–141). Springer International Publishing. doi:10.1007/978-3-319-65633-5_6
  • CSIRO. (2022). Earth analytics science and innovation (EASI) platform. Retrieved from https://research.csiro.au/cceo/underpinning-technologies/earth-analytics/
  • Davis, D. S., Buffa, D., Rasolondrainy, T., Creswell, E., Anyanwu, C., Ibirogba, A., Randolph, C., Ouarghidi, A., Phelps, L. N.,Lahiniriko, F., Chrisostome, Z. M., Manahira, G., & Douglass, K. (2021). The aerial panopticon and the ethics of archaeological remote sensing in sacred cultural spaces. Archaeological Prospection, 28(3), 305–320. doi:10.1002/arp.1819
  • Dhu, T., Giuliani, G., Juárez, J., Kavvada, A., Killough, B., Merodio, P. … Ramage, S. (2019). National open data cubes and their contribution to country-level development policies and practices. Data, 4(4), 144. doi:10.3390/data4040144
  • Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H., & Lymburner, L. (2018). Analysis ready data: Enabling analysis of the landsat archive. Remote Sensing, 10(9), 1363. doi:10.3390/rs10091363
  • European Commission. Directorate General for Internal Market, Industry, Entrepreneurship and SMEs. & PwC. (2019). Copernicus market report: February 2019. Issue 2. Publications Office. https://data.europa.eu/doi/10.2873/011961
  • European Commission. Directorate General for Research and Innovation. (2018). Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data. Publications Office. https://data.europa.eu/doi/10.2777/1524
  • European Commission. Joint Research Centre. (2017). Proceedings of the 2017 conference on Big Data from Space (BIDS’ 2017): 28th 30th November 2017 Toulouse (France). Publications Office. https://data.europa.eu/doi/10.2760/383579
  • The European Parliament and the council of the European Union. (2019). Directive 2019/1024 of the European Parliament and of the council on open data and the re-use of public sector information. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32019L1024&from=EN
  • European Union Agency for the Space Programme (EUSPA). (2022). EO and GNSS Market Report. Publications Office of the European Union. Retrieved February 17, 2022, from https://www.euspa.europa.eu/sites/default/files/uploads/euspa_market_report_2022.pdf
  • Gavin, D., Dhu, T., Sagar, S., Mueller, N., Dunn, B., Lewis, A., … Thankappan, M. (2018). Digital Earth Australia—From Satellite Data to Better Decisions. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8633–8635). doi:10.1109/IGARSS.2018.8518160
  • Giuliani, G., Chatenoux, B., De Bono, A., Rodila, D., Richard, J.-P., Allenbach, K., Dao, H., & Peduzzi, P. (2017). Building an Earth Observations Data Cube: Lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD). Big Earth Data, 1(1–2), 100–117. doi:10.1080/20964471.2017.1398903
  • Giuliani, G., Chatenoux, B., Honeck, E., & Richard, J.-P. (2018). Towards Sentinel-2 analysis ready data: A Swiss data cube perspective. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8659–8662). doi:10.1109/IGARSS.2018.8517954
  • Giuliani, G., Camara, G., Killough, B., & Minchin, S. (2019a). Earth observation open science: enhancing reproducible science using data cubes. Data, 4(4), 147. doi:10.3390/data4040147
  • Giuliani, G., Masó, J., Mazzetti, P., Nativi, S., & Zabala, A. (2019b). Paving the way to increased interoperability of earth observations data cubes. Data, 4(3), 113. doi:10.3390/data4030113
  • Giuliani, G., Chatenoux, B., Benvenuti, A., Lacroix, P., Santoro, M., & Mazzetti, P. (2020a). Monitoring land degradation at national level using satellite earth observation time-series data to support SDG15 – exploring the potential of data cube. Big Earth Data, 4(1), 3–22. doi:10.1080/20964471.2020.1711633
  • Giuliani, G., Egger, E., Italiano, J., Poussin, C., Richard, J.-P., & Chatenoux, B. (2020b). Essential variables for environmental monitoring: What are the possible contributions of earth observation data cubes? Data, 5(4), 100. doi:10.3390/data5040100
  • Giuliani, G., Cazeaux, H., Burgi, P.-Y., Poussin, C., Richard, J.-P., & Chatenoux, B. (2021). SwissEnveo: A FAIR national environmental data repository for earth observation open science. Data Science Journal, 20(1), 22. doi:10.5334/dsj-2021-022
  • Gomes, V., Queiroz, G., & Ferreira, K. (2020). An overview of platforms for big earth observation data management and analysis. Remote Sensing, 12(8), 1253. doi:10.3390/rs12081253
  • Google. (2021). Helping companies tackle climate change with Earth Engine. Retrieved from https://blog.google/products/earth/earth-engine-preview-google-cloud/
  • Gore, A. (1998). The digital earth: Understanding our planet in the 21st Century. Australian Surveyor, 43(2), 89–91. doi:10.1080/00050348.1998.10558728
  • Grainger, A. (2017). Citizen observatories and the new earth observation science. Remote Sensing, 9(2), 153. doi:10.3390/rs9020153
  • Halabisky, M., Mubea, K., Mar, F., Yuan, F., Burton, C., Birchall, E., Fouladi Moghaddam, N., Adimou, G., Mamane, B., Ongo, D., Boamah, E., Chong, E.-F., Gandhi, N., Leith, A., Hall, L., & Lewis, A. (2021, December 13–17). Water Observations from Space: Accurate maps of surface water through time for the continent of Africa. In American Geophysical Union Fall Meeting 2021, New Orleans, LA, USA. doi:10.1002/essoar.10510203.1
  • Hargreaves, P. K., & Watmough, G. R. (2021). Satellite Earth observation to support sustainable rural development. International Journal of Applied Earth Observation and Geoinformation, 103, 102466. doi:10.1016/j.jag.2021.102466
  • Hofer, B., Lang, S., & Ferber, N. (2020). Future occupational profiles in Earth observation and geoinformation—scenarios resulting from changing workflows. In P. Kyriakidis, D. Hadjimitsis, D. Skarlatos, & A. Mansourian (Eds.), Geospatial technologies for local and regional development (pp. 349–366). Springer International Publishing. doi:10.1007/978-3-030-14745-7_19
  • Honeck, E., Castello, R., Chatenoux, B., Richard, J.-P., Lehmann, A., & Giuliani, G. (2018). From a vegetation index to a sustainable development goal indicator: forest trend monitoring using three decades of earth observations across Switzerland. ISPRS International Journal of Geo-Information, 7(12), 455. doi:10.3390/ijgi7120455
  • Hoyer, S., & Hamman, J. J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1), 10. doi:10.5334/jors.148
  • Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021). Data sovereignty: A review. Big Data & Society, 8(1), 2053951720982012. doi:10.1177/2053951720982012
  • Janowicz, K., & Hitzler, P. (2017). Geospatial semantic web. In D. Richardson, N. Castree, M. F. Goodchild, A. Kobayashi, W. Liu, & R. A. Marston (Eds.), International encyclopedia of geography: people, the earth, environment and technology (pp. 1–6). John Wiley & Sons, Ltd. doi:10.1002/9781118786352.wbieg1158
  • Kavvada, A., Metternicht, G., Kerblat, F., Mudau, N., Haldorson, M., Laldaparsad, S., Friedl, L., Held, A., & Chuvieco, E. (2020). Towards delivering on the Sustainable Development Goals using Earth observations. Remote Sensing of Environment, 247, 111930. doi:10.1016/j.rse.2020.111930
  • Kganyago, M., & Mhangara, P. (2019). The role of African emerging space agencies in earth observation capacity building for facilitating the implementation and monitoring of the African development agenda: The case of African earth observation program. ISPRS International Journal of Geo-Information, 8(7), 292. doi:10.3390/ijgi8070292
  • Killough, B., Siqueira, A., & Dyke, G. (2020). Advancements in the open data cube and analysis ready data—past, present and future. In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 3373–3375). doi:10.1109/IGARSS39084.2020.9324712
  • Knoth, C., & Nüst, D. (2017). Reproducibility and practical adoption of GEOBIA with open-source software in docker containers. Remote Sensing, 9(3), 290. doi:10.3390/rs9030290
  • Koers, H., Bangert, D., Hermans, E., van Horik, R., de Jong, M., & Mokrane, M. (2020). Recommendations for services in a FAIR data ecosystem. Patterns, 1(5), 100058. doi:10.1016/j.patter.2020.100058
  • Kumar, L., & Mutanga, O. (2018). Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509. doi:10.3390/rs10101509
  • Lewis, A., Lymburner, L., Purss, M. B. J., Brooke, B., Evans, B., Ip, A., Dekker, A. G., Irons, J. R., Minchin, S.,Mueller, N., Oliver, S., Roberts, D., Ryan, B., Thankappan, M., Woodcock, R., & Wyborn, L. (2016). Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube. International Journal of Digital Earth, 9(1), 106–111. doi:10.1080/17538947.2015.1111952
  • Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R.,Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D.,Ayers, D., Bala, B., Dwyer, J. … Wang, L.-W. (2017). The Australian Geoscience Data Cube—Foundations and lessons learned. Remote Sensing of Environment, 202, 276–292. doi:10.1016/j.rse.2017.03.015
  • Lewis, A., Lacey, J., Mecklenburg, S., Ross, J., Siqueira, A., Killough, B., Szantoi, Z., Tadono, T., Rosenavist, A.,Goryl, P., Miranda, N., & Hosford, S. (2018). CEOS Analysis Ready Data for Land (CARD4L) Overview. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 7407–7410). doi:10.1109/IGARSS.2018.8519255
  • Liang, J., Gong, J., & Li, W. (2018). Applications and impacts of Google Earth: A decadal review (2006–2016). ISPRS Journal of Photogrammetry and Remote Sensing, 146, 91–107. doi:10.1016/j.isprsjprs.2018.08.019
  • Liu, Z., Foresman, T., van Genderen, J., & Wang, L. (2020). Understanding digital earth. In H. Guo, M. F. Goodchild, & A. Annoni (Eds.), Manual of digital earth (pp. 1–21). Springer Singapore. doi:10.1007/978-981-32-9915-3_1
  • Loveland, T. R., & Dwyer, J. L. (2012). Landsat: Building a strong future. Remote Sensing of Environment, 122, 22–29. doi:10.1016/j.rse.2011.09.022
  • Mahecha, M. D., Gans, F., Brandt, G., Christiansen, R., Cornell, S. E., Fomferra, N. … Reichstein, M. (2020). Earth system data cubes unravel global multivariate dynamics. Earth System Dynamics, 11(1), 201–234. doi:10.5194/esd-11-201-2020
  • Maier, D., & Vance, B. (1993). A call to order. In Proceedings of the Twelfth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems - PODS ’93 (pp. 1–16). doi:10.1145/153850.153851
  • Mazzetti, P., Nativi, S., Santoro, M., Giuliani, G., Rodila, D., Folino, A., Caruso, S., Aracri, G., & Lehmann, A. (2022). Knowledge formalization for Earth Science informed decision-making: The GEOEssential Knowledge Base. Environmental Science & Policy, 131, 93–104. doi:10.1016/j.envsci.2021.12.023
  • Mei-Po, K. (2016). Algorithmic geographies: big data, algorithmic uncertainty, and the production of geographic knowledge. E, Annals of the American Association of Geographers, 106(2), 274–282. doi:10.1080/00045608.2015.1117937
  • Mfundisi, K., Mubea, K., Yuan, F., Burton, C., & Boamah, E. (2022). Analysing effects of drought on inundation extent and vegetation cover dynamics in the Okavango Delta. In AGU 2021 Fall Meeting. New Orleans: LA and virtual. doi:10.1002/essoar.10510000.2
  • Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174, 341–352. doi:10.1016/j.rse.2015.11.003
  • Nativi, S., Mazzetti, P., & Craglia, M. (2017). A view-based model of data-cube to support big earth data systems interoperability. Big Earth Data, 1(1–2), 75–99. doi:10.1080/20964471.2017.1404232
  • Nüst, D., Granell, C., Hofer, B., Konkol, M., Ostermann, F. O., Sileryte, R., & Cerutti, V. (2018). Reproducible research and GIScience: An evaluation using AGILE conference papers. PeerJ, 6, e5072. doi:10.7717/peerj.5072
  • Poussin, C., Guigoz, Y., Palazzi, E., Terzago, S., Chatenoux, B., & Giuliani, G. (2019). Snow cover evolution in the Gran Paradiso National Park, Italian Alps, Using the earth observation data cube. Data, 4(4), 138. doi:10.3390/data4040138
  • Poussin, C., Massot, A., Ginzler, C., Weber, D., Chatenoux, B., Lacroix, P., Piller, T., Nguyen, L., & Giuliani, G. (2021). Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs. Big Earth Data, 5(4), 445–475. doi:10.1080/20964471.2021.1974681
  • Purss, M. B. J., Lewis, A., Oliver, S., Ip, A., Sixsmith, J., Evans, B., Edberg, R., Frankish, G., Hurst, L., & Chan, T. (2015). Unlocking the Australian Landsat Archive – from dark data to High Performance Data infrastructures. GeoResj, 6, 135–140. doi:10.1016/j.grj.2015.02.010
  • Schramm, M., Pebesma, E., Milenković, M., Foresta, L., Dries, J., Jacob, A., Wagner, W., Mohr, M., Neteler, M., Kadunc, M.,Miksa, T., Kempeneers, P., Verbesselt, J., Gößwein, B., Navacchi, C., Lippens, S., & Reiche, J. (2021). The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities. Remote Sensing, 13(6), 1125. doi:10.3390/rs13061125
  • Scoones, I., Hall, R., Borras, S. M., White, B., & Wolford, W. (2013). The politics of evidence: Methodologies for understanding the global land rush. The Journal of Peasant Studies, 40(3), 469–483. doi:10.1080/03066150.2013.801341
  • Simoes, R., Camara, G., Queiroz, G., Souza, F., Andrade, P. R., Santos, L., Carvalho, A., & Ferreira, K. (2021). Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sensing, 13(13), 2428. doi:10.3390/rs13132428
  • Stonebraker, M., Brown, P., Poliakov, A., & Raman, S. (2011). The architecture of SciDB. In J. B. Cushing, J. French, & S. Bowers (Eds.), Scientific and statistical database management (Vol. 6809, pp. 1–16). Berlin Heidelberg: Springer. doi:10.1007/978-3-642-22351-8_1
  • Storch, T., Reck, C., Holzwarth, S., Wiegers, B., Mandery, N., Raape, U., Strobl, C., Volkmann, R., Böttcher, M., Hirner, A., Senft, J., Plesia, N., Kukuk, T., Meissl, S., Felske, J.-R., Heege, T.,Keuck, V., Schmidt, M., & Staudenrausch, H. (2019). Insights into CODE-DE Germany’s Copernicus data and exploitation platform. Big Earth Data, 3(4), 338–361. doi:10.1080/20964471.2019.1692297
  • Strobl, P., Baumann, P., Lewis, A., Szantoi, Z., Killough, B., Purss, M., Craglia, M., Nativi, S., Held, A., & Dhu, T. (2017). The six faces of the data cube. Publications Office of the European Union. doi:10.2760/383579
  • Sudmanns, M., Tiede, D., Lang, S., Bergstedt, H., Trost, G., Augustin, H., Baraldi, A., & Blaschke, T. (2020). Big Earth data: Disruptive changes in Earth observation data management and analysis? International Journal of Digital Earth, 13(7), 832–850. doi:10.1080/17538947.2019.1585976
  • Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021a). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. doi:10.3390/rs13234807
  • Sudmanns, M., Augustin, H., van der Meer, L., Werner, C., Baraldi, A., & Tiede, D. (2021b). One GUI to rule them all: Accessing multiple semantic EO data cubes in one graphical user interface. Gi_forum, 1, 53–59. doi:10.1553/giscience2021_01_s53
  • Tan, D. T., Siri, J. G., Gong, Y., Ong, B., Lim, S. C., MacGillivray, B. H., & Marsden, T. (2019). Systems approaches for localising the SDGs: Co-production of place-based case studies. Globalization and Health, 15(1), 85. doi:10.1186/s12992-019-0527-1
  • Thornton, J. M., Palazzi, E., Pepin, N. C., Cristofanelli, P., Essery, R., Kotlarski, S., Giuliani, G., Guigoz, Y., Kulonen, A., Pritchard, D., Li, X., Fowler, H. J., Randin, C. F., Shahgedanova, M.,Steinbacher, M., Zebisch, M., & Adler, C. (2021). Toward a definition of Essential Mountain Climate Variables. One Earth, 4(6), 805–827. doi:10.1016/j.oneear.2021.05.005
  • Tondapu, G., Markert, K., Lindquist, E., Wiell, D., Díaz, A.-S.-P., Johnson, G., Ashmall, W., Chishtie, F., Ate, P., Tenneson, K., Patterson, M. S., Ricci, S., Fontanarosa, R., & Saah, D. (2018). A SERVIR FAO Open Source Partnership: Co-development of Open Source Web Technologies using Earth Observation for Land Cover Mapping. In AGU Fall Meeting Abstracts. AGU Fall Meeting, Washington, DC, USA.
  • Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C., Thiel, C., Rossi, C., Syriou, A., & Giuliani, G. (2019). Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube. Data, 4(3), 93. doi:10.3390/data4030093
  • Wagemann, J., Clements, O., Marco Figuera, R., Rossi, A. P., & Mantovani, S. (2018). Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data. International Journal of Digital Earth, 11(1), 7–25. doi:10.1080/17538947.2017.1351583
  • Wagemann, J., Siemen, S., Seeger, B., & Bendix, J. (2021). A user perspective on future cloud-based services for Big Earth data. International Journal of Digital Earth, 14(12), 1758–1774. doi:10.1080/17538947.2021.1982031
  • Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M.,Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. doi:10.1038/sdata.2016.18
  • Xu, C., Du, X., Jian, H., Dong, Y., Qin, W., Mu, H., Yan, Z., Zhu, J., & Fan, X. (2022). Analyzing large-scale Data Cubes with user-defined algorithms: A cloud-native approach. International Journal of Applied Earth Observation and Geoinformation, 109, 102784. doi:10.1016/j.jag.2022.102784
  • Yuan, F., Lewis, A., Leith, A., Dhar, T., & Gavin, D. (2021). Analysis Ready Data for Africa. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 1789–1791). doi:10.1109/IGARSS47720.2021.9554019
  • ZAMG. (2021). Freier Zugang zu hochwertigen Wetterdaten. Retrieved from https://www.zamg.ac.at/cms/de/aktuell/news/freier-zugang-zu-hochwertigen-wetterdaten
  • Zawacki-Richter, O., Conrad, D., Bozkurt, A., Aydin, C. H., Bedenlier, S., Jung, I., Stöter, J., Veletsianos, G., Blaschke, L. M., Bond, M., Broens, A., Bruhn, E., Dolch, C., Kalz, M., Kondakci, Y., Marin, V., Mayrberger, K., Müskens, W., Naidu, S., … Xiao, J. (2020). Elements of Open Education: An Invitation to Future Research. The International Review of Research in Open and Distributed Learning, 21(3). doi:10.19173/irrodl.v21i3.4659

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